Logic Specification for ACIP Recommendations v4.4 Page 1 of 151

CLINICAL DECISION SUPPORT FOR IMMUNIZATION (CDSI):

LOGIC SPECIFICATION FOR ACIP RECOMMENDATIONS 

National Center for Immunization and Respiratory Disease (NCIRD)

Informatics and Data Analytics Branch (IDAB)

Version 4.4

January 11, 2024
Logic Specification for ACIP Recommendations v4.4 Page 2 of 151

TABLE OF CONTENTS

1 Executive Summary.................................................................................................................................... 7

1.1 Background and Goals ........................................................................................................................ 7

1.2 Approach............................................................................................................................................. 8

1.3 Scope.................................................................................................................................................. 8

1.4 Products.............................................................................................................................................. 9

2 Resource Overview .................................................................................................................................. 12

2.1 Chapter Overview.............................................................................................................................. 12

2.2 Resource Design Principles............................................................................................................... 13

2.3 Design and Documentation Strategy ................................................................................................. 13

2.4 Supporting Data ................................................................................................................................ 14

2.5 Logic Definition - Purpose ................................................................................................................. 17

2.6 Logic Definition – Evaluation ............................................................................................................. 17

2.7 Logic Definition – Forecasting ........................................................................................................... 18

2.8 Logic Definition – Select Patient Series ............................................................................................. 18

2.9 Logic Definition – Identify and Evaluate Vaccine Group..................................................................... 19

2.10 Processing Model.............................................................................................................................. 20

2.11 Decision Table Overview................................................................................................................... 20

3 Logic Specification Concepts.................................................................................................................... 23

3.1 Target Dose ...................................................................................................................................... 23

3.2 Statuses ............................................................................................................................................ 24

3.3 Selecting Supporting Data................................................................................................................. 25

3.4 Date Calculations .............................................................................................................................. 27

4 Processing Model..................................................................................................................................... 30

4.1 Gather Necessary Data..................................................................................................................... 31

4.2 Organize Immunization History.......................................................................................................... 32

4.3 Create Relevant Patient Series ......................................................................................................... 34

4.4 Evaluate and Forecast all Relevant Patient Series ............................................................................ 35

4.5 Select Best Patient Series ................................................................................................................. 39

4.6 Identify and Evaluate Vaccine Group................................................................................................. 39

5 Create Relevant Patient Series................................................................................................................. 41

5.1 Select Relevant Patient Series .......................................................................................................... 41

6 Evaluate Vaccine Dose Administered....................................................................................................... 45

6.1 Evaluate Dose Administered Condition.............................................................................................. 46

6.2 Evaluate Conditional Skip.................................................................................................................. 47

6.3 Evaluate for Inadvertent Vaccine....................................................................................................... 52

6.4 Evaluate Age..................................................................................................................................... 52

6.5 Evaluate Preferable Interval .............................................................................................................. 55
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6.6 Evaluate Allowable Interval................................................................................................................ 59

6.7 Evaluate Vaccine Conflict.................................................................................................................. 62

6.8 Evaluate for Preferable Vaccine ........................................................................................................ 64

6.9 Evaluate for Allowable Vaccine ......................................................................................................... 67

6.10 Satisfy Target Dose........................................................................................................................... 70

7 Forecast Dates and Reasons ................................................................................................................... 72

7.1 Evaluate Conditional Skip.................................................................................................................. 73

7.2 Determine Evidence of Immunity....................................................................................................... 74

7.3 Determine Contraindications ............................................................................................................. 75

7.4 Determine Forecast Need ................................................................................................................. 79

7.5 Generate Forecast Dates and Recommended Vaccines ................................................................... 81

7.6 Validate Recommendation................................................................................................................. 84

8 Select Best Patient Series ........................................................................................................................ 85

8.1 Pre-filter Patient Series...................................................................................................................... 88

8.2 Identify One Prioritized Patient Series ............................................................................................... 88

8.3 Classify Scorable Patient Series........................................................................................................ 89

8.4 Complete Patient Series.................................................................................................................... 90

8.5 In-process Patient Series .................................................................................................................. 91

8.6 No Valid Doses.................................................................................................................................. 92

8.7 Select Prioritized Patient Series ........................................................................................................ 92

8.8 Determine Best Patient Series........................................................................................................... 93

9 Identify and Evaluate Vaccine Group........................................................................................................ 94

9.1 Apply General Vaccine Group Rules ................................................................................................. 95

9.2 Single Antigen Vaccine Group........................................................................................................... 96

9.3 Multiple Antigen Vaccine Group ........................................................................................................ 96

Appendix A: Domain Model and Glossary ....................................................................................................... 98

Domain Model (Concept Model, Vocabulary) Overview ............................................................................... 98

Domain Model Diagrams............................................................................................................................ 100

Appendix B: Acronyms and Abbreviations ..................................................................................................... 119

Appendix C: Retired Items............................................................................................................................. 121

Appendix D: Acknowledgements ................................................................................................................... 128

Appendix E: References................................................................................................................................ 142

Appendix F: Supplemental Material ............................................................................................................... 145

Appendix G: Document Management............................................................................................................ 146
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LIST OF FIGURES AND TABLES

Table 1-1 Vaccine Groups in Scope ....................................................................................................................................... 8

Figure 1-1 CDSi Resources ..................................................................................................................................................10

Table 2-1 List of Chapters.....................................................................................................................................................12

Table 2-2 Descriptions of Components.................................................................................................................................13

Table 2-3 Supporting Data Suggested Audience..................................................................................................................14

Figure 2-1 Supporting Data...................................................................................................................................................16

Table 2-4 Evaluation Suggested Audience...........................................................................................................................17

Table 2-5 Forecasting Suggested Audience.........................................................................................................................18

Table 2-6 Select Patient Series Suggested Audience ..........................................................................................................19

Table 2-7 Identify and Evaluate Vaccine Group Suggested Audience .................................................................................19

Table 2-8 Processing Model Suggested Audience...............................................................................................................20

Table 2-9 Should I Get my Car Washed? .............................................................................................................................21

Table 2-10 Explanations of Outcomes..................................................................................................................................21

Table 2-11 What Exercise Should I do Today? ....................................................................................................................21

Figure 3-1 How a Vaccine Dose Administered Satisfies a Target Dose ..............................................................................24

Table 3-1 Evaluation Statuses ..............................................................................................................................................24

Table 3-2 Target Dose Statuses ...........................................................................................................................................25

Table 3-3 Patient Series Statuses ........................................................................................................................................25

Figure 3-2 Selecting Supporting Data...................................................................................................................................26

Table 3-4 Relevant supporting data Attributes......................................................................................................................26

Table 3-5 Select Relevant supporting data Logical Component element.............................................................................26

Figure 3-3 Conditional Skip Context .....................................................................................................................................27

Table 3-6 General Date Rules ..............................................................................................................................................27

Table 3-7 Logical Component Date Rules ............................................................................................................................28

Table 4-1 Logic Specification Processing Steps...................................................................................................................30

Figure 4-1 Logic Specification Processing Model .................................................................................................................30

Figure 4-2 Refinement of Patient Series...............................................................................................................................31

Table 4-2 Prior to Organize Immunization History Example.................................................................................................32

Table 4-3 After Organize Immunization History Example.....................................................................................................32

Figure 4-3 Organize Immunization History Process Model ..................................................................................................34

Figure 4-4 Create Relevant Patient Series Process Model ..................................................................................................35

Figure 4-5 Evaluate and Forecast Process Model................................................................................................................36

Figure 4-6 Evaluate Immunization History Process Model ...................................................................................................37

Figure 4-7 Select Best Series Process Model ......................................................................................................................39

Figure 4-8 Identify and Evaluate Vaccine Group Process Model .........................................................................................40

Table 5-1 Create Relevant Patient Series Process Steps ....................................................................................................41

Figure 5-1 Create Relevant Series Process Model...............................................................................................................41

Figure 5-2 Select Relevant Patient Series Process Model ...................................................................................................42

Table 5-2 Select Relevant Patient Series Attributes .............................................................................................................42

Table 5-3 Select Relevant Patient Business Rules...............................................................................................................42

Figure 5-3 Assess Indications Process Model ......................................................................................................................43

Table 5-4 Does the Indication Apply to the Patient?.............................................................................................................43

Table 5-5 Is An Antigen Series a Relevant Patient series for a Patient? .............................................................................43

Table 6-1 Evaluation Process Steps .....................................................................................................................................45

Figure 6-1 Evaluation Process Model ...................................................................................................................................46

Figure 6-2 Vaccine Dose Administered Condition Process Model .......................................................................................47

Table 6-2 Dose Administered Condition Attributes ...............................................................................................................47

Table 6-3 Can the Vaccine Dose Administered be Evaluated?............................................................................................47

Figure 6-3 Conditional Skip Process Model..........................................................................................................................49

Table 6-4 Conditional Skip Attributes....................................................................................................................................49
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Table 6-5 Conditional Skip Business Rules ..........................................................................................................................50

Table 6-6 Conditional Type of Age – Is the Condition Met? .................................................................................................50

Table 6-7 Conditional Type of Completed Series – Is the Condition Met?...........................................................................50

Table 6-8 Conditional Type of Interval – Is the Condition Met?............................................................................................51

Table 6-9 Conditional Type of Vaccine Count by Age or Date – Is the Condition Met?.......................................................51

Table 6-10 Is the Conditional Skip Set Met?.........................................................................................................................51

Figure 6-4 Evaluate for an Inadvertent Vaccine Process Model...........................................................................................52

Table 6-12 Inadvertent Vaccine Attributes............................................................................................................................52

Table 6-13 Was the vaccine dose administered an Inadvertent administration for the target dose?...................................52

Figure 6-5 Evaluate Age Timeline.........................................................................................................................................53

Figure 6-6 Evaluate Age Process Model ..............................................................................................................................53

Table 6-14 Age Attributes .....................................................................................................................................................53

Table 6-15 Was the Vaccine Dose Administered at a Valid Age?........................................................................................53

Table 6-16 Evaluate Age Business Rules.............................................................................................................................54

Figure 6-7 Evaluate Interval ‘From Immediate Previous Dose Administered Flag’ Timeline................................................55

Figure 6-8 Evaluate Interval 'From Target Dose Number in Series' Timeline.......................................................................56

Figure 6-9 Evaluate Interval 'From Most Recent Vaccine Type' Timeline ............................................................................56

Figure 6-10 Evaluate Interval 'From Relevant Observation Code' Timeline.........................................................................57

Figure 6-11 Evaluate Preferable Interval Process Model .....................................................................................................57

Table 6-17 Preferable Interval Attributes ..............................................................................................................................58

Table 6-18 Did the Vaccine Dose Administered Satisfy the preferable Interval for the target dose?...................................58

Table 6-19 Evaluate Preferable Interval Business Rules .....................................................................................................59

Figure 6-12 Evaluate Allowable Interval 'From Immediate Previous Dose Administered Flag' Timeline .............................60

Figure 6-13 Evaluate Allowable Interval 'From Target Dose Number in Series' Timeline....................................................60

Figure 6-14 Evaluate Allowable Interval Process Model ......................................................................................................61

Table 6-20 Allowable Interval Attributes ...............................................................................................................................61

Table 6-21 Did the Vaccine Dose Administered Satisfy the Allowable Interval for the target dose? ...................................61

Table 6-22 Evaluate Allowable Interval Business Rules.......................................................................................................62

Figure 6-15 Evaluate Vaccine Conflict Timeline ...................................................................................................................62

Figure 6-16 Evaluate vaccine Conflict Process Model .........................................................................................................63

Table 6-23 Vaccine Conflict Attributes..................................................................................................................................63

Table 6-24 Vaccine Conflict Business Rules ........................................................................................................................63

Figure 6-17 Patient Received a Preferable Vaccine.............................................................................................................65

Figure 6-18 Patient Did Not Receive a Preferable Vaccine..................................................................................................65

Figure 6-19 Evaluate for a Preferable Vaccine Process Model............................................................................................66

Table 6-25 Preferable Vaccine Administered Attributes .......................................................................................................66

Table 6-26 Was the vaccine dose administered a preferable Vaccine for the target dose? ................................................66

Table 6-27 Preferable Vaccine Business Rules....................................................................................................................67

Figure 6-20 Patient Received an Allowable Vaccine ............................................................................................................68

Figure 6-21 Patient Did Not Receive an Allowable Vaccine .................................................................................................68

Figure 6-22 Evaluate for an Allowable Vaccine Process Model ...........................................................................................69

Table 6-28 Allowable Vaccine Attributes ..............................................................................................................................69

Table 6-29 Was the vaccine dose admninstered an Allowable Vaccine for the target dose?..............................................69

Table 6-30 Allowable Vaccine Business Rules.....................................................................................................................70

Figure 6-23 Satisfy Target Dose Process Model ..................................................................................................................70

Table 6-31 Was the Target Dose Satisfied? .........................................................................................................................70

Table 7-1 Forecast Dates and Reasons Process Steps.......................................................................................................72

Figure 7-1 Forecast Dates and Reason Process Model.......................................................................................................73

Figure 7-2 Evidence of Immunity Process Model .................................................................................................................74

Table 7-2 Immunity Attributes ...............................................................................................................................................74

Table 7-3 Does the Patient have Evidence of Immunity?.....................................................................................................74

Figure 7-3 Contraindication Process Model..........................................................................................................................75
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Table 7-4 Determine Contraindication Attributes ..................................................................................................................75

Figure 7-4 Antigen Contraindication Process Model.............................................................................................................76

Table 7-5 Does the Antigen Contraindication Apply to the Patient?.....................................................................................76

Figure 7-5 Vaccine Contraindication Process Model............................................................................................................77

Table 7-6 Does the Vaccine Contraindication Apply to the Patient? ....................................................................................77

Table 7-7 Is the Relevant Patient Series a Contraindicated Patient Series? .......................................................................78

Figure 7-6 Determine Forecast Need Process Model ..........................................................................................................79

Table 7-9 Determine Forecast Need Attributes ....................................................................................................................79

Table 7-10 Should the Patient Receive Another Target Dose?............................................................................................80

Table 7-11 Determine Forecast Need Business Rules.........................................................................................................81

Figure 7-7 Forecast Dates Timeline......................................................................................................................................82

Figure 7-8 Generate Forecast Dates and Recommended Vaccine Process Model.............................................................82

Table 7-12 Generate Forecast Date and Recommended Vaccine Attributes ......................................................................82

Table 7-13 Generate Forecast Date and Recommended Vaccine Business Rules.............................................................83

Figure 7-9 Validate Recommended Dose Process Model....................................................................................................84

Table 8-1 Select Best Patient Series Process Steps ............................................................................................................85

Figure 8-1 Select Best Patient Series Process Model ..........................................................................................................86

Figure 8-2 Select Prioritized Patient Series Process Model .................................................................................................88

Table 8-2 Pre-Filter Patient Series Business Rules..............................................................................................................88

Table 8-3 Is There a single Prioritized Patient Series in a series group?.............................................................................88

Table 8-4 Identify one prioritized patient series business rules ............................................................................................89

Table 8-5 Which Scorable Patient Series Should be Scored? .............................................................................................89

Table 8-6 Classify scorable patient series business rules ....................................................................................................90

Table 8-7 How Many Points are Awarded to a Scorable Patient Series that is a Complete Patient Series?.......................90

Table 8-8 Complete Patient Series Business Rules .............................................................................................................91

Table 8-9 How Many Points are Awarded to Scorable Patient Series that is an In-Process Patient Series?......................91

Table 8-10 In-Process Patient Series Business Rules .........................................................................................................91

Table 8-11 How Many Points are Awarded to a Scorable Patient Series that has No Valid Doses?...................................92

Table 8-12 No Valid Doses Business Rules .........................................................................................................................92

Table 8-13 Select Prioritized Patient Series Business Rules ...............................................................................................93

Table 8-14 Is the prioritized patient series the best patient series for the series group? .....................................................93

Table 9-1 Identify and Evaluate Vaccine Group Process Steps ...........................................................................................94

Figure 9-1 Identify and Evaluate Vaccine Group Process Model .........................................................................................94

Table 9-2 GeneraL Vaccine Group Business Rules .............................................................................................................95

Table 9-3 Single Antigen Vaccine Group Business Rules....................................................................................................96

Table 9-4 What is the Vaccine Group Status of a Vaccine Group forecast for a multiple antigent vaccine group?.............96

Table 9-5 Multiple Antigen Vaccine Group Business Rules .................................................................................................97

Figure A-1 Neighborhood......................................................................................................................................................99

Table A-1 Domain Model Glossary .......................................................................................................................................99

Figure A-2 CDSi Domain Diagram: Patient Neighborhood.................................................................................................100

Figure A-3 CDSi Domain Diagram: Schedule Neighborhood .............................................................................................102

Figure A-4 CDSi Domain Diagram: Antigen Series Neighborhood.....................................................................................104

Figure A-5 CDSi Domain Diagram: Evaluation Neighborhood ...........................................................................................106

Figure A-6 CDSi Domain Diagram: Forecast Neighborhood ..............................................................................................107

Table A-7 Glossary .............................................................................................................................................................108

Table B-1 Acronyms and Abbreviations..............................................................................................................................119

Table C-1 Retired Items ......................................................................................................................................................121
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1 EXECUTIVE SUMMARY

1.1 BACKGROUND AND GOALS

In 2022, approximately 98% of U.S. children under the age of six participated1in an Immunization Information 

System (IIS), an increase from 82% in 2010.

2 Adolescent participation in 2022 was 82%, up from 60% in 2010. 

Adult participation in 2022 increased to 94% up from 22% in 2010. Given this widespread IIS participation, it is 

important that each patient’s immunization record is consistent and up to date within an IIS.

Health Information Systems (HIS) – which can include Health Information Exchanges (HIEs), IIS, Electronic 

Health Records (EHRs), and others – provide healthcare providers with immunization evaluation and forecasting 

tools designed to automatically determine the recommended immunizations needed when a patient presents for 

vaccination. These recommendations are developed by the Advisory Committee on Immunization Practices 

(ACIP). ACIP is a federal advisory committee responsible for providing expert external advice and guidance to 

the Director of the Centers for Disease Control and Prevention (CDC) and the Secretary of the U.S. Department 

of Health and Human Services (DHHS) on use of vaccines and related agents for control of vaccine-preventable 

disease in the United States. Recommendations include but are not limited to age for vaccine dose administered, 

number of doses, dosing interval, risk factors, precautions, and contraindications. 

After ACIP recommendations are published, technical and clinical subject matter experts (SMEs) work to 

interpret and integrate them into their evaluation and forecasting engines. An example of an evaluation and 

forecasting engine is a tool an IIS might use to alert a physician that a presenting child is overdue for a Measles, 

Mumps, and Rubella (MMR) vaccination. ACIP schedule changes are currently communicated only through 

clinical language, in publications like the Morbidity and Mortality Weekly Report (MMWR) and the Epidemiology 

and Prevention of Vaccine-Preventable Diseases ("The Pink Book"). The translation of that clinical language into 

technical logic that is processed within evaluation and forecasting engines is a time-consuming and complex 

process that happens mostly independently within the different HIS. Due to the challenge of interpreting clinically 

written ACIP recommendations, clinical decision support (CDS) engine outputs often vary and do not always 

match the expectations of clinical SMEs. 

To harmonize the outcomes of existing HIS CDS tools, the Informatics and Data Analytics Branch (IDAB) at the 

CDC funded the Clinical Decision Support for Immunization (CDSi) Project to develop clinical decision aids3for 

each vaccine preventable disease in accordance with ACIP recommendations to: 

• Make it easier to develop and maintain immunization evaluation and forecasting products

• Ensure a patient’s immunization status is current, accurate, consistent, and readily available

• Increase the accuracy and consistency of immunization evaluation and forecasting

• Improve the timeliness of accommodating new and changed ACIP recommendations

The ultimate goal of the project is to ensure that patients receive proper immunizations, i.e., “the right 

immunization at the right time.”

1 Participation was defined as having at least two recorded vaccinations in an Immunization Information System (IIS).

2 All data derived from the 2018 Immunization Information Systems Annual Report (IISAR). For further information, see: 2018 

Immunization Information Systems Annual Report (IISAR).

3 Aids refer to manual support mechanisms and in no way imply that an automated system is being developed or provided. These aids 

can, however, be used to refine existing or develop new automated systems.
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1.2 APPROACH

As part of this project, an expert panel was formed in April 2011, consisting of SMEs and expert reviewers from:

• CDC Public Health Informatics and Technology Program Office (PHITPO)

• American Immunization Registry Association (AIRA)

• Indian Health Service (IHS)

• EHR vendors

• IIS programs and vendors

• Academic institutions

Please refer to Appendix D for more information regarding the expert panelists.

1.3 SCOPE

The vaccine groups in scope for the current phase of the project are those routinely recommended by ACIP for 

healthy individuals from birth through age 65+ years as well as those recommended because of underlying 

conditions.

TABLE 1-1 VACCINE GROUPS IN SCOPE

Vaccine Groups

Cholera Influenza Rabies

COVID-19 Japanese Encephalitis Respiratory Syncytial Virus (RSV)

Dengue Measles, Mumps, Rubella (MMR) Rotavirus

Diphtheria, Tetanus, and Pertussis 

(DTaP, Tdap, Td)

Meningococcal ACWY Tick-borne Encephalitis (TBE)

Ebola Meningococcal B Typhoid

Haemophilus influenzae type B (Hib) Orthopoxvirus Varicella

Hepatitis A Pneumococcal Yellow Fever

Hepatitis B Polio Zoster

Human Papillomavirus (HPV)

Additional items in scope include:

• Current ACIP recommendations with clarifications

• Compromised/sub-potent/expired doses

• Vaccine recalls

• Wrong vaccine formulations

• Underlying conditions related to contraindications

• Immunities

• The 4-day grace period

• Catch-up schedule

While not addressed specifically, the CDSi resources were developed to accommodate non-ACIP published 

rules (i.e., state law variations, local school schedules, rules published by other organizations, rules published 
Logic Specification for ACIP Recommendations v4.4 Page 9 of 151

in other countries). Supporting Data can be adjusted by implementers to cover these variations from the ACIP 

recommendations.

Items currently out of scope but candidates for future project phases include the following:

• Outbreak recommendations

• Immune Globulin (IG)

• Route and body site of administration

• Non-FDA approved vaccines (i.e., those used in clinical trials)

• Precautions

• Shared Clinical Decision Making

1.4 PRODUCTS

1.4.1 Resources

The CDSi team has developed the resources which captures ACIP recommendations in an unambiguous 

manner and improves both the uniform representation of vaccine decision guidelines as well as the ability to 

automate vaccine evaluation and forecasting. The resources provide a single, authoritative, implementationneutral foundation for development and maintenance of clinical decision support engines. It increases the 

accuracy and consistency of forecasting and evaluation across the HIS community and improves the timeliness 

of HIS accommodation of new and changed rules.

The objectives of the CDSi resources are to:

• Create a standardized CDS logic representation for ACIP recommendations that allows for broad 

implementation and effective usage across IIS and other HIS

• Document the logic for applying ACIP business rules in CDS engines in order to improve the clarity, 

consistency, and computability of on-going childhood, adolescent, and adult immunization evaluation and 

forecasting

• Provide testing scenarios to ensure the accurate implementation of the Supporting Data and Logic 

Specification by CDS engines

• Educate audiences regarding the content and use of the CDSi resources
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FIGURE 1-1 CDSI RESOURCES

The CDSi resources were developed to be as technology-neutral as possible to support those currently with or 

without complete evaluation and forecasting engines as they:

• Refine, extend, or develop their implementation

• Clarify their understanding of immunization rules

• Troubleshoot and verify correct implementation of immunization rules

1.4.2 Logic Specification

The CDSi team developed a Logic Specification that describes the functionality required to evaluate and forecast 

based on the Supporting Data as applied to a patient’s immunization history and patient observations. The Logic 

Specification uses defined vocabulary and domain models to build business rules, decision tables and a 

processing model which can be implemented by a CDS engine.

The intended audience of the Logic Specification includes business and technical implementers of immunization 

CDS engines. These implementers may support any system with an immunization evaluation and forecasting 

engine, including but not limited to an IIS. 

1.4.3 Supporting Data
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The CDSi team developed Supporting Data to describe, by antigen, various factors and their accompanying sets 

of values to be considered when implementing ACIP recommendations. Supporting Data can be thought of as a 

set of configuration files used as input to a CDS engine. Supporting Data is published both as Excel spreadsheets 

and XML.

The intended audience of the Supporting Data includes business and technical implementers of immunization 

CDS engines. These implementers may support any system with an immunization evaluation and forecasting 

engine, including but not limited to an IIS. 

1.4.4 Test Cases 

The CDSi team developed a representative set of test cases for use to compare and improve CDS engine actual 

results with ACIP recommendations and clarifications. 

The intended audience of the Test Cases includes business implementers of immunization CDS engines who 

are responsible for ensuring the accuracy of the CDS engine. These implementers may support any system with 

an immunization evaluation and forecasting engine, including but not limited to an IIS. 

1.4.5 Training Materials

The CDSi team developed training materials to educate audiences interested in knowing more about the CDSi 

project and how to implement the accompanying CDSi resources. These materials are also intended to promote 

the use of CDSi by a variety of HIS.

The intended audience of the Training Materials includes anyone interested in knowing more about CDSi.
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2 RESOURCE OVERVIEW

2.1 CHAPTER OVERVIEW

The Logic Specification provides the rules to determine if the immunizations received meet the requirements 

stated by the ACIP. A description of each chapter is presented below:

TABLE 2-1 LIST OF CHAPTERS

Chapter Title Description

Chapter 1 Executive Summary Introduces the context, goals, and primary deliverable of the CDSi project.

Chapter 2 Resource Overview Provides a high-level overview of the key components of the CDSi resources. The 

purpose and function are described for each component. In addition, the 

instruments used to document each component are also introduced.

Chapter 3 Logic Specification 

Concepts

Provides an explanation of target dose, the meanings of statuses used in evaluation 

and forecasting, an introduction to Supporting Data, and the business rules for 

calculating dates

Chapter 4 Processing Model Provides the major logical steps involved in the immunization evaluation and 

forecasting engine of the CDS process.

Chapter 5 Create Relevant 

Patient Series

Provides the rules for selecting series which are relevant for the patient.

Chapter 6 Evaluate Vaccine 

Dose Administered

Provides the rules for evaluating a vaccine dose administered. The approach is 

documented using a process model, decision tables, and business rules.

Chapter 7 Forecast Dates and 

Reasons

Provides the rules for determining forecast dates. The approach is documented 

using a process model, decision tables, and business rules.

Chapter 8 Select Patient 

Series

Provides the rules for selecting the patient series which best fits based on various 

important factors. The approach is documented using a process model, decision 

tables, and business rules.

Chapter 9 Identify & Evaluate 

Vaccine Group

Provides the rules for combining selected patient series from an antigen-based 

forecast into a vaccine group-based forecast. The approach is documented using a 

process model, decision tables, and business rules.

Appendix A Domain Model and 

Glossary

Provides a domain model that includes diagrams and vocabulary that is pertinent to 

the CDSi resources.

Appendix B Acronyms and 

Abbreviations

Provides the meanings of acronyms and abbreviations used in the document.

Appendix C Retired Items Provides a list of concepts that have been previously included in the CDSi 

resources but are no longer used.

Appendix D Acknowledgements Provides biographies of subject matter experts who served as volunteer panelists 

for the CDSi project.

Appendix E References Provides citations of various reference materials that were used to document the 

business rules and Supporting Data tables.

Appendix F Supplemental 

Material

Provides supplemental material to aid with concepts found in CDSi resources.

Appendix G Document 

Management

Provides a table to track key changes and versions of the document.
Logic Specification for ACIP Recommendations v4.4 Page 13 of 151

2.2 RESOURCE DESIGN PRINCIPLES

The following guiding principles (GP) were central to the development and the design of the CDSi resources. 

Ultimately, the CDSi resources should:

GP1. Reduce complexity of understanding and implementing ACIP recommendations

GP2. Ensure consistency in interpretation of ACIP recommendations

GP3. Enhance maintainability in response to newly published ACIP recommendations 

• Improved timeliness (i.e., turnaround time)

• Reduction in rework

• Minimal impact of changes

GP4. Inform a variety of implementations

2.3 DESIGN AND DOCUMENTATION STRATEGY

Giving the complexity of implementing ACIP recommendations and considering the guiding principles, the design 

strategy included two key elements:

• Focusing on three components by setting apart the configuration data, the business rules, and the 

processing model that pulls the business rules together

• Emphasizing “universal” functionality applicable across HIS instead of implementation-specific 

engineering requirements

In addition, a variety of mechanisms were chosen to document the specification in order to provide a concise, 

unambiguous, and computable description of the functionality required. Thus, the design of CDSi resources is 

divided into three components. The graphic below lists each component, the description, and the documentation 

method.

TABLE 2-2 DESCRIPTIONS OF COMPONENTS

Component Description Documentation Method

Describes, by antigen, various factors and 

their accompanying sets of values to be 

considered when implementing ACIP 

recommendations

Chapter 3:

• Introduction to 

Supporting Data

• Rules to select proper 

supporting data

Describes the functionality required to 

evaluate and forecast based on a 

patient’s immunization history and the 

Supporting Data.

Logic definitions include:

• Create Relevant Series Logic

• Evaluation Logic

• Forecasting Logic

• Select Patient Series Logic

• Identify and Evaluate Vaccine Group 

Logic

Chapters 3, 5, 6, 7, 8 & 9:

• Thin process models

• Decision tables

• Business rules

Supporting Data

Logic Definition
Logic Specification for ACIP Recommendations v4.4 Page 14 of 151

Component Description Documentation Method

Describes the technical structure 

necessary to pull the details of the Logic 

Definition, Supporting Data, and patient 

related data together

Chapter 4:

• Activity diagrams

Together these components describe the functionality to evaluate and forecast based on ACIP recommendations 

using a patient’s immunization history.

2.4 SUPPORTING DATA

2.4.1 Purpose

The Supporting Data describes the attributes (e.g., minimum age, earliest 

recommended age, and preferable vaccine type) necessary and specific 

values (e.g., schedule-specific, antigen series-specific, and dose-specific) 

required to support evaluation and forecasting as described by the Logic 

Specification.

Simply put, Supporting Data is akin to configuration data which feeds the system and is able to be modified 

separately from the logic. The Supporting Data supplied by the CDSi project represents the ACIP 

recommendations and clarifications from Subject Matter Experts at the Health Education and Communication 

Branch (HECB).

2.4.2 What problem it helps solve

The Supporting Data was separated from the Logic Specification in order to reduce and ease the maintenance 

of the resources as clinical guidance evolves. The Supporting Data values change on a regular basis in 

conjunction with new and updated ACIP recommendations. The Logic Specification evolves much more slowly. 

When Supporting Data are ultimately implemented as some form of a data store (e.g., database), new and 

updated recommendations can be reflected through simple Supporting Data changes. In essence, Supporting 

Data can be thought of as configuration parameters and values.

TABLE 2-3 SUPPORTING DATA SUGGESTED AUDIENCE

Role Perspective

Business Analyst Understanding and documenting the specific values that describe the relevant 

information about antigens, series, doses, etc.

Technical Developer Implementing the data structures to support storage and access of the 

Supporting Data. Understanding the integration of the Supporting Data, Logic 

Specification, and processing model.

2.4.3 How and where it is documented

The format of the Supporting Data files is described below and the vocabulary in Appendix A provides definitions 

of the concepts and elements used within the CDSi resources. Additional understanding can be obtained by 

reviewing the actual Supporting Data. The current standard set of Supporting Data definitions with appropriate 

Processing Model

.......................................... Doses ......................... Antigen Series ........................... Schedule Supporting Data
Logic Specification for ACIP Recommendations v4.4 Page 15 of 151

values, based on the ACIP recommendations without modification for any local differences can be found at 

http://www.cdc.gov/vaccines/programs/iis/cdsi.html.

The CDSi project provides two Types of Supporting Data:

• Antigen Supporting Data which holds the discrete data describing the ACIP recommendations for each 

antigen listed in the scope section of Chapter 1. 

• Schedule Supporting Data which documents information used across all antigens or between antigens. 

Flavors of schedule Supporting Data are:

o A consolidated list of Coded Observations used to identify risk indications, underlying patient 

conditions and contraindications in the antigen Supporting Data files

o A CVX to Antigen map which links individual CVX codes to antigen Supporting Data 

o A list of Vaccine Groups

o A Vaccine Group to Antigen map to identify the antigens which make up a vaccine group

o A Live Virus Conflict table to identify situations where the timing of administration of a vaccine 

may be impacted

Each Type of Supporting Data is available in both XML format as well as in spreadsheet form. The former is 

intended to be machine processable while the latter will be more helpful for humans reviewing the Supporting 

Data. Should the content of the two formats conflict, the Excel format should be considered the source of truth.

The antigen Supporting Data spreadsheets are split into a number of different Sections organized as different 

tabs of the spreadsheets:

• The Antigen Series Overview tab provides high level data for the antigen.

• The Change History tab documents the evolution of the Supporting Data across multiple releases of the 

CDSi resources.

• The FAQ tab is home to common questions about the antigen Supporting Data.

• The Immunity tab contains discrete data regarding when a patient may have sufficient evidence of 

immunity to the disease.

• The Contraindication tab identifies discrete patient scenarios which indicate when a dose should not be 

administered to a patient.

• One or more Series tabs which document different paths to immunity drawn from the ACIP 

recommendations.

Note that the Antigen Series Overview, Change History and FAQ tabs are largely intended for human 

consumption and the data in these Sections are not represented in the cognate XML file for the antigen.

Each Section (excel tab) is further sub-divided into multiple Logical Components which group sets of related 

data Elements. The figure below shows the Logical Components (which are always in Column A of the tab with 

a dark orange background) outlined in purple. The Elements of the Logical Component extend across the 

spreadsheet (with a light orange background) and are outlined in pink the figure. For a given Logical Component, 

there will be zero or more Instances of data (with a yellow background). For example, in the figure below, the 

Preferable Vaccine Logical Component contains three different Instances (Preferable Vaccines), each outlined 

in a different shade of green.

Furthermore, the Series tabs include a single set of series-level Logical Components which apply to the Series 

in general and are outlined in red in the figure below. As well, each Series includes one or more sets of doselevel Logical Components outlined in shades of blue in the figure. While the set of Logical Components is 
Logic Specification for ACIP Recommendations v4.4 Page 16 of 151

identical in all doses, the Instance data may be different between doses within a series. For example, the 

Preferable Interval for the second dose in the Series may be different than the Preferable Interval for the third 

dose in the series.

FIGURE 2-1 SUPPORTING DATA
Logic Specification for ACIP Recommendations v4.4 Page 17 of 151

2.5 LOGIC DEFINITION - PURPOSE

The logic definition describes, in a technology-neutral fashion, the functional steps 

necessary to process the patient’s history using the Supporting Data.

The logic definition is composed of four separate, but related functions:

• Evaluation

• Forecasting

• Select Patient Series

• Identify and Evaluate Vaccine Group

To further reduce complexity, the four logic definitions are divided into logical sub-steps, each of which focuses 

on one aspect of the more complex processes of evaluation and forecasting. In addition, the vaccine-specific 

values have been abstracted out of the logic and reside in the Supporting Data.

2.6 LOGIC DEFINITION – EVALUATION

2.6.1 Purpose

The logic definition evaluation describes the process of evaluating a single vaccine 

dose administered against a defined target dose to determine if the vaccine dose 

administered is valid or not valid for that specific target dose.

2.6.2 What problem it helps solve

Focusing only on evaluation of a patient’s immunization history greatly simplifies the complexity of interpreting 

ACIP recommendations. It also reduces the breadth of the impact on the logic of future ACIP recommendation 

changes.

TABLE 2-4 EVALUATION SUGGESTED AUDIENCE

Role Perspective

Business Analyst Understanding and documenting the logical steps of evaluation and the impact 

of Supporting Data elements.

Technical Developer Coding the system to implement the functional processes described in the logic 

definition. Understanding the integration of the Supporting Data, Logic 

Specification and processing model.

2.6.3 How and where it is documented

Chapter 6 describes the process of evaluation. It is documented using the following:

• A thin process model that represents the high-level steps to evaluate each of the logical sub-components 

which ultimately affect the validity of a vaccine dose administered.

• Timelines that graphically represent dates and/or time intervals used in evaluation.

• Attribute tables that provide the attribute type, name, and assumed value if empty.

• Decision tables that state the conditions and rules which must be assessed for a specific logical subcomponent and the resulting outcomes.

Evaluate Vaccine Group Select Best Patient Series Forecasting Evaluation Logic Definition

Evaluate Vaccine Group Select Best Patient Series Forecasting Evaluation Logic Definition
Logic Specification for ACIP Recommendations v4.4 Page 18 of 151

2.7 LOGIC DEFINITION – FORECASTING 

2.7.1 Purpose

The logic definition forecasting describes the process of using a patient’s history 

to determine immunization due dates.

2.7.2 What problem it helps solve

Focusing only on forecasting immunization due dates, separate from determining 

which possible paths to immunity a patient is on, greatly simplifies the complexity of interpreting ACIP 

recommendations. It also reduces the breadth of the impact on the logic of future ACIP recommendation 

changes. Even though the logic for evaluation and forecasting is separate, sound evaluation simplifies the work 

of forecasting; i.e., understanding which target dose has been satisfied simplifies forecasting the next target 

dose in the patient series.

TABLE 2-5 FORECASTING SUGGESTED AUDIENCE

Role Perspective

Business Analyst Understanding and documenting the logical steps of forecasting and the impact 

of Supporting Data elements.

Technical Developer Coding the system to implement the functional processes described in the logic 

definition. Understanding the integration of the Supporting Data, Logic 

Specification and processing model.

2.7.3 How and where it is documented

Chapter 7 describes the process of forecasting. It is documented using the following:

• A thin process model that represents the high-level steps to forecast immunization due dates.

• Attribute tables that provide the attribute type, name, and assumed value if empty.

• Timelines that graphically represent dates and/or time intervals used to generate or result from the 

generated forecasted dates.

• Decision tables that represent the combination of conditions and the resulting impact on the need to 

generate forecasted dates.

2.8 LOGIC DEFINITION – SELECT PATIENT SERIES

2.8.1 Purpose 

The logic definition select patient series describes the process of selecting the 

patient series, out of the possible series, which puts the patient on the best path to 

immunity based on various important factors.

2.8.2 What problem it helps solve

There is more than one path which can lead a patient to immunity. See Appendix F for representations of multiple 

patient series (paths to immunity) for an antigen. Select patient series helps to put a specific patient on the best 

path for them through the application of ACIP recommendations given the outcomes of evaluation and 

forecasting.

Evaluate Vaccine Group Select Best Patient Series Forecasting Evaluation Logic Definition

Evaluate Vaccine Group Select Best Patient Series Forecasting Evaluation Logic Definition
Logic Specification for ACIP Recommendations v4.4 Page 19 of 151

TABLE 2-6 SELECT PATIENT SERIES SUGGESTED AUDIENCE

Role Perspective

Business Analyst Understanding and documenting the logical steps of Select Patient Series and 

the factors used when scoring patient series.

Technical Developer Coding the system to implement the functional processes described in the logic 

definition. Understanding the integration of the Supporting Data, Logic 

Specification, and processing model.

2.8.3 How and where it is documented

Chapter 8 describes the process of selecting best patient series. It is documented using the following:

• A thin process model that represents the high-level steps to select patient series.

• A vocabulary table that provides meanings to terms used strictly in the select patient series logic 

definition.

• Decision tables that represent the combination of conditions and the resulting impact on classifying and 

scoring patient series.

• Business rules used to concisely, unambiguously describe what and how various factors affect the score 

given to competing patient series.

2.9 LOGIC DEFINITION – IDENTIFY AND EVALUATE VACCINE GROUP

2.9.1 Purpose 

The logic definition identify and evaluate vaccine group describes the process of 

combining patient series, described in terms of antigens, into vaccine group-based 

forecasts.

2.9.2 What problem it helps solve

Performing evaluation and forecasting at the antigen-level provides for an extremely effective and 

comprehensive approach. However, clinicians and physicians look at vaccines in a broader grouping known as 

vaccine groups. Identify and evaluate vaccine group pulls this notion together to provide a clinical-centric 

forecast based on vaccine groups.

TABLE 2-7 IDENTIFY AND EVALUATE VACCINE GROUP SUGGESTED AUDIENCE

Role Perspective

Business Analyst Understanding and documenting the logical steps of identifying and evaluating 

vaccine groups.

Technical Developer Coding the system to implement the functional processes described in the logic 

definition. Understanding the integration of the Supporting Data, Logic 

Specification, and processing model.

2.9.3 How and where it is documented

Evaluate Vaccine Group Select Best Patient Series Forecasting Evaluation Logic Definition
Logic Specification for ACIP Recommendations v4.4 Page 20 of 151

Chapter 9 describes the process of identifying and evaluating vaccine groups. It is documented using the 

following:

• A thin process model that represents the high-level steps to identify and evaluate vaccine groups.

• Decision tables that represent the combination of conditions which dictate which set of vaccine group 

forecasting rules apply.

• Business rules used to concisely, unambiguously describe how to apply the proper vaccine group 

forecasting rules to determine the appropriate vaccine group-based forecast

2.10PROCESSING MODEL

2.10.1 Purpose

The logic definitions focus on the functionality necessary to evaluate and forecast 

based on one specific target dose and one specific vaccine dose administered. 

This simplifies the entire process by only focusing on one item at a time. However, 

there are many possible paths to immunity which result in many potential target 

doses. In addition, a patient’s history often contains multiple vaccine doses 

administered. Thus, the processing model describes, in a technology-neutral fashion, the algorithms necessary 

to merge multiple executions and results of the logic definitions for evaluation and forecasting. 

2.10.2 What problem it helps solve

Separating the functionality of evaluation from forecasting and the algorithmic details of handling multiple 

iterations of evaluation and forecasting greatly simplifies the complexity of implementing ACIP recommendations. 

It also reduces the breadth of the impact on the logic of future ACIP recommendation changes

TABLE 2-8 PROCESSING MODEL SUGGESTED AUDIENCE

Role Perspective

Technical Developer Coding the system to implement the functional processes described in the logic 

definition. Understanding the integration of the patient related data, Supporting 

Data, and Logic Specification.

2.10.3 How and where it is documented

Chapter 4 describes the more detailed algorithms represented in the Logic Specification Processing Model. 

These algorithms are documented using activity diagrams, which represent the detailed looping necessary to 

evaluate a patient’s full immunization history against multiple potential vaccination series resulting in multiple 

candidate forecasted immunization due dates. 

2.11DECISION TABLE OVERVIEW

A decision table documents the way that a system responds to various combinations of input conditions. It 

describes business rules where the required response depends on a number of factors that must all be 

considered at the same time. Decision tables are useful when trying to clearly define a set of conditions, how 

they work in combination, and what actions should be taken on encountering a given set of conditions. 

Processing Model For.

Eval.
Logic Specification for ACIP Recommendations v4.4 Page 21 of 151

There are various ways of documenting decision tables. The Logic Specification uses two different styles. Both 

start with a simple business question as the title or subject of the decision table. 

The majority of decision tables in the Logic Specification use a condition/outcome style formatting. In this 

approach, the top half lists conditions based on the business question. The bottom half of the decision table 

states the outcome after the rules have been applied to the condition.

In order to familiarize the reader with the use of decision tables in the Logic Specification, an example is provided 

below using a real-world scenario that is unrelated to immunizations.

TABLE 2-9 SHOULD I GET MY CAR WASHED?

CONDITIONS RULES

Is the car wash open? No - -

Is my car dirty? - No -

Do I have enough money? - - No

OUTCOMES

Default: Yes. I should get 

my car washed.

No. The car wash is 

closed.

No. My car is not dirty. No. I cannot afford it.

The following table provides explanations of how the various outcomes were determined.

TABLE 2-10 EXPLANATIONS OF OUTCOMES

Outcome Explanations

No. The car wash is 

closed.

The answer “No” to the first condition means the car wash was not open. The other 

conditions (Is my car dirty? or Do I have enough money?) do not matter.

No. My car is not 

dirty.

The answer “No” to the second condition means my car is not dirty. The other 

conditions (Is the car wash open? Or Do I have enough money?) do not matter.

No. I cannot afford it. The answer “No” to the third condition means I do not have enough money. The other 

conditions (Is the car wash open? Or Is my car dirty?) do not matter. 

Default: Yes. I should 

get my car washed.

The default outcome is to wash the car. When nothing is preventing the car from being 

washed, the decision is to always wash the car.

In the second style, the outcome is the intersection of a row and column where the row and column heading are 

the conditions. The example below illustrates exercise based on the day of the week and the weather outside. 

For example, the exercise on Saturday when it is raining outside is a Yoga Class.

TABLE 2-11 WHAT EXERCISE SHOULD I DO TODAY?

Weather

Dry Raining Snowing

Monday Trail Run Treadmill Cross Country Ski

Tuesday No Exercise No Exercise No Exercise

Wednesday Trail Run Treadmill Cross Country Ski

Thursday Trail Run Treadmill Cross Country Ski

Friday No Exercise No Exercise No Exercise
Logic Specification for ACIP Recommendations v4.4 Page 22 of 151

Weather

Saturday Golf Yoga Class Downhill Ski

Sunday Golf Yoga Class Downhill Ski

A decision table is helpful when decision-based rules have to be applied in combination. As illustrated above, 

the Logic Specification refers to key components of a decision table as (1) Conditions, (2) Rules, and (3) 

Outcomes. These components function together in the following manner: Conditions + Answers = Rules; Rules 

determine Outcomes.

Logical reasoning used to determine the outcome in the example decision tables above is similar to the decision 

tables used in the Logic Specification. The goal of a decision table is to answer a business question while 

providing the correct technical outcome.
Logic Specification for ACIP Recommendations v4.4 Page 23 of 151

3 LOGIC SPECIFICATION CONCEPTS

The information contained in this chapter will be useful in understanding the business rules, decision tables, and 

process models that are used in the Logic Specification. The first section provides a basic understanding of 

target dose and how it is used throughout the document. Next, relevant meanings of statuses used during 

evaluation and forecasting are provided for clarity. Then, rules for selecting the correct Supporting Data is 

provided. Business rules used when calculating dates for evaluation and forecasting are provided next. 

3.1 TARGET DOSE 

Target dose is a term used often in the Logic Specification document. A target dose is a patient-specific dose 

required to satisfy the recommendations of ACIP. Until a target dose is satisfied, the patient is not allowed to 

move to the next target dose in the patient series. The patient remains on the “unsatisfied” target dose until the 

patient has a “valid” vaccine dose administered that satisfies the target dose. A target dose is also allowed to 

be skipped. This situation isn’t the common path and not immediately discussed here. Details on skipping target 

doses can be found in Chapters 6 and 7.

This concept can be seen graphically below in Figure 3-1. For simplicity in this hypothetical patient series, the 

target doses are defined only by the minimum age. The target doses have minimum ages of 0 days, 2 months, 

and 6 months. These are the minimum ages allowed by this patient series. The patient must have vaccine 

doses administered on or after these minimum ages to be considered valid. A valid vaccine dose administered 

will satisfy a target dose and allow movement to the next target dose. A vaccine dose administered which is 

anything but valid does not satisfy a target dose and does not allow movement to the next target dose. 

This can be seen in Figure 3-1 by looking at target dose 2 and vaccine doses administered dose 2 and dose 3. 

Dose 2 was administered too early and resulted in the evaluation status “not valid.” A not valid vaccine dose 

administered means the target dose was not satisfied and must be repeated. Dose 3 was given at an appropriate 

age which resulted in the evaluation status “valid” and satisfied the goals of target dose 2. This allows movement 

to target dose 3 which is subsequently satisfied by vaccine dose administered dose 4.

While not shown on this graphic, there is also a status which tracks the patient’s progress towards completion of 

a patient series. In this example, the patient series status is “not complete” for the first three vaccine doses 

administered. The patient series status is changed to “complete” once the fourth vaccine dose administered 

satisfies the third target dose which completes the patient series.
Logic Specification for ACIP Recommendations v4.4 Page 24 of 151

FIGURE 3-1 HOW A VACCINE DOSE ADMINISTERED SATISFIES A TARGET DOSE

3.2 STATUSES

The Logic Specification uses different statuses to denote the state of evaluation, target dose, and patient series. 

The following tables provide the meanings of statuses used in Logic Specification business rules and decision 

tables.

TABLE 3-1 EVALUATION STATUSES

Status Meaning

Extraneous An evaluation status that indicates the vaccine dose administered was not administered according to ACIP 

recommendations, but the dose does not need to be repeated (including maximum age and extra doses) 

Not Valid An evaluation status that indicates the vaccine dose administered was not administered according to ACIP 

recommendations and must be repeated at an appropriate time in the future 

Sub-standard An evaluation status that indicates the vaccine dose administered has a known dose condition (e.g., expired, 

sub-potent, and recall) which requires the dose to be repeated at an appropriate time in the future 

Valid An evaluation status that indicates the vaccine dose administered was administered according to ACIP 

recommendations 
Logic Specification for ACIP Recommendations v4.4 Page 25 of 151

TABLE 3-2 TARGET DOSE STATUSES

Status Meaning

Not Satisfied A target dose status that indicates no vaccine dose administered has met the goals of the target dose 

Satisfied A target dose status that indicates a vaccine dose administered has met the goals of the target dose 

Skipped A target dose status that indicates no vaccine dose administered has met the goals of the target dose. Due 

to the patient's age and/or interval from a previous dose, the target dose does not need to be satisfied. 

TABLE 3-3 PATIENT SERIES STATUSES

Status Meaning

Aged Out A patient series status that indicates the patient exceeded the maximum age prior to completing the patient 

series 

Complete A patient series status that indicates the patient has met all of the ACIP recommendations for the patient 

series 

Contraindicated A patient series status that indicates no further vaccines should be administered at this time for the patient 

series 

Immune A patient series status that indicates the patient has evidence of immunity indicating no further vaccines 

are needed for the patient series 

Not Complete A patient series status that indicates the patient has not yet met all of the ACIP recommendations for the 

patient series 

Not Recommended A patient series status that indicates the patient's immunization history provides sufficient protection 

against a target disease and there's no recommended action at this time 

3.3 SELECTING SUPPORTING DATA

When a clinical recommendation is changed, it is typically applied to patient evaluation and forecasting 

retroactively but, depending on the nature of the change, the evaluation status of administered doses may or 

may not change. For example, if a recommendation changed the minimum interval from 6 months to 4 months, 

previously administered doses that met the 6 month interval requirement are still considered valid when the new 

4 month interval is applied. For this reason, many recommendations changes are instituted in new published 

versions of the Supporting Data simply as a new value with no indication of the previous value. 

However, some recommendation changes are not applied retroactively, and historical Supporting Data must be 

retained and selectively applied during the evaluation and forecasting processes. For example, prior to the ACIP 

HPV recommendation published 12/16/2016, the absolute minimum interval between Doses 1 and 3 was 16 

weeks but the recommendation increased the absolute minimum interval to 5 months minus 4 days. However, 

the change was not applied retroactively. Therefore, a third dose administered prior to 12/16/2016 need only 

meet the 16-week interval while a third dose (for a different patient) administered on or after 12/16/2016 would 

need to meet the longer 5 month interval. In the figure below, these differential requirements are represented, 

highlighted in blue, in the Supporting Data using Effective and Cessation Dates which indicate a date range that 

the Supporting Data component was in effect. 

Only a subset of the Supporting Data Logical Components use Effective and Cessation Dates elements (e.g. 

Age, Preferable Interval, Allowable Interval and Conditional Skip). When a Logical Component does not include 
Logic Specification for ACIP Recommendations v4.4 Page 26 of 151

Effective and Cessation Dates elements (e.g. Preferable Vaccine, Allowable Vaccine, Inadvertent Vaccine, 

Recurring Dose and Seasonal Recommendation), the Instances of supporting data in that Logical Component 

must be selected for evaluation and forecasting purposes.

In order to determine if a Supporting Data Logical Component Instance is relevant, the anchor date (the 

administration date in the case of an evaluation or the assessment date in the case of a forecast) must fall 

between the Effective and Cessation Dates (see decision table below) for the Supporting Data. This selection 

process must be applied any time either the Effective Date or Cessation Date is valued other than “n/a” in the 

Supporting Data as highlighted in red below. Data Instances where both valued “n/a” must be always be applied 

during the evaluation and forecasting process.

FIGURE 3-2 SELECTING SUPPORTING DATA

TABLE 3-4 RELEVANT SUPPORTING DATA ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Vaccine dose administered Date Administered -

Runtime data Assessment Date current date

Supporting Data (Effective Date) Effective Date 01/01/1900

Supporting Data (Cessation Date) Cessation Date 12/31/2999

TABLE 3-5 SELECT RELEVANT SUPPORTING DATA LOGICAL COMPONENT ELEMENT

Business Rule ID Business Rule

RELEVANT-1 A logical component instance must be used in evaluation if one of the following is true:

• the effective date and cessation date are both 'n/a'

• the date administered is on or after the effective date and on or before the cessation date. 

RELEVANT-2 A logical component instance must be used in forecasting or selecting the best patient series if one of the 

following is true:

• the effective date and cessation date are both 'n/a'

• the assessment date is on or after the effective date and on or before the cessation date. 

In addition to Supporting Data selection by Effective and Cessation Dates, relevant Conditional Skip Logic 

Component Instances must be selected on the basis of context. In some scenarios, the rationale for skipping a 

dose in a series differs depending on whether a retrospective evaluation or a prospective forecast is being 

performed. In the Supporting Data, 4 contexts are possible:

• Evaluation – applicable when a dose is being evaluated (see section 6.2)

• Forecast – applicable when a dose is being forecasted (see section 7.1)

• Both – applicable when a dose is being either evaluated or forecasted (see sections 6.2 and 7.1)

• n/a – there are no conditions that can result in a dose being skipped during either evaluation or forecast
Logic Specification for ACIP Recommendations v4.4 Page 27 of 151

FIGURE 3-3 CONDITIONAL SKIP CONTEXT

3.4 DATE CALCULATIONS

Business rules that are specific to calculating dates are provided in this section. A calculated date is a date that 

is mathematically derived from one or more terms. The first table provides rules for calculating dates in general. 

The second table provides rules for calculating dates by logical component.

TABLE 3-6 GENERAL DATE RULES

Business Rule ID Business Rule Example

CALCDT-1 The computed date of adding any number of years to an existing 

date must be calculated by incrementing the date-year while 

holding the date-month and date-day constant. 

01/01/2000 + 3 years = 01/01/2003 

CALCDT-2 The computed date of adding any number of months to an existing 

date must be calculated by incrementing the date-month (and dateyear, if necessary) while holding the date-day constant. 

01/01/2000 + 6 months = 07/01/2000 

11/01/2000 + 6 months = 05/01/2001 

CALCDT-3 The computed date of adding any number of weeks or days to an 

existing date must be calculated by adding the total days to the 

existing date. 

01/01/2000 + 3 days = 01/04/2000 

01/01/2000 + 3 weeks = 01/22/2000 

02/01/2000 + 5 weeks = 03/07/2000 

(leap year) 

02/01/2001 + 5 weeks = 03/08/2001 

(not a leap year) 

CALCDT-4 The computed date of subtracting any number of days from an 

existing date must be calculated by subtracting the total days from 

the existing date. 

01/15/2000 – 4 days = 01/11/2000 

CALCDT-5 A computed date which is not a real date must be moved forward 

to first day of the next month. 

03/31/2000 + 6 months = 10/01/2000 

(September 31 does not exist) 

08/31/20010 + 6 months = 

03/01/2001 (February 31 does not 

exist) 

CALCDT-6 A computed date must be calculated by first adjusting the years, 

followed by the months, and finally the weeks and/or days. 

01/31/2000 + 6 months – 4 days = 

07/27/2000 
Logic Specification for ACIP Recommendations v4.4 Page 28 of 151

TABLE 3-7 LOGICAL COMPONENT DATE RULES

Business Rule ID Business Rule

CALCDTAGE-1 A patient's maximum age date must be calculated as the patient's date of birth plus the maximum age. 

CALCDTAGE-2 A patient's latest recommended age date must be calculated as the patient's date of birth plus the latest 

recommended age. 

CALCDTAGE-3 A patient's earliest recommended age date must be calculated as the patient's date of birth plus the earliest 

recommended age. 

CALCDTAGE-4 A patient's minimum age date must be calculated as the patient's date of birth plus the minimum age. 

CALCDTAGE-5 A patient's absolute minimum age date must be calculated as the patient's date of birth plus the absolute 

minimum age. 

CALCDTALLOW-1 A patient's allowable vaccine type begin age date must be calculated as the patient's date of birth plus the 

vaccine type begin age of an allowable vaccine. 

CALCDTALLOW-2 A patient's allowable vaccine type end age date must be calculated as the patient's date of birth plus the 

vaccine type end age of an allowable vaccine. 

CALCDTCI-1 A patient's contraindication begin age date must be calculated as the patient's date of birth plus the 

contraindication begin age of a contraindication. 

CALCDTCI-2 A patient's contraindication end age date must be calculated as the patient's date of birth plus the 

contraindication end age of a contraindication. 

CALCDTCONFLICT-1 The conflict begin interval date for a previous vaccine dose administered must be calculated as the date 

administered of the previous vaccine dose administered plus the conflict begin interval of a vaccine type 

conflict where all the following are true:

• The vaccine type of the current vaccine dose administered is an impacted vaccine type. 

• The vaccine type of the previous vaccine dose administered is a conflicting vaccine type for the 

impacted vaccine type. 

CALCDTCONFLICT-2 The conflict end interval date for a previous vaccine dose administered must be calculated as one of the 

following:

• the date administered of the previous vaccine dose administered plus the minimum conflict end 

interval of a vaccine type conflict if all the following are true: 

o The vaccine type of the current vaccine dose administered is an impacted vaccine type. 

o The vaccine type of the previous vaccine dose administered is a conflicting vaccine type 

for the impacted vaccine type. 

o The previous vaccine dose administered has one of the following:

▪ an evaluation status of 'Valid'

▪ no evaluation status.

• the date administered of the previous vaccine dose administered plus the conflict end interval of a 

vaccine type conflict if all the following are true: 

o The vaccine type of the current vaccine dose administered is an impacted vaccine type. 

o The vaccine type of the previous vaccine dose administered is a conflicting vaccine type 

for the impacted vaccine type. 

o The previous vaccine dose administered has an evaluation status.

o The previous vaccine dose administered does not have an evaluation status of 'Valid'.

CALCDTCONFLICT-3 The forecast conflict end date of a vaccine type conflict that impacts a next target dose must be calculated 

as the date administered of a vaccine dose administered plus the conflict end interval of the vaccine type 

conflict if all the following are true:

• The next target dose is tracked by a series dose that includes a preferable vaccine that is 

classified by an impacted vaccine type. 

• The vaccine type of the vaccine dose administered is a conflicting vaccine type for the impacted 

vaccine type. 
Logic Specification for ACIP Recommendations v4.4 Page 29 of 151

Business Rule ID Business Rule

CALCDTIND-1 A patient's indication begin age date must be calculated as the patient's date of birth plus the indication begin 

age of an indication. 

CALCDTIND-2 A patient's indication end age date must be calculated as the patient's date of birth plus the indication end 

age of an indication. 

CALCDTINT-1 A patient's reference dose date for an interval must be calculated as the date administered of the most 

immediate previous vaccine dose administered if all the following are true:

• The interval has a from immediate previous dose administered flag of 'Y'

• The vaccine dose administered has an evaluation status of 'Valid' or 'Not Valid'

• The vaccine dose administered is not an inadvertent administration.

CALCDTINT-2 A patient's reference dose date for an interval must be calculated as the date administered of the vaccine 

dose administered that satisfies the target dose with the same target dose number as the from target dose 

number in series if all the following are true for the interval:

• From immediate previous dose administered flag is 'N'

• From target dose number in series is not 'n/a'

CALCDTINT-3 A patient's absolute minimum interval date must be calculated as the patient's reference dose date plus the 

absolute minimum interval. 

CALCDTINT-4 A patient's minimum interval date must be calculated as the patient's reference dose date plus the minimum 

interval. 

CALCDTINT-5 A patient's earliest recommended interval date must be calculated as the patient's reference dose date plus 

the earliest recommended interval. 

CALCDTINT-6 A patient's latest recommended interval date must be calculated as the patient's reference dose date plus 

the latest recommended interval. 

CALCDTINT-8 A patient's reference dose date for an interval must be calculated as the date administered of the most recent 

vaccine dose administered that is the same vaccine type as the from most recent vaccine type if all the 

following are true for the interval:

• From immediate previous dose administered flag is 'N'

• From most recent vaccine type is not 'n/a'

• The vaccine dose administered is not an inadvertent administration.

CALCDTINT-9 A patient's reference dose date for an interval must be calculated as the observation date of the most recent 

active patient observation if all the following are true for the interval:

• From immediate previous dose administered flag is 'N'

• From relevant observation code is not 'n/a'

CALCDTLOTEXP-1 The lot number expiration date must be one of the following:

• Lot number expiration date if the month, day, and year are all known.

• Last day of the month if only the month and year are known.

CALCDTPREF-1 A patient's preferable vaccine type begin age date must be calculated as the patient's date of birth plus the 

vaccine type begin age of a preferable vaccine. 

CALCDTPREF-2 A patient's preferable vaccine type end age date must be calculated as the patient's date of birth plus the 

vaccine type end age of a preferable vaccine. 

CALCDTSKIP-3 A patient's conditional skip begin age date must be calculated as the patient's date of birth plus the 

conditional skip begin age of a conditional skip. 

CALCDTSKIP-4 A patient's conditional skip end age date must be calculated as the patient's date of birth plus the conditional 

skip end age of a conditional skip. 

CALCDTSKIP-5 A patient's conditional skip interval date must be calculated as the vaccine date administered from the 

immediate previous vaccine dose administered plus the Interval of the conditional skip condition. 
Logic Specification for ACIP Recommendations v4.4 Page 30 of 151

4 PROCESSING MODEL

At a very simple level, the major logical steps involved in the immunization evaluation and forecasting engine 

can be described in two parts. The first part is very mechanical in nature and focuses on gathering and prepping 

all of the required data. The second part uses the data gathered earlier to generate the evaluation and forecast. 

The following table lists the major steps of the processing model.

TABLE 4-1 LOGIC SPECIFICATION PROCESSING STEPS

Section Activity Goal

4.1 Gather Necessary Data The goal of this step is to gather all pertinent information which will be used in 

subsequent steps in the process.

4.2 Organize Immunization History The goal of this step is to break apart vaccine doses administered into their 

antigen parts.

4.3 Create Relevant Patient Series The goal of this step is to instantiate (Chapter 5) antigen series into relevant 

patient series for this patient.

4.4 Evaluate and Forecast All Patient 

Series

The goal of this step is to evaluate (Chapter 6) each antigen administered and 

create a forecast for each relevant patient series (Chapter 7).

4.5 Select Best Patient Series The goal of this step is to select one or more best patient series (Chapter 8) for 

the patient based on their evaluated history and forecast.

4.6 Identify and Evaluate Vaccine Group The goal of this step is to merge together patient series forecasts into a vaccine 

group forecast (Chapter 9).

Figure 4-1 provides the high-level process of the major steps of the processing model.

FIGURE 4-1 LOGIC SPECIFICATION PROCESSING MODEL
Logic Specification for ACIP Recommendations v4.4 Page 31 of 151

As illustrated in Figure 4-2, as the process model progresses, the set of patient series continually becomes more 

restricted as:

• Relevant Patient Series are selected from the total list of Antigen Series based on standard 

recommendations, patient gender, and patient observations.

• Scorable Patient Series are selected from the evaluated and forecasted Relevant Patient Series.

• A Prioritized Patient Series is selected per Series Group (provided the Series Group had at least one 

Relevant Patient Series.

• One or more non-redundant Best Patient Series are selected from the Prioritized Patient Series

FIGURE 4-2 REFINEMENT OF PATIENT SERIES

4.1 GATHER NECESSARY DATA

Gathering all the necessary data is a generic step which could technically be performed in several different ways. 

While this step is important, it is outside of the purview of this document and is only noted as a generic step in 

the process. 

The required data fall into two categories (1) Patient-related data and (2) Evaluation and forecasting data. The 

lists below provide class level data needed. Further details on these classes can be found in Appendix A.

Patient-related data needed:

• Patient

• Vaccine Dose Administered

• Vaccine

• Immunization History

• Adverse Reaction

• Patient Observations

Evaluation and forecasting data needed:

• Schedule
Logic Specification for ACIP Recommendations v4.4 Page 32 of 151

• Antigen Series

• Series Dose

• Vaccine Group

• Antigen

• Vaccine

Finally, the term “gather” is not meant to imply a fetch, get, or retrieve operation to accumulate this data. 

Depending upon the implementation, some of this data may be passed by an external entity; other data may 

already be known; and still other data may arrive at different points in the process on an as needed basis. It is 

an acknowledgement of the minimal data needed in the evaluation and forecasting processes.

4.2 ORGANIZE IMMUNIZATION HISTORY

The second step in the process is to look at the patient’s immunization history and prepare those records for 

evaluation and forecasting by breaking them into their antigen parts. This allows the evaluation and forecasting 

engine to be as granular and specific as possible for both evaluation and forecasting purposes. Later in the 

process, these antigens are assembled into commonly known vaccine groups (vaccine families) for vaccine 

group forecasts.

To provide some specifics to this step, the following tables are provided as a high-level example of the work 

organize immunization history performs.

TABLE 4-2 PRIOR TO ORGANIZE IMMUNIZATION HISTORY EXAMPLE

Product (CVX/MVX) – Description Date 

Engerix B-Peds (08/SKB) – HepB 01/01/2011

Pediarix (110/SKB) – DTaP-HepB-IPV 03/01/2011

ActHIB (48/PMC) – Hib 03/01/2011

Prevnar 13 (133/WAL) – PCV13 03/01/2011

Pediarix (110/SKB) – DTaP-HepB-IPV 06/01/2011

ActHIB (48/PMC) – Hib 06/01/2011

Prevnar 13 (133/WAL) – PCV13 06/01/2011

ProQuad (94/MSD) – MMRV 01/01/2012

TABLE 4-3 AFTER ORGANIZE IMMUNIZATION HISTORY EXAMPLE

Product (CVX/MVX) – Description Date Antigen*

Pediarix (110/SKB) – DTaP-HepB-IPV 03/01/2011 Diphtheria

Pediarix (110/SKB) – DTaP-HepB-IPV 06/01/2011 Diphtheria

Engerix B-Peds (08/SKB) – HepB 01/01/2011 HepB

Pediarix (110/SKB) – DTaP-HepB-IPV 03/01/2011 HepB

Pediarix (110/SKB) – DTaP-HepB-IPV 06/01/2011 HepB

ActHIB (48/PMC) – Hib 03/01/2011 Hib

ActHIB (48/PMC) – Hib 06/01/2011 Hib
Logic Specification for ACIP Recommendations v4.4 Page 33 of 151

Product (CVX/MVX) – Description Date Antigen*

ProQuad (94/MSD) – MMRV 01/01/2012 Measles

ProQuad (94/MSD) – MMRV 01/01/2012 Mumps

Prevnar 13 (133/Wal) – PCV13 03/01/2011 PCV

Prevnar 13 (133/Wal) – PCV13 06/01/2011 PCV

Pediarix (110/SKB) – DTaP-HepB-IPV 03/01/2011 Pertussis

Pediarix (110/SKB) – DTaP-HepB-IPV 06/01/2011 Pertussis

Pediarix (110/SKB) – DTaP-HepB-IPV 03/01/2011 Polio

Pediarix (110/SKB) – DTaP-HepB-IPV 06/01/2011 Polio

ProQuad (94/MSD) – MMRV 01/01/2012 Rubella

Pediarix (110/SKB) – DTaP-HepB-IPV 03/01/2011 Tetanus

Pediarix (110/SKB) – DTaP-HepB-IPV 06/01/2011 Tetanus

ProQuad (94/MSD) – MMRV 01/01/2012 Varicella

*Sorted by antigen and then by date

The figure below illustrates how an immunization history of vaccine doses administered can be converted into 

antigen administered records. 
Logic Specification for ACIP Recommendations v4.4 Page 34 of 151

FIGURE 4-3 ORGANIZE IMMUNIZATION HISTORY PROCESS MODEL

The process of breaking apart vaccine doses administered into their antigen parts is a fairly simple iterative 

process. 

1. For each vaccine dose administered in the patient’s immunization history, the vaccine dose administered is 

interrogated for the antigens contained within. 

2. For each antigen within a vaccine dose administered, an antigen administered record is created. The activity 

diagram above provides the basic data elements used in evaluation and forecasting. The following notes 

should be considered:

a. The CVX to Antigen Supporting Data includes Association Begin Age and Association End Age 

attributes to properly associate the administered vaccine with the proper antigen based on the 

age of patient at the time of administration (e.g., a Zoster vaccine administered below 50 years 

should be associated with Varicella).

b. The activity diagram above provides the basic data elements used in evaluation and forecasting. 

It is entirely possible different implementations may use more or less attributes from this list.

3. After all vaccine doses administered have been turned into antigen administered records, the final step in 

the activity diagram is to sort the antigen administered records by antigen and then by ascending date order 

within each antigen. Sorting these now will allow for consistent and accurate results in remainder of the 

steps.

A Supporting Data table mapping CVX codes to antigens to aid in this process can be found at the following 

location: http://www.cdc.gov/vaccines/programs/iis/cdsi.html

4.3 CREATE RELEVANT PATIENT SERIES

An antigen series is one way to reach perceived immunity against a disease. An antigen series can be thought 

of as a “path to immunity” and is described in relative terms. In many cases, a single antigen may have more 

than one successful path to immunity and as such may have more than one antigen series. Antigen series are 

defined through Supporting Data spreadsheets defined in Chapters 2 and 3. Some series, classified here as 

“Standard” series, are based on recommendations for all patients based on age. Other series, classified as “Risk” 

series, are based on recommendations for patients with specific characteristics or underlying conditions which 

put them at increased risk. Finally, some series are strictly for the purpose of “Evaluation Only” and should not 

be recommended for completion, but if already complete can be used as proof of series completion.

Similar to gathering necessary data (section 4.1), create relevant patient series will likely vary from system to 

system based on design details and technologies used. The important aspect of this step is to instantiate each 

antigen series as a relevant patient series provided it meets necessary requirements as defined by the logic in 

Chapter 5.

The process model below shows the iterative steps to create relevant patient series. At the end of this step, each 

antigen series relevant for the patient is turned into a relevant patient series. The set of relevant patient series 

for a given patient are used in subsequent chapters. Those not relevant for the patient are excluded from further 

processing.
Logic Specification for ACIP Recommendations v4.4 Page 35 of 151

FIGURE 4-4 CREATE RELEVANT PATIENT SERIES PROCESS MODEL

4.4 EVALUATE AND FORECAST ALL RELEVANT PATIENT SERIES

This step is the core of the business logic and decision points many people think of when describing evaluation 

and forecasting. In the Logic Specification, this step contains all of the clinical business rules and decision logic 

in the form of business rules and decision tables.

At the end of this step, each relevant patient series will have an evaluated history and a forecast.

The iterative nature of this step is best described with two activity diagrams. First, Figure 4-5 shows the highlevel iterative process of looping through all relevant patient series. Next, Figure 4-6 specifically deals with the 

details of evaluation. A description of the activity diagram follows each figure.
Logic Specification for ACIP Recommendations v4.4 Page 36 of 151

FIGURE 4-5 EVALUATE AND FORECAST PROCESS MODEL

At the highest level of this step, as illustrated in the figure above, a simple iterative process is used to walk 

through each relevant patient series and apply the logic defined in the evaluation and forecasting chapters. 

For each relevant patient series created in the create relevant patient series step (Chapter 5), the following steps 

are performed:

1. Evaluate the immunization history. See the evaluate immunization history activity diagram below for further 

details.

2. Create forecast dates and/or reasons for the next target dose to be administered. Process models and 

detailed decision logic on forecasting are in Chapter 7.
Logic Specification for ACIP Recommendations v4.4 Page 37 of 151

FIGURE 4-6 EVALUATE IMMUNIZATION HISTORY PROCESS MODEL

Figure 4-6 illustrates the iterative nature of evaluate immunization history in greater detail. There are two 

collections (arrays, lists, etc.) which must be traversed. The first collection is the relevant patient series 

consisting of one or more target doses. The second collection is the antigen administered records. At any point 

in the iterative process either collection could be the trigger to end our evaluation process. Specifically, whichever 

collection is exhausted first will be the trigger for ending the evaluation process.

It is important to note the contents of antigen administered records at this point in the process. Antigen 

administered records are only those which could potentially satisfy the goals of the relevant patient series. For 

example, if the patient series is a path to immunity for HepB, then the antigen administered records will only 

contain HepB records in ascending date order.

It should also be noted that when multiple relevant patient series have been created (Chapter 5), all antigen 

administered records for the patient should be evaluated against each relevant patient series, and a status (valid, 
Logic Specification for ACIP Recommendations v4.4 Page 38 of 151

not valid, etc.) should be assigned per dose per relevant patient series. An administered dose that is “valid” for 

one relevant patient series may be “not valid” for a different relevant patient series for the same patient.

The evaluate immunization history process is as follows:

1. The process begins by getting the first target dose from the relevant patient series collection. The current 

target dose is an important concept as the process moves from evaluation into forecasting. The evaluation 

process will inform the forecasting process which target dose needs to be forecasted.

2. If the antigen administered collection has elements in it, the process gets the first antigen administered and 

continues to step 3. 

a. If the antigen administered collection is empty, the evaluation process for this relevant patient series 

ends.

3. The step described as “evaluate the antigen administered record against the target dose” is a reference to 

Chapter 6 which contains process models and detailed decision logic that must be followed prior to moving 

on to step 4.

4. After the antigen administered record was evaluated against the target dose, the next step is to determine 

which collections to iterate based on the results of the evaluation. 

a. If the target dose status is satisfied, proceed to step 5.

i. The antigen administered was valid. The target dose is satisfied. The evaluation process can 

push forward to the next target dose if one exists.

b. If the target dose status is not satisfied, proceed to step 7.

i. The antigen administered did not meet the goals of the target dose. The evaluation process 

cannot move onto the next target dose.

5. This step determines if there are more target doses in the relevant patient series collection.

a. If the relevant patient series collection has been exhausted, proceed to step 6.

b. If the relevant patient series collection contains another target dose, get the next target dose and 

proceed to step 7.

6. This step determines if the current target dose (now the last target dose in the relevant patient series) is a 

recurring dose. This is a semi-rare condition (e.g., Td boosters, yearly Flu, certain risk series). A recurring 

dose may recur based on a time interval from the previous dose (i.e. a tetanus recurring dose every 10 years 

for adults) or based on a patient observation (i.e. a pertussis recurring dose with every pregnancy). 

a. If the target dose is defined to be a recurring dose, initialize a new target dose identical to the current 

target dose. The newly created target dose must now be the last element in the collection. Finally, 

iterate the collection to get this target dose and proceed to step 7.

b. If the target dose is not defined to be a recurring dose, the evaluation process for this relevant patient 

series ends. Any remaining antigen administered records should have their evaluation statuses set 

to “extraneous.”

7. This step determines if there are any more antigen administered records to evaluate.

a. If the antigen administered collection has been exhausted, the evaluation process for this relevant 

patient series ends.

b. If the antigen administered collection contains another record, get the next antigen administered 

record and return back to step 3. 

i. Repeat steps 3 – 7 until the evaluation process for this relevant patient series ends. At this 

point the process can end in one of two ways: (1) No more target doses (step 6.b) or (2) No 

more antigen administered records (step 7a).
Logic Specification for ACIP Recommendations v4.4 Page 39 of 151

4.5 SELECT BEST PATIENT SERIES

The goal of select patient series is to determine the best path(s) to immunity for the patient based on the 

evaluated immunization history, forecast, and any patient observations. A best patient series will be selected for 

each Series Group, however, some antigen series define Equivalent Series Groups which allow a single best 

series to be selected from across multiple Series Groups. Depending upon the ACIP recommendations, it is 

possible to select a single best patient series across the entire antigen(e.g., across all Series Groups). In other 

cases, multiple best patient series may be selected for a patient. For example, a patient may need to complete 

a risk series in the short term to address an underlying risk condition but still need to complete a standard series 

later in life.

The process of selecting the best patient series at the highest level is a simple iterative process which loops 

through each antigen and applies the business rules found in Chapter 8 to each antigen. A sample iterative 

process model is shown below to detail the looping structure.

FIGURE 4-7 SELECT BEST SERIES PROCESS MODEL

4.6 IDENTIFY AND EVALUATE VACCINE GROUP

The goal of identify and evaluate vaccine group is to merge together antigen-based forecasts into vaccine group 

forecasts. This is especially important in MMR and DTaP/Tdap/Td vaccine groups which each contain more 

than one antigen in their respective vaccine groups. In these cases, it is important to provide a forecast 

consistent with the vaccine group rather than the individual antigen. The business rules to create vaccine group 

forecasts are defined in Chapter 9. For vaccine groups which contain non-equivalent series groups, it is important 

to only blend best patient series of the same series type (e.g., risk with risk and standard with standard).
Logic Specification for ACIP Recommendations v4.4 Page 40 of 151

The process of identifying and evaluating a vaccine group at the highest level is a simple iterative process which 

loops through each vaccine group and applies the business rules defined in Chapter 9 to each vaccine group. 

Figure 4-8 is a sample iterative process model that shows the looping structure.

FIGURE 4-8 IDENTIFY AND EVALUATE VACCINE GROUP PROCESS MODEL
Logic Specification for ACIP Recommendations v4.4 Page 41 of 151

5 CREATE RELEVANT PATIENT SERIES

The antigen Supporting Data defines one or more antigen series for each antigen. Before beginning the 

evaluation process for a given patient, a set of relevant patient series must first be selected and created for the 

patient. Not all antigen series will be relevant for a given patient and only antigen series appropriate for the 

patient should be evaluated, forecasted, and considered for best patient series selection. The appropriateness 

of an antigen series is based on criteria such as patient gender, age, and underlying conditions.

TABLE 5-1 CREATE RELEVANT PATIENT SERIES PROCESS STEPS

Section Activity Goal

5.1 Select Relevant Patient Series The goal of this step is to identify the antigen series which are appropriate for the 

patient.

FIGURE 5-1 CREATE RELEVANT SERIES PROCESS MODEL

5.1 SELECT RELEVANT PATIENT SERIES

Select relevant patient series determines which series defined by the Supporting Data are appropriate to 

evaluate for the patient. Antigen series with a Series Type of “Standard” or “Evaluation Only” are relevant for all 

patients of the appropriate gender. Not all antigen series with a Series Type of “Risk” will be appropriate for a 

given patient. 

Given the complex nature of indications, it may not always be possible to conclusively determine if an indication 

applies to a patient. To minimize false positive forecasts, in the case where a Risk Series cannot be definitively 

determined to be relevant for a patient (that is some or all indications are inconclusive and none unambiguously 

apply to the patient) the series will not be evaluated or forecast, but a notification should be available to a clinician 

alerting them to the presence of the indication(s) which could not be resolved.
Logic Specification for ACIP Recommendations v4.4 Page 42 of 151

FIGURE 5-2 SELECT RELEVANT PATIENT SERIES PROCESS MODEL

TABLE 5-2 SELECT RELEVANT PATIENT SERIES ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Patient Gender Unknown

Patient Date of Birth -

Patient history Active Patient Observation(s) -

Supporting Data (Gender) Required Gender Gender of the patient

Supporting Data (Series Type) Series type -

Supporting Data (Indication) Observation Code -

Runtime data Assessment Date current date

Calculated date (CALCDTIND-1) Indication Begin Age Date 01/01/1900

Calculated date (CALCDTIND-2) Indication End Age Date 12/31/2999

TABLE 5-3 SELECT RELEVANT PATIENT BUSINESS RULES

Business Rule ID Business Rule

CALCDTIND-1 A patient's indication begin age date must be calculated as the patient's date of birth plus the indication begin 

age of an indication. 

CALCDTIND-2 A patient's indication end age date must be calculated as the patient's date of birth plus the indication end 

age of an indication. 
Logic Specification for ACIP Recommendations v4.4 Page 43 of 151

FIGURE 5-3 ASSESS INDICATIONS PROCESS MODEL

TABLE 5-4 DOES THE INDICATION APPLY TO THE PATIENT?

CONDITIONS RULES

Does the indication describe 

any active patient 

observations?

Yes No Unknown -

Is the indication begin age 

date ≤ assessment date < 

indication end age date?

Yes Yes Yes No

OUTCOMES Yes. The 

Indication 

applies to the 

patient.

No. The 

Indication does 

not apply to the 

patient.

No. The Indication does not apply to the 

patient; however, the Indication Text 

Description should be made available to the 

clinician for manual determination.

No. The 

indication does 

not apply to the 

patient.

TABLE 5-5 IS AN ANTIGEN SERIES A RELEVANT PATIENT SERIES FOR A PATIENT?

CONDITIONS RULES

Is the patient gender one of the 

required genders of the antigen 

series?

Yes No Yes Yes

Is the series type of the antigen 

series 'Standard' or 'Evaluation 

Only'?

Yes - No No

Does at least one indication that 

drives the need for the antigen 

series apply to the patient?

- - Yes No
Logic Specification for ACIP Recommendations v4.4 Page 44 of 151

CONDITIONS RULES

OUTCOMES Yes. The antigen 

series is a relevant 

patient series for the 

patient.

No. The antigen series 

is not a relevant patient 

series for the patient.

Yes. The antigen 

series is a relevant 

patient series for the 

patient.

No. The antigen series is 

not relevant patient 

series for the patient. 
Logic Specification for ACIP Recommendations v4.4 Page 45 of 151

6 EVALUATE VACCINE DOSE ADMINISTERED 

The core of a CDS engine is the process of evaluating a single vaccine dose administered against a defined 

target dose within a relevant patient series to determine if the vaccine dose administered is “valid” or “not valid” 

for the relevant patient series. The results will ultimately determine if all requirements of the target dose are 

satisfied. This can be accomplished by breaking the evaluation process into simple logical components. After 

processing each logical component, the results of those logical components are used to determine if the vaccine 

dose administered satisfies the goals of the target dose.

Each logical component has its own set of business rules that are used to determine if a target dose is “satisfied.” 

These business rules are documented using business rules and decision tables. (See section 2.11 to review an 

example of a decision table using a real-world scenario.) The decision table describes the way that the CDS 

engine responds to various combinations of conditions. The implementer can clearly see the set of conditions, 

how they work in combination, and what actions should be taken on a given set of conditions.

Specific attributes and decision tables are provided for each step of the evaluation process.

TABLE 6-1 EVALUATION PROCESS STEPS

Section Activity Goal

6.1 Evaluate Dose Administered Condition The goal of this step is to determine if a vaccine dose administered can be 

evaluated.

6.2 Evaluate Conditional Skip The goal of this step is to determine if the target dose can be skipped due to a 

patient’s age or immunization history.

6.3 Evaluate For Inadvertent Vaccine The goal of this step is to determine if the vaccine dose administered was an 

inadvertent administration due to the vaccine type that was administered.

6.4 Evaluate Age The goal of this step is to determine if the vaccine dose administered was given 

at an appropriate age.

6.5 Evaluate Preferable Interval The goal of this step is to determine if the vaccine dose administered was given 

at an appropriate interval.

6.6 Evaluate Allowable Interval The goal of this step is to determine if the vaccine dose administered was given 

at an allowable interval.

6.7 Evaluate Vaccine Conflict The goal of this step is to determine if the vaccine dose administered was in 

conflict with any other vaccines.

6.8 Evaluate For Preferable Vaccine The goal of this step is to determine if the vaccine dose administered was one 

of the preferable vaccines.

6.9 Evaluate For Allowable Vaccine The goal of this step is to determine if the vaccine dose administered was one 

of the allowable vaccines.

6.10 Satisfy Target Dose The goal of this step is to determine if the target dose is satisfied.
Logic Specification for ACIP Recommendations v4.4 Page 46 of 151

FIGURE 6-1 EVALUATION PROCESS MODEL

6.1 EVALUATE DOSE ADMINISTERED CONDITION 
Logic Specification for ACIP Recommendations v4.4 Page 47 of 151

Evaluate Dose Administered Condition checks the dose administered to see if the target dose must be repeated 

regardless of the other evaluation rules. 

Relationship to ACIP recommendations: 

• Doses which were administered after the lot expiration date or which contain a condition do not need to 

be evaluated. 

• Examples of conditions which would prevent evaluation of a vaccine dose administered range from 

misadministration to recalls to cold chain breaks.

The following processing model, attribute table and decision table are used to determine if dose administered 

can be evaluated.

FIGURE 6-2 VACCINE DOSE ADMINISTERED CONDITION PROCESS MODEL

TABLE 6-2 DOSE ADMINISTERED CONDITION ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Vaccine dose administered Date Administered -

Vaccine dose administered Dose Condition Flag -

Calculated date (CALCDTLOTEXP-1) Lot Expiration Date 12/31/2999

TABLE 6-3 CAN THE VACCINE DOSE ADMINISTERED BE EVALUATED?

CONDITIONS RULES

Date administered > lot 

number expiration 

date?

Yes No No

Is the dose condition 

flag 'Y'?

- Yes No

OUTCOMES No. The vaccine dose administered 

cannot be evaluated. Target dose 

status is 'not satisfied.' 

Evaluation status is 'sub-standard.'

No. The vaccine dose administered cannot 

be evaluated. Target dose status is 'not 

satisfied.' Evaluation status is 'sub-standard.'

Yes. The vaccine dose 

administered can be 

evaluated.

6.2 EVALUATE CONDITIONAL SKIP 
Logic Specification for ACIP Recommendations v4.4 Page 48 of 151

Evaluate Conditional Skip addresses times when a target dose can be skipped. A dose should be considered 

necessary unless it is determined that it can be skipped. The most common scenarios for skipping a dose are:

• Catch-up doses where the patient is current with their administrations and does not need to catch-up

• The patient is behind schedule and the total number of doses needed to complete the relevant patient 

series can be reduced

• The previously administered dose(s) negates the need for the current target dose

Only Conditional Skip Instances with a context of Evaluation or Both should be used. In cases where a target 

dose does not specify Conditional Skip attributes, the target dose cannot be skipped.

A dose may be skipped based on whether or not one or more conditions evaluates to true. Conditions are 

classified as one of a number of types, each with one or more parameters in the Supporting Data. Conditions 

are contained within sets. Each set contains one or more conditions to be evaluated. Within a set, one or more 

conditions must be met for the set to be met. In the case where a set contains multiple conditions, whether all 

conditions or just one condition must be met is specified by the Condition Logic (e.g., AND vs. OR). Similarly, a 

dose may contain multiple sets. In the case where a dose contains multiple sets, whether all sets or just one set 

must be met is specified by the Set Logic. 

Finally, in an effort to reduce page size and eliminate duplicate logic which could result in typographical and 

consistency errors, this section of logic is defined here once, but used in both Evaluation and Forecasting. The 

forecasting chapter refers the reader back to this section for appropriate logic.

The following process model, attribute table, and decision table are used to determine if the target dose can be 

skipped.
Logic Specification for ACIP Recommendations v4.4 Page 49 of 151

FIGURE 6-3 CONDITIONAL SKIP PROCESS MODEL

TABLE 6-4 CONDITIONAL SKIP ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Vaccine dose administered Date Administered -

Patient Immunization History Administered Dose Count -

Supporting Data (Conditional Skip) Conditional Skip elements -
Logic Specification for ACIP Recommendations v4.4 Page 50 of 151

Attribute Type Attribute Name Assumed Value if Empty

Runtime data Assessment Date current date

Runtime data Earliest Date -

Calculated date (CALCDTSKIP-3) Conditional Skip Begin Age Date 01/01/1900

Calculated date (CALCDTSKIP-4) Conditional Skip End Age Date 12/31/2999

Calculated date (CALCDTSKIP-5) Conditional Skip Interval Date -

TABLE 6-5 CONDITIONAL SKIP BUSINESS RULES

Business Rule ID Business Rule

CALCDTSKIP-3 A patient's conditional skip begin age date must be calculated as the patient's date of birth plus the 

conditional skip begin age of a conditional skip. 

CALCDTSKIP-4 A patient's conditional skip end age date must be calculated as the patient's date of birth plus the conditional 

skip end age of a conditional skip. 

CALCDTSKIP-5 A patient's conditional skip interval date must be calculated as the vaccine date administered from the 

immediate previous vaccine dose administered plus the Interval of the conditional skip condition. 

CONDSKIP-1 The Number of Conditional Doses Administered must be calculated as the count of vaccine doses 

administered where all of the following are true:

• Vaccine Type is one of the conditional skip vaccine types.

• Date Administered is:

o on or after the conditional skip begin age date and before the conditional skip end age 

date OR 

o on or after the conditional skip start date and before conditional skip end date

• Evaluation Status is:

o 'Valid' if the conditional skip dose type is 'Valid' OR

o of any status if the conditional skip dose type is 'Total'.

CONDSKIP-2 The Conditional Skip Reference Date must be one of the following:

• The Date Administered of the vaccine dose administered when evaluating a vaccine dose 

administered.

• The Assessment Date when determining a forecast.

• The Earliest Date when validating a forecast.

TABLE 6-6 CONDITIONAL TYPE OF AGE – IS THE CONDITION MET?

CONDITIONS RULES 

Is the Conditional Skip End Age Date > Conditional Skip Reference Date 

≥ Conditional Skip Begin Age Date?

Yes No

OUTCOMES Yes. The condition is met. No. The condition is not met.

TABLE 6-7 CONDITIONAL TYPE OF COMPLETED SERIES – IS THE CONDITION MET?
Logic Specification for ACIP Recommendations v4.4 Page 51 of 151

CONDITIONS RULES

Does the Does the Conditional Skip Series Group identify a Series Group with at least one 

relevant patient series with a patient series status of ‘Complete’?

Yes No

OUTCOMES Yes. The condition 

is met.

No. The condition is 

not met.

TABLE 6-8 CONDITIONAL TYPE OF INTERVAL – IS THE CONDITION MET?

CONDITIONS RULES

Has at least one dose been administered to the patient? Yes Yes No

Is the Conditional Skip Reference Date ≥ Conditional Skip 

Interval Date?

Yes No -

OUTCOMES Yes. The condition is 

met.

No. The condition is not 

met.

No. The condition is not 

met.

TABLE 6-9 CONDITIONAL TYPE OF VACCINE COUNT BY AGE OR DATE – IS THE CONDITION MET?

Number of conditional doses administered 

(BR: CONDSKIP-1) / Dose Count Logic

Greater than Conditional 

Skip Dose Count

Equal to Conditional 

Skip Dose Count

Less than Conditional 

Skip Dose Count

Greater Than Yes. The condition is met. No. The condition is not 

met.

No. The condition is not 

met.

Equal No. The condition is not 

met.

Yes. The condition is 

met.

No. The condition is not 

met.

Less Than No. The condition is not 

met.

No. The condition is not 

met.

Yes. The condition is met.

TABLE 6-10 IS THE CONDITIONAL SKIP SET MET?

How many conditions were met? / 

Condition Logic Type

All At least one, but not all None

AND Yes. The set is met. No. The set is not met. No. The set is not met.

OR Yes. The set is met. Yes. The set is met. No. The set is not met.

TABLE 6-11 CAN THE TARGET DOSE BE SKIPPED?

How many sets 

were met?/

Set Logic Type

All At least one, but not all None

AND Yes. The target dose can be skipped. The 

target dose status is 'Skipped'.

No. The target dose cannot be skipped. No. The target dose 

cannot be skipped.
Logic Specification for ACIP Recommendations v4.4 Page 52 of 151

How many sets 

were met?/

Set Logic Type

All At least one, but not all None

OR Yes. The target dose can be skipped. The 

target dose status is 'Skipped'.

Yes. The target dose can be skipped. The 

target dose status is 'Skipped'.

No. The target dose 

cannot be skipped.

6.3 EVALUATE FOR INADVERTENT VACCINE

Evaluate for inadvertent vaccine determines if the vaccine type of a vaccine dose administered was an 

inadvertent administration due to the vaccine type that was administered. 

The following process model, attribute table, Business Rule table and decision table, are used to evaluate for an 

unallowable vaccine.

FIGURE 6-4 EVALUATE FOR AN INADVERTENT VACCINE PROCESS MODEL

TABLE 6-12 INADVERTENT VACCINE ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Vaccine dose administered Vaccine Type -

Supporting Data (inadvertent Vaccine) Vaccine Type -

TABLE 6-13 WAS THE VACCINE DOSE ADMINISTERED AN INADVERTENT ADMINISTRATION FOR THE TARGET 

DOSE?

CONDITIONS RULES

Is the vaccine type of the vaccine dose 

administered one of the vaccine types 

of an inadvertent vaccine for the target 

dose?

Yes No

OUTCOMES Yes. The vaccine dose administered was an inadvertent 

administration for the target dose. Target Dose Status is 

'Not Satisfied'. Evaluation Status is 'Not Valid'. Evaluation 

Reason is 'Inadvertent Administration'.

No. The vaccine dose 

administered was not an 

inadvertent administration for 

the target dose.

6.4 EVALUATE AGE 
Logic Specification for ACIP Recommendations v4.4 Page 53 of 151

Evaluate age validates the age at administration of a vaccine dose administered against a defined age range of 

a target dose. In cases where a target dose does not specify age attributes, the age at administration is 

considered “valid.” 

FIGURE 6-5 EVALUATE AGE TIMELINE

The following process model, attribute table and decision table are used to evaluate age at administration.

FIGURE 6-6 EVALUATE AGE PROCESS MODEL

TABLE 6-14 AGE ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Patient Date of Birth -

Vaccine dose administered Date Administered -

Calculated date (CALCDTAGE-1) Maximum Age Date 12/31/2999

Calculated date (CALCDTAGE-4) Minimum Age Date 01/01/1900

Calculated date (CALCDTAGE-5) Absolute Minimum Age Date 01/01/1900

TABLE 6-15 WAS THE VACCINE DOSE ADMINISTERED AT A VALID AGE?
Logic Specification for ACIP Recommendations v4.4 Page 54 of 151

CONDITIONS RULES

Is the date 

administered < 

absolute minimum age 

date?

Yes No No No No No

Is the absolute 

minimum age date ≤ 

date administered < 

minimum age date?

No Yes Yes Yes No No

Is the minimum age 

date ≤ date 

administered < 

maximum age date?

No No No No Yes No

Is the date 

administered ≥ 

maximum age date?

No No No No No Yes

Is this the first target 

dose?

- No No Yes - -

Is the evaluation status 

of the previous vaccine 

dose administered 'Not 

Valid' due to age or 

interval 

recommendations and 

< 1 year from the 

vaccine dose 

administered being 

evaluated?

- Yes No - - -

OUTCOMES No. The vaccine 

dose 

administered 

was not 

administered at 

a valid age for 

the target dose. 

Evaluation 

reason is 'Too 

young'.

No. The vaccine 

dose 

administered 

was not 

administered at 

a valid age for 

the target dose. 

Evaluation 

reason is 'Too 

young'.

Yes. The 

vaccine dose 

administered 

was 

administered at 

a valid age for 

the target dose. 

Evaluation 

reason is 'Grace 

period'.

Yes. The 

vaccine dose 

administered 

was 

administered at 

a valid age for 

the target dose. 

Evaluation 

reason is 'Grace 

period'.

Yes. The 

vaccine dose 

administered 

was 

administered at 

a valid age for 

the target 

dose.

No. The vaccine 

dose 

administered 

was not 

administered at a 

valid age for the 

target dose. It is 

extraneous. 

Evaluation 

reason is 'Too 

old'.

TABLE 6-16 EVALUATE AGE BUSINESS RULES

Business Rule ID Business Rule

CALCDTAGE-1 A patient's maximum age date must be calculated as the patient's date of birth plus the maximum age. 

CALCDTAGE-4 A patient's minimum age date must be calculated as the patient's date of birth plus the minimum age. 

CALCDTAGE-5 A patient's absolute minimum age date must be calculated as the patient's date of birth plus the absolute 

minimum age. 
Logic Specification for ACIP Recommendations v4.4 Page 55 of 151

6.5 EVALUATE PREFERABLE INTERVAL

Evaluate preferable interval validates the date administered of a vaccine dose administered against defined 

preferable interval(s) from previous vaccine dose(s) administered or other events. In cases where a target dose 

does not specify preferable interval attributes, the interval is considered “valid.”

Preferable intervals can be measures in four different ways:

• “From Immediate Previous Dose Administered Flag” requires the interval to be evaluated from the 

immediate previous vaccine dose administered and is used in the majority of cases.

• “From Target Dose # in Series” requires the interval to be evaluated from the date of the specified dose. 

• “From Most Recent Vaccine Type” requires the interval to be evaluated from the date of the most recently 

administered dose of any of the specific vaccine types listed (e.g., this is used in Pneumococcal to ensure 

proper spacing between the different intervals between PCV13 and PPSV23).

• “From Relevant Observation Code” requires the interval to be evaluated from the date of a particular 

patient observation (e.g. the interval for a dose of Pertussis vaccine is measured from the date of the 

onset of pregnancy).

It is possible for a given dose to use multiple preferable interval types. For example, dose 3 of HepB and dose 3 

of HPV, each have two preferable intervals. The first interval is from the immediate previous vaccine dose 

administered. The second interval is from satisfied target dose 1 in each respective antigen series. Note that if 

multiple intervals are specified, then all intervals must be satisfied in order for the dose to satisfy the interval 

requirements.

Figure 6-7 provides the evaluation interval timeline used to define adjacent intervals by using from immediate 

previous dose administered flag as the reference point. 

FIGURE 6-7 EVALUATE INTERVAL ‘FROM IMMEDIATE PREVIOUS DOSE ADMINISTERED FLAG’ TIMELINE

Figure 6-8 illustrates the evaluation interval timeline used to define non-adjacent intervals by using from target 

dose number in series as the reference point. This timeline is used only when from immediate previous dose 

administered flag is “N”.
Logic Specification for ACIP Recommendations v4.4 Page 56 of 151

FIGURE 6-8 EVALUATE INTERVAL 'FROM TARGET DOSE NUMBER IN SERIES' TIMELINE

Figure 6-9 illustrates the evaluation interval timeline used to define most recent vaccine intervals by using from 

most recent vaccine type as the reference point. 

FIGURE 6-9 EVALUATE INTERVAL 'FROM MOST RECENT VACCINE TYPE' TIMELINE

Figure 6-10 illustrates the evaluation interval timeline used to define most recent vaccine intervals by using from 

relevant observation code as the reference point. 
Logic Specification for ACIP Recommendations v4.4 Page 57 of 151

FIGURE 6-10 EVALUATE INTERVAL 'FROM RELEVANT OBSERVATION CODE' TIMELINE

The following process model, attribute table, decision table, and business rule table are used to evaluate 

preferable interval of a vaccine dose administered.

FIGURE 6-11 EVALUATE PREFERABLE INTERVAL PROCESS MODEL
Logic Specification for ACIP Recommendations v4.4 Page 58 of 151

TABLE 6-17 PREFERABLE INTERVAL ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Vaccine dose administered Date Administered -

Supporting Data Preferable Interval elements -

Calculated date (CALCDTINT-3) Absolute Minimum Interval Date 01/01/1900

Calculated date (CALCDTINT-4) Minimum Interval Date 01/01/1900

TABLE 6-18 DID THE VACCINE DOSE ADMINISTERED SATISFY THE PREFERABLE INTERVAL FOR THE TARGET 

DOSE?

CONDITIONS RULES

Is the date 

administered < 

absolute minimum 

interval date?

Yes No No No No

Is the absolute 

minimum interval date 

≤ date administered < 

minimum interval 

date?

No Yes Yes Yes No

Is the minimum 

interval date ≤ date 

administered?

No No No No Yes

Is this the first target 

dose?

- No No Yes -

Is the evaluation 

status of the previous 

vaccine dose 

administered 'not 

valid' due to age or 

interval 

recommendations 

and < 1 year from the 

vaccine dose 

administered being 

evaluated?

- Yes No - -

OUTCOMES No. The 

vaccine dose 

administered 

did not satisfy 

the preferable 

interval for the 

target dose. 

Evaluation 

reason is 'Too 

soon'.

No. The 

vaccine dose 

administered 

did not satisfy 

the preferable 

interval for the 

target dose. 

Evaluation 

reason is 'Too 

soon'.

Yes. The vaccine dose 

administered satisfied 

the preferable interval 

for the target dose. 

Evaluation reason is 

'Grace period'.

Yes. The vaccine dose 

administered satisfied 

the preferable interval 

for the target dose. 

Evaluation reason is 

'Grace period'.

Yes. The vaccine dose 

administered satisfied 

the preferable interval 

for the target dose.
Logic Specification for ACIP Recommendations v4.4 Page 59 of 151

TABLE 6-19 EVALUATE PREFERABLE INTERVAL BUSINESS RULES

Business Rule ID Business Rule

CALCDTINT-1 A patient's reference dose date for an interval must be calculated as the date administered of the most 

immediate previous vaccine dose administered if all the following are true:

• the interval has a from immediate previous dose administered flag of 'Y'

• the vaccine dose administered has an evaluation status of 'Valid' or 'Not Valid'

• the vaccine dose administered is not an inadvertent administration.

CALCDTINT-2 A patient's reference dose date for an interval must be calculated as the date administered of the vaccine 

dose administered that satisfies the target dose with the same target dose number as the from target dose 

number in series if all the following are true for the interval:

• from immediate previous dose administered flag is 'N'

• from target dose number in series is not 'N/A'.

CALCDTINT-3 A patient's absolute minimum interval date must be calculated as the patient's reference dose date plus the 

absolute minimum interval. 

CALCDTINT-4 A patient's minimum interval date must be calculated as the patient's reference dose date plus the minimum 

interval. 

CALCDTINT-8 A patient's reference dose date for an interval must be calculated as the date administered of the most recent 

vaccine dose administered that is the same vaccine type as the from most recent vaccine type if all the 

following are true for the interval:

• from immediate previous dose administered flag is 'N'

• from most recent vaccine type is not 'N/A'

• the vaccine dose administered is not an inadvertent administration.

CALCDTINT-9 A patient's reference dose date for an interval must be calculated as the observation date of the most recent 

active patient observation if all the following are true for the interval:

• from immediate previous dose administered flag is 'N'

• from relevant observation code is not 'N/A'.

6.6 EVALUATE ALLOWABLE INTERVAL 

Evaluate allowable interval validates the date administered of a vaccine dose administered against defined 

allowable interval(s) from previous vaccine dose(s) administered. In rare cases, intervals can be applied which 

are either abnormally early – usually specified in ACIP footnotes or subsequent clarifications – or intervals which 

differ following a not valid administration. 

In cases where a target dose does not specify allowable interval attributes, evaluate allowable interval cannot 

be used to validate a vaccine dose administered. To avoid a false validation, the allowable interval should be 

considered “not valid” in these cases.

The figure below provides evaluate allowable interval timeline used to define all adjacent intervals by using from 

immediate previous dose administered flag as the reference dose. 
Logic Specification for ACIP Recommendations v4.4 Page 60 of 151

FIGURE 6-12 EVALUATE ALLOWABLE INTERVAL 'FROM IMMEDIATE PREVIOUS DOSE ADMINISTERED FLAG' 

TIMELINE

The figure below illustrates evaluate allowable interval timeline used to define all non-adjacent intervals by using 

from target dose number in series as the reference dose.

FIGURE 6-13 EVALUATE ALLOWABLE INTERVAL 'FROM TARGET DOSE NUMBER IN SERIES' TIMELINE

The following process model, attribute table, decision table, and business rule table are used to evaluate interval 

of a vaccine dose administered.
Logic Specification for ACIP Recommendations v4.4 Page 61 of 151

FIGURE 6-14 EVALUATE ALLOWABLE INTERVAL PROCESS MODEL

TABLE 6-20 ALLOWABLE INTERVAL ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Vaccine dose administered Date Administered -

Supporting Data Allowable Interval elements -

Calculated date (CALCDTINT-3) Absolute Minimum Interval Date 01/01/1900

TABLE 6-21 DID THE VACCINE DOSE ADMINISTERED SATISFY THE ALLOWABLE INTERVAL FOR THE TARGET 

DOSE?

CONDITIONS RULES

Is the date administered < 

absolute minimum interval 

date?

Yes No 

OUTCOMES No. The vaccine dose administered did not satisfy the 

allowable interval for the target dose. Evaluation 

Reason is 'Too soon'.

Yes. The vaccine dose administered satisfied 

the allowable interval for the target dose.
Logic Specification for ACIP Recommendations v4.4 Page 62 of 151

TABLE 6-22 EVALUATE ALLOWABLE INTERVAL BUSINESS RULES

Business Rule ID Business Rule

CALCDTINT-1 A patient's reference dose date for an interval must be calculated as the date administered of the most 

immediate previous vaccine dose administered if all the following are true:

• The interval has a from immediate previous dose administered flag of 'Y'

• The vaccine dose administered has an evaluation status of 'Valid' or 'Not Valid'

• The vaccine dose administered is not an inadvertent administration.

CALCDTINT-2 A patient's reference dose date for an interval must be calculated as the date administered of the vaccine 

dose administered that satisfies the target dose with the same target dose number as the from target dose 

number in series if all the following are true for the interval:

• From immediate previous dose administered flag is 'N'

• From target dose number in series is not 'n/a'

CALCDTINT-3 A patient's absolute minimum interval date must be calculated as the patient's reference dose date plus the 

absolute minimum interval. 

6.7 EVALUATE VACCINE CONFLICT 

Evaluate vaccine conflict validates the date administered of a vaccine dose administered against previous 

administered vaccines to ensure proper spacing between administrations. This covers live virus vaccine conflicts

as well as non-live virus vaccine conflicts. Many vaccines do not have any conflict with each other. Therefore, if 

no vaccine Supporting Data exists for the vaccine type of the vaccine dose administered being evaluated, the 

vaccine dose administered is not in conflict with any other vaccine dose administered.

FIGURE 6-15 EVALUATE VACCINE CONFLICT TIMELINE

The following process model, attribute table, decision tables, and business rule table are used to evaluate for a 

vaccine conflict.
Logic Specification for ACIP Recommendations v4.4 Page 63 of 151

FIGURE 6-16 EVALUATE VACCINE CONFLICT PROCESS MODEL

TABLE 6-23 VACCINE CONFLICT ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Vaccine dose administered Date Administered -

Vaccine dose administered Vaccine Type -

Supporting Data Live Virus Conflicts -

Calculated date (CALCDTCONFLICT-1) Conflict Begin Interval Date -

Calculated date (CALCDTCONFLICT-2) Conflict End Interval Date -

Implementer Note

This section of the logic specification has been considerable refined in version 4.4 to be less process driven and more 

business rule based. The outcome should be functionally equivalent to previous versions of the logic specification. One 

important note is in the terminology used in the logic specification and the supporting data. In an effort to not break 

existing implementations who auto-consume the supporting data, we have not updated headings or XML tags. These will 

be migrated in 5.0 to use the same terms across the logic specification and the supporting data.

In the meantime, here are the terms in this version of the logic specification mapped to existing supporting data terms.

Logic Specification Term Supporting Data Term

Conflicting Vaccine Type Previous Vaccine Type

Impacted Vaccine Type Current Vaccine Type

 

TABLE 6-24 VACCINE CONFLICT BUSINESS RULES
Logic Specification for ACIP Recommendations v4.4 Page 64 of 151

Business Rule ID Business Rule

CALCDTCONFLICT-1 The conflict begin interval date for a previous vaccine dose administered must be calculated as the date 

administered of the previous vaccine dose administered plus the conflict begin interval of a vaccine type 

conflict where all the following are true:

• The vaccine type of the current vaccine dose administered is an impacted vaccine type. 

• The vaccine type of the previous vaccine dose administered is a conflicting vaccine type for the 

impacted vaccine type. 

CALCDTCONFLICT-2 The conflict end interval date for a previous vaccine dose administered must be calculated as one of the 

following:

• the date administered of the previous vaccine dose administered plus the minimum conflict end 

interval of a vaccine type conflict if all the following are true: 

o The vaccine type of the current vaccine dose administered is an impacted vaccine type. 

o The vaccine type of the previous vaccine dose administered is a conflicting vaccine type 

for the impacted vaccine type. 

o The previous vaccine dose administered has one of the following:

▪ an evaluation status of 'Valid'

▪ no evaluation status.

• the date administered of the previous vaccine dose administered plus the conflict end interval of a 

vaccine type conflict if all the following are true: 

o The vaccine type of the current vaccine dose administered is an impacted vaccine type. 

o The vaccine type of the previous vaccine dose administered is a conflicting vaccine type 

for the impacted vaccine type. 

o The previous vaccine dose administered has an evaluation status.

o The previous vaccine dose administered does not have an evaluation status of 'Valid'.

CONFLICT-3 A current vaccine dose administered must be considered an impacted vaccine dose administered if all the 

following are true for the date administered of the current vaccine dose administered: 

• It is on or after the conflict begin interval date.

• It is before the conflict end interval date.

6.8 EVALUATE FOR PREFERABLE VACCINE 

Evaluate for preferable vaccine validates the vaccine of a vaccine dose administered against the list of preferable 

vaccines.

Figure 6-17 depicts a patient who received a preferable vaccine while Figure 6-18 depicts a patient who did not 

receive a preferable vaccine.
Logic Specification for ACIP Recommendations v4.4 Page 65 of 151

FIGURE 6-17 PATIENT RECEIVED A PREFERABLE VACCINE

FIGURE 6-18 PATIENT DID NOT RECEIVE A PREFERABLE VACCINE

It should be noted that volume is sparsely populated and tracked differently in most systems. Therefore, volume 

will not be used to evaluate the validity of a vaccine dose administered. However, it will be provided as an 

evaluation reason that less than sufficient volume was administered.

The following process model, attribute table, decision table, and business rule table are used to evaluate for a 

preferable vaccine.
Logic Specification for ACIP Recommendations v4.4 Page 66 of 151

FIGURE 6-19 EVALUATE FOR A PREFERABLE VACCINE PROCESS MODEL

TABLE 6-25 PREFERABLE VACCINE ADMINISTERED ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Vaccine dose administered Date Administered -

Vaccine dose administered Volume -

Vaccine dose administered Trade Name -

Supporting Data Preferable Vaccine elements -

Calculated date (CALCDTPREF-1) Preferable Vaccine Type Begin Age Date 01/01/1900

Calculated date (CALCDTPREF-2) Preferable Vaccine Type End Age Date 12/31/2999

TABLE 6-26 WAS THE VACCINE DOSE ADMINISTERED A PREFERABLE VACCINE FOR THE TARGET DOSE?

CONDITIONS RULES

Is the vaccine type of 

the vaccine dose 

administered the 

same as the vaccine 

type of a preferable 

vaccine for the target 

dose?

Yes Yes No Yes Yes
Logic Specification for ACIP Recommendations v4.4 Page 67 of 151

CONDITIONS RULES

Is the preferable 

vaccine type begin 

age date ≤ date 

administered < 

preferable vaccine 

type end age date?

Yes Yes - No Yes

Is the trade name of 

the vaccine dose 

administered the 

same as the trade 

name of the 

preferable vaccine for 

the target dose?

Yes Yes - - No

Is the volume of the 

vaccine dose 

administered ≥ the 

volume of the 

preferable vaccine for 

the target dose?

Yes No - - -

OUTCOMES Yes. The vaccine 

dose 

administered was 

a preferable 

vaccine for the 

target dose.

Yes. The vaccine dose 

administered was a 

preferable vaccine for 

the target dose. 

Evaluation Reason is 

'Volume administered is 

less than recommended 

volume'.

No. The vaccine 

dose 

administered was 

not a preferable 

vaccine for the 

target dose.

No. The vaccine dose 

administered was not a 

preferable vaccine for 

the target dose. It was 

administered out of the 

recommended age 

range for the preferable 

vaccine.

No. The vaccine 

dose administered 

was not a 

preferable vaccine 

for the target dose.

The trade name of 

the vaccine dose 

administered is not 

the same as the 

trade name of the 

preferable vaccine.

TABLE 6-27 PREFERABLE VACCINE BUSINESS RULES

Business Rule ID Business Rule

CALCDTPREF-1 A patient's preferable vaccine type begin age date must be calculated as the patient's date of birth plus the 

vaccine type begin age of a preferable vaccine. 

CALCDTPREF-2 A patient's preferable vaccine type end age date must be calculated as the patient's date of birth plus the 

vaccine type end age of a preferable vaccine. 

6.9 EVALUATE FOR ALLOWABLE VACCINE 

Evaluate for allowable vaccine validates the vaccine of a vaccine dose administered against the list of allowable 

vaccines. 

Figure 6-20 depicts a patient who received an allowable vaccine while Figure 6-21 depicts a patient who did not 

receive an allowable vaccine.
Logic Specification for ACIP Recommendations v4.4 Page 68 of 151

FIGURE 6-20 PATIENT RECEIVED AN ALLOWABLE VACCINE

FIGURE 6-21 PATIENT DID NOT RECEIVE AN ALLOWABLE VACCINE

The following process model, attribute table, decision table, and business rule table are used to evaluate for an 

allowable vaccine.
Logic Specification for ACIP Recommendations v4.4 Page 69 of 151

FIGURE 6-22 EVALUATE FOR AN ALLOWABLE VACCINE PROCESS MODEL

TABLE 6-28 ALLOWABLE VACCINE ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Vaccine dose administered Date Administered -

Vaccine dose administered Vaccine Type -

Supporting data Allowable Vaccine elements -

Calculated date (CALCDTALLOW-1) Allowable Vaccine Type Begin Age Date 01/01/1900

Calculated date (CALCDTALLOW-2) Allowable Vaccine Type End Age Date 12/31/2999

TABLE 6-29 WAS THE VACCINE DOSE ADMNINSTERED AN ALLOWABLE VACCINE FOR THE TARGET DOSE?

CONDITIONS RULES

Is the vaccine type of the vaccine 

dose administered the same as 

the vaccine type of an allowable 

vaccine for the target dose?

Yes No Yes

Is the allowable vaccine type begin 

age date ≤ date administered < 

allowable vaccine type end age 

date?

Yes - No
Logic Specification for ACIP Recommendations v4.4 Page 70 of 151

CONDITIONS RULES

OUTCOMES Yes. The vaccine dose 

administered was an 

allowable vaccine for the 

target dose.

No. The vaccine dose 

administered was not an 

allowable vaccine for the 

target dose.

No. The vaccine dose administered was 

not an allowable vaccine for the target 

dose. It was administered out of the 

recommended age range for the allowable 

vaccine.

TABLE 6-30 ALLOWABLE VACCINE BUSINESS RULES

Business Rule ID Business Rule

CALCDTALLOW-1 A patient's allowable vaccine type begin age date must be calculated as the patient's date of birth plus the 

vaccine type begin age of an allowable vaccine. 

CALCDTALLOW-2 A patient's allowable vaccine type end age date must be calculated as the patient's date of birth plus the 

vaccine type end age of an allowable vaccine. 

6.10SATISFY TARGET DOSE 

Satisfy target dose uses the results from the previous evaluation sections as conditions to determine if the target 

dose is satisfied. 

The following processing model and decision table are used to determine if the target dose was satisfied.

FIGURE 6-23 SATISFY TARGET DOSE PROCESS MODEL

TABLE 6-31 WAS THE TARGET DOSE SATISFIED?

CONDITIONS RULES

Was the vaccine 

dose administered at 

a valid age for the 

target dose?

Yes Extraneous No - - -

Did the vaccine dose 

administered satisfy 

all preferable 

intervals or all 

allowable intervals 

for the target dose?

Yes - - No - -
Logic Specification for ACIP Recommendations v4.4 Page 71 of 151

CONDITIONS RULES

Is the current 

vaccine dose 

administered an 

impacted vaccine 

dose administered?

No - - - Yes -

Was the vaccine 

dose administered a 

preferable vaccine or 

an allowable vaccine 

for the target dose?

Yes - - - - No

OUTCOMES Yes. The target 

dose status is 

'Satisfied'. 

Evaluation 

status is 'Valid' 

with evaluation 

reasons.

No. The target 

dose status is 'Not 

Satisfied'. 

Evaluation status 

is 'Extraneous' 

with evaluation 

reasons.

No. The target 

dose status is 

'Not Satisfied'. 

Evaluation 

status is 'Not 

Valid' with 

evaluation 

reasons.

No. The target 

dose status is 

'Not Satisfied'. 

Evaluation 

status is 'Not 

Valid' with 

evaluation 

reasons.

No. The target 

dose status is 

'Not Satisfied'. 

Evaluation 

status is 'Not 

Valid' with 

evaluation 

reasons.

No. The target 

dose status is 

'Not Satisfied'. 

Evaluation 

status is 'Not 

Valid' with 

evaluation 

reasons.
Logic Specification for ACIP Recommendations v4.4 Page 72 of 151

7 FORECAST DATES AND REASONS 

A CDS engine uses a patient’s medical and vaccine history to forecast immunization due dates for a relevant 

patient series. It is also possible the patient would not be recommended additional doses. In that case, the 

outcome of the forecast will be an appropriate status (e.g., complete, immune, contraindicated) and no dates will 

be generated. This chapter identifies specific business rules that are used by a CDS engine to forecast the next 

target dose. The major steps involved in this process are listed in the table below.

TABLE 7-1 FORECAST DATES AND REASONS PROCESS STEPS

Section Activity Goal

7.1 Evaluate Conditional Skip The goal of this step is to determine if the target dose can be skipped due to a 

patient’s age at assessment or immunization history.

7.2 Determine Evidence Of Immunity The goal of this step is to determine if the patient has evidence of immunity.

7.3 Determine Contraindications The goal of this step is to determine if any patient series are contraindicated.

7.4 Determine Forecast Need The goal of this step is to determine if the patient should receive another dose.

7.5 Generate Forecast Dates And 

Recommended Vaccines

The goal of this step is to generate forecast dates for the next target dose.

7.6 Validate Recommendation The goal of this step is to ensure the generated forecast will be logical when the 

patient arrives for their next visit (e.g., will they no longer need the dose).

The next figure provides an illustration of the forecast dates and reasons process.

 
Logic Specification for ACIP Recommendations v4.4 Page 73 of 151

FIGURE 7-1 FORECAST DATES AND REASON PROCESS MODEL

7.1 EVALUATE CONDITIONAL SKIP

Evaluate Conditional Skip addresses times when a target dose can be skipped. A dose should be considered 

necessary unless it is determined that it can be skipped. The most common scenarios for skipping a dose are:

• Catch-up doses where the patient is current with their administrations and does not need to catch-up

• The patient is behind schedule and the total number of doses needed to satisfy the patient series can be 

reduced

• The previously administered dose(s) negates the need for the current target dose
Logic Specification for ACIP Recommendations v4.4 Page 74 of 151

Only Conditional Skip Instances with a context of Forecast or Both should be used. In cases where a target dose 

does not specify Conditional Skip attributes, the target dose cannot be skipped.

The process model, attribute table, business rules, and decision tables are used to determine if the target dose 

can be skipped is the same as described in Chapter 6.2.

7.2 DETERMINE EVIDENCE OF IMMUNITY 

Determine evidence of immunity assesses the patient’s profile to determine if the patient is already potentially 

immune to the target disease, negating the need for additional doses. A patient may be considered immune due 

to their clinical history or if they were born before a defined date for the given target disease. For example, for 

measles, a patient is considered immune if they have a clinical finding of “Measles immune” or if they were born 

before 01/01/1957. Additional patient attributes, such as occupation or pregnancy status, may supersede the 

birth date logic.

FIGURE 7-2 EVIDENCE OF IMMUNITY PROCESS MODEL

TABLE 7-2 IMMUNITY ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Patient Date of Birth -

Patient Country of Birth -

Patient Evidence of Immunity

Supporting Data Immunity elements -

TABLE 7-3 DOES THE PATIENT HAVE EVIDENCE OF IMMUNITY?

CONDITIONS RULES

Does the patient history 

contain one of the immunity 

guidelines?

Yes No No No No

Is the patient's date of birth 

< the immunity birth date?

- Yes Yes Yes No

Does this patient have an 

immunity exclusion 

condition?

- Yes No No - 

Is the patient's country of 

birth the same as the 

immunity country of birth?

- - Yes No -
Logic Specification for ACIP Recommendations v4.4 Page 75 of 151

CONDITIONS RULES

OUTCOMES Yes. The patient 

has evidence of 

immunity.

No. The patient 

does not have 

evidence of 

immunity.

Yes. The patient 

has evidence of 

immunity.

No. The patient 

does not have 

evidence of 

immunity.

No. The patient 

does not have 

evidence of 

immunity.

7.3 DETERMINE CONTRAINDICATIONS 

Determine contraindications assesses if any or all series for an antigen are contraindicated for the patient. 

Contraindications may be applied at either the antigen or vaccine level. 

Given the complex nature of contraindications, it may not always be possible to conclusively determine if a 

contraindication applies to a patient. To minimize missed doses, in the case where a contraindication cannot be 

definitively determined to be relevant for a patient, the contraindication will not be applied, but a notification 

should be made to a clinician alerting them to the presence of the possible contraindication which could not be 

resolved.

The following process model is used to assess contraindications.

FIGURE 7-3 CONTRAINDICATION PROCESS MODEL

TABLE 7-4 DETERMINE CONTRAINDICATION ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Patient Active Patient Observations -

Patient Adverse Reactions -

Supporting Data Contraindication elements -

Processing data Assessment Date current date

Calculated date (CALCDTCI-1) Contraindication Begin Age Date 01/01/1900

Calculated date (CALCDTCI-2) Contraindication End Age Date 12/31/2999

An antigen contraindication prevents all relevant patient series for that antigen from recommending further 

vaccination for the patient. That is, no relevant patient series for the antigen should forecast dates for the patient.

The patient series status will be contraindicated for each relevant patient series.
Logic Specification for ACIP Recommendations v4.4 Page 76 of 151

FIGURE 7-4 ANTIGEN CONTRAINDICATION PROCESS MODEL

TABLE 7-5 DOES THE ANTIGEN CONTRAINDICATION APPLY TO THE PATIENT?

CONDITIONS RULES

Does the antigen 

contraindication 

describe any active 

patient 

observations?

Yes No No Unknown -

Does the antigen 

contraindication 

describe any 

adverse reactions?

No Yes No Unknown -

Is the 

contraindication 

begin age date ≤ 

assessment date < 

contraindication end 

age date?

Yes Yes Yes Yes No
Logic Specification for ACIP Recommendations v4.4 Page 77 of 151

CONDITIONS RULES

OUTCOMES Yes. The antigen 

contraindication 

applies to the 

patient.

Yes. The antigen 

contraindication 

applies to the 

patient.

No. The antigen 

contraindication 

does not apply to 

the patient.

No. It could not be 

determined if the antigen 

contraindication applies to 

the patient; however, the 

Contraindication Text 

Description should be made 

available to the clinician for 

manual determination.

No. The antigen 

contraindication 

does not apply to 

the patient.

A vaccine contraindication eliminates a specific vaccine from being forecast for the patient. 

FIGURE 7-5 VACCINE CONTRAINDICATION PROCESS MODEL

TABLE 7-6 DOES THE VACCINE CONTRAINDICATION APPLY TO THE PATIENT?
Logic Specification for ACIP Recommendations v4.4 Page 78 of 151

CONDITIONS RULES

Does the vaccine 

contraindication 

describe any active 

patient observations?

Yes Yes No No No - Unknown

Does the vaccine 

contraindication 

describe any adverse 

reactions?

No No Yes Yes No - Unknown

Is the 

contraindication 

begin age date ≤ 

assessment date < 

contraindication end 

age date?

Yes Yes Yes Yes - No Yes

Is the vaccine type of 

the preferable 

vaccine one of the 

contraindicated vacci

ne types for the 

contraindication?

Yes No Yes No - - Yes

OUTCOMES Yes. The 

vaccine 

contraindicati

on applies to 

the patient.

No. The 

vaccine 

contraindicati

on does not 

apply to the 

patient. 

Yes. The 

vaccine 

contraindicati

on applies to 

the patient.

No. The 

vaccine 

contraindicati

on does not 

apply to the 

patient.

No. The 

vaccine 

contraindicati

on does not 

apply to the 

patient. 

No. The 

vaccine 

contraindicati

on does not 

apply to the 

patient.

No. It could 

not be 

determined if 

the vaccine 

contraindicatio

n applies to 

the patient; 

however, the 

Contraindicati

on Text 

Description 

should be 

made 

available to 

the clinician 

for manual 

determination.

A relevant patient series should not be forecast if either an antigen contraindication exists, or if all preferable 

vaccines are contraindicated.

TABLE 7-7 IS THE RELEVANT PATIENT SERIES A CONTRAINDICATED PATIENT SERIES?

CONDITIONS RULES

Are there any antigen contraindication that 

apply to the patient?

Yes No No
Logic Specification for ACIP Recommendations v4.4 Page 79 of 151

CONDITIONS RULES

Do all preferable vaccines for the relevant 

patient series have at least one vaccine 

contraindication that applies to the patient?

- Yes No

OUTCOMES Yes. The relevant patient 

series is a contraindicated 

patient series.

Yes. The relevant patient 

series is a contraindicated 

patient series.

No. The relevant patient 

series is not a 

contraindicated patient 

series.

TABLE 7-8 DETERMINE CONTRAINDICATION BUSINESS RULES

Business Rule ID Business Rule

CALCDTCI-1 A patient's contraindication begin age date must be calculated as the patient's date of birth plus the 

contraindication begin age of a contraindication. 

CALCDTCI-2 A patient's contraindication end age date must be calculated as the patient's date of birth plus the 

contraindication end age of a contraindication. 

7.4 DETERMINE FORECAST NEED 

Determine forecast need determines if there is a need to forecast dates. This involves reviewing patient data, 

antigen administered records, and patient series. This is a prerequisite before a CDS engine can produce 

forecast dates and reasons.

The following process model, attribute table, and decision table are used to determine the need to generate 

forecast dates.

FIGURE 7-6 DETERMINE FORECAST NEED PROCESS MODEL

TABLE 7-9 DETERMINE FORECAST NEED ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Immunization history Vaccine Dose(s) Administered -

Relevant Patient series Target Dose Statuses -

Supporting Data (Seasonal Recommendation) Seasonal Recommendation End Date 12/31/2999

Section 7.2 Outcome Evidence of Immunity No evidence of immunity

Section 7.3 Outcome Contraindicated Patient Series The relevant patient series is 

NOT a contraindicated patient 

series.
Logic Specification for ACIP Recommendations v4.4 Page 80 of 151

Attribute Type Attribute Name Assumed Value if Empty

Runtime data Assessment Date current date

Calculated date (CALCDTAGE-1) Maximum Age Date 12/31/2999

Calculated date (FORECASTDTCAN-1) Candidate Earliest Date 12/31/2999

TABLE 7-10 SHOULD THE PATIENT RECEIVE ANOTHER TARGET DOSE?

CONDITIONS RULES

Does the patient 

have at least one 

target dose with 

a target dose 

status of 'Not 

Satisfied'?

Yes No No - - - - -

Does the patient 

have at least one 

target dose with 

a target dose 

status of 

'Satisfied'?

- Yes No - - - - -

Does the patient 

have evidence of 

immunity?

No - - Yes - - - -

Is the relevant 

patient series a 

contraindicated 

patient series?

No - - - Yes - - -

Is the 

assessment date 

≤ the seasonal 

recommendation 

end date?

Yes - - - - No - -

Is the 

assessment date 

< the maximum 

age date?

Yes - - - - - No -

Is the candidate 

earliest date < 

the maximum 

age date?

Yes - - - - - - No
Logic Specification for ACIP Recommendations v4.4 Page 81 of 151

CONDITIONS RULES

OUTCOMES Yes. The 

patient 

should 

receive 

another 

dose.

Patient 

Series 

Status is 

'Not 

Complete'

No. The 

patient 

should not 

receive 

another 

dose.

Patient 

Series 

Status is 

'Complete'

Forecast 

reason is 

'Patient 

series is 

complete'.

No. The patient 

should not 

receive another 

dose.

Patient series 

status is 'Not 

Recommended'

Forecast reason 

is 'Not 

recommended 

at this time due 

to past 

immunization 

history'

No. The 

patient 

should not 

receive 

another 

dose. 

Patient 

Series 

Status is 

'Immune'

Forecast 

reason is 

'Patient 

has 

evidence 

of 

immunity'.

No. The patient 

should not 

receive another 

dose.

Patient Series 

Status is 

'Contraindicated'

Forecast reason 

is 'Patient has a 

contraindication'.

No. The patient 

should not 

receive another 

dose. 

Patient Series 

Status is 'Not 

Recommended' 

Forecast reason 

is 'Past seasonal 

recommendation 

end date'.

No. The 

patient 

should 

not 

receive 

another 

dose. 

Patient 

Series 

Status is 

'Aged Out'

Forecast 

reason is 

'Patient 

has 

exceeded 

the 

maximum 

age'

No. The 

patient 

should not 

receive 

another 

dose. 

Patient 

Series 

Status is 

'Aged Out' 

Forecast 

reason is 

'Patient is 

unable to 

finish the 

series 

prior to 

the 

maximum 

age'. 

TABLE 7-11 DETERMINE FORECAST NEED BUSINESS RULES

Business Rule ID Business Rule

CALCDTAGE-1 A patient's maximum age date must be calculated as the patient's date of birth plus the maximum age. 

FORECASTDTCAN-1 The candidate earliest date of a patient series forecast must be the latest of the following dates:

• Minimum age date

• Latest of all minimum interval dates

• Latest of all forecast conflict end dates

• Seasonal recommendation start date

• Latest of all dates administered of any inadvertent administration being evaluated against a target 

dose that is part of a patient series that is the basis of the patient series forecast 

• Date administered of the most recent vaccine dose administered being evaluated against a target 

dose that is part of a patient series that is the basis of the patient series forecast. 

7.5 GENERATE FORECAST DATES AND RECOMMENDED VACCINES

Generate forecast dates and recommend vaccines determines the forecast dates for the next target dose and 

identifies one or more recommended vaccines if the target dose warrants specific vaccine recommendations. 

Additional detail, such as administrative guidance for providers may also be included. The forecast dates are 

generated based on the patient’s immunization history. If the patient has not adhered to the preferred schedule, 

then the forecast dates are adjusted to provide the best dates for the next target dose. Figure 7-7 below provides 

an illustration of how forecast dates appear on the timeline.
Logic Specification for ACIP Recommendations v4.4 Page 82 of 151

FIGURE 7-7 FORECAST DATES TIMELINE

The following process model, attribute table, and business rule table are used to generate forecast dates. If an 

attribute value is empty, then the date calculations will remain empty. No assumptions will be made for the 

attribute.

FIGURE 7-8 GENERATE FORECAST DATES AND RECOMMENDED VACCINE PROCESS MODEL

TABLE 7-12 GENERATE FORECAST DATE AND RECOMMENDED VACCINE ATTRIBUTES

Attribute Type Attribute Name Assumed Value if Empty

Calculated date (CALCDTAGE-4) Minimum Age Date -

Calculated date (CALCDTAGE-3) Earliest Recommended Age Date -

Calculated date (CALCDTAGE-2) Latest Recommended Age Date -

Calculated date (CALCDTAGE-1) Maximum Age Date -

Calculated date (CALCDTINT-4) Minimum Interval Date(s) -

Calculated date (CALCDTINT-5) Earliest Recommended Interval Date(s) -

Calculated date (CALCDTINT-6) Latest Recommended Interval Date(s) -

Calculated date (CALCDTLIVE-4) Latest Conflict End Interval Date -

Supporting Data (Seasonal 

Recommendation)

Seasonal Recommendation Start Date 01/01/1900

Supporting Data (Preferable Vaccine) Vaccine Type (CVX) -

Supporting Data (Preferable Vaccine) Forecast Vaccine Type Flag N
Logic Specification for ACIP Recommendations v4.4 Page 83 of 151

TABLE 7-13 GENERATE FORECAST DATE AND RECOMMENDED VACCINE BUSINESS RULES

Business Rule ID Business Rule

FORECASTDT-1 The earliest date of a patient series forecast made from a relevant patient series must be the candidate 

earliest date. 

FORECASTDT-2 The unadjusted recommended date of a patient series forecast must be one of the following:

• The earliest recommended age date.

• The latest of all earliest recommended interval dates if there is no earliest recommended age 

date. 

• The earliest date of the patient series forecast if there is no earliest recommended age date or 

earliest recommended interval date. 

FORECASTDT-3 The unadjusted past due date of a patient series forecast must be one of the following:

• The latest recommended age date minus 1 day.

• The latest of all latest recommended interval dates minus 1 day if there is no latest recommended 

age date. 

• Blank if there is no latest recommended age date or latest recommended interval date.

FORECASTDT-4 The latest date of a patient series forecast must be one of the following:

• The maximum age date minus 1 day if there is a maximum age date.

• Blank if there is no maximum age date.

FORECASTDT-5 The adjusted recommended date of a patient series forecast must be one of the following:

• The earliest date of the patient series forecast.

• The unadjusted recommended date of the patient series forecast if it is after the earliest date. 

FORECASTDT-6 The adjusted past due date of a patient series forecast must be one of the following:

• The later of the earliest date of the patient series forecast and the unadjusted past due date of the 

patient series forecast if there is an unadjusted past due date. 

• Blank if there is no unadjusted past due date.

FORECASTGUIDANCE1 Administrative guidance included in a forecast made for a patient must include all the following:

• Administrative guidance pertaining to any antigen series that defines the regimen for a 

recommended antigen.

• Administrative guidance pertaining to any indication for which there is an active patient 

observation for the patient.

• Administrative guidance pertaining to any contraindication for which there is an active patient 

observation for the patient. 

FORECASTRECVAC-1 A series dose vaccine must be considered a recommended series dose vaccine for a patient series forecast 

if all the following are true:

• The series dose vaccine is a preferable vaccine.

• The forecast vaccine type flag of the series dose vaccine is 'Y'

• There is no vaccine contraindication involving the vaccine type that is used for the series dose 

vaccine. 

• At least one of the following is true: 

o The earliest date of the patient series forecast is on or after the preferable vaccine type 

begin age date and before the preferable vaccine type end age date of the series dose 

vaccine. 

o The adjusted recommended date of the patient series forecast is on or after the 

preferable vaccine type begin age date and before the preferable vaccine type end age 

date of the series dose vaccine. 
Logic Specification for ACIP Recommendations v4.4 Page 84 of 151

Business Rule ID Business Rule

FORECASTDN-1 The forecast dose number for a patient series forecast must be calculated as one of the following:

• The count of all target doses plus 1 where all the following are true:

o The target dose is part of the relevant patient series.

o The target dose has a target dose status of 'Satisfied.'

o There is no seasonal recommendation start date for the series dose that is tracked by 

the target dose. 

• The count of all target doses plus 1 where all the following are true:

o The target dose is part of the relevant patient series.

o The target dose has a target dose status of 'Satisfied.'

o There is a seasonal recommendation start date for the series dose that is tracked by the 

target dose. 

o The date administered of the vaccine dose administered evaluated by the target dose is 

on or after the seasonal recommendation start date. 

7.6 VALIDATE RECOMMENDATION

Validation Recommendation interrogates the forecasted earliest date to ensure the forecast makes logical sense. 

Conditional Skip is used to determine if a forecast is illogical and thus in need of a complete re-forecasting. 

The forecasted dates are beyond the conditional skip requirements of the target dose being forecasted 

resulting in a different forecast when the patient returns for vaccination. To prevent erroneous 

recommendations, this section prospectively ensures the recommendation remains valid at the earliest date. If 

the recommendation is found to be invalid, re-forecasting for the next target dose is required.

• For example, a patient is behind on Hib and has just received a first dose at 11 months and 1 week of 

age. The patient is then recommended for a catch-up dose in four weeks, shortly after the 12-month 

mark. However, upon returning to the provider office four weeks later, the freshly updated forecast skips 

the previously forecasted target dose and now forecasts the patient return 8 weeks after the previous 

dose.

FIGURE 7-9 VALIDATE RECOMMENDED DOSE PROCESS MODEL

7.6.1 Conditional Skip

The process model, attribute table, and decision table are used to determine if the target dose can be skipped 

is the same as described in Chapter 6.2. Only Conditional Skip Instances with a context of Forecast or Both 

should be used. In cases where a target dose does not specify Conditional Skip attributes, the target dose cannot 

be skipped. In CONDSKIP-2, the Earliest Date is used.
Logic Specification for ACIP Recommendations v4.4 Page 85 of 151

8 SELECT BEST PATIENT SERIES 

Select Best Patient Series involves reviewing all potential patient series which might satisfy the goals of an 

antigen and determining the one or more revelant patient series which best fits the patient needs based on 

several important factors. 

The basic steps of this process are listed in table 8-1. Process steps 8.1 through 8.7 are repeated for each series 

group to identify one prioritized patient series per series group. Process step 8.8 is then used to determing which 

prioritized patient series are selected as a best patient series. In some cases, the prioritized patient series from 

one series group may negate the need for the prioritized patient series from another equivalent series group. 

However, in other cases, multiple prioritized patient series may be needed to fully protect the patient.

TABLE 8-1 SELECT BEST PATIENT SERIES PROCESS STEPS

Section Activity Goal

8.1 Pre-Filter Patient Series The goal of this step is to determine which relevant patient series should be 

excluded and which should be considered a scorable patient series.

8.2 Identify One Prioritized Patient Series The goal of this step is to determine if one scorable patient series is superior to 

the other scorable patient series for each series group.

8.3 Classify Scorable Patient Series The goal of this step is to classify where the patient is in the overall path to 

immunity and pass those scorable patient series on to the next step. Only those 

scorable patient series with the most likely chance to be considered the best 

are retained for scoring.

8.4 Complete Patient Series The goal of this step is to apply the proper scoring business rules based on 

results of the Classify Scorable Patient Series step. The scoring business rules 

will determine the prioritized patient series. Scoring business rules are specific 

to where the patient is in the overall path to immunity. The complete patient 

series scoring business rules look at factors important when scorable patient 

series are complete. Similarly, in-process patient series scoring business rules 

and no valid doses scoring business rules look at factors important to their 

respective situation. For any given Series Group, only one set of these scoring 

business rules will be applied to each scorable patient series as determined by 

Classify Scorable Patient Series.

8.5 In-Process Patient Series The goal of this step is to apply the proper scoring business rules based on 

results of the Classify Scorable Patient Series step. The scoring business rules 

will determine the prioritized patient series. Scoring business rules are specific 

to where the patient is in the overall path to immunity. The complete patient 

series scoring business rules look at factors important when scorable patient 

series are complete. Similarly, in-process patient series scoring business rules 

and no valid doses scoring business rules look at factors important to their 

respective situation. For any given Series Group, only one set of these scoring 

business rules will be applied to each scorable patient series as determined by 

the Classify Scorable Patient Series.
Logic Specification for ACIP Recommendations v4.4 Page 86 of 151

Section Activity Goal

8.6 No Valid Doses The goal of this step is to apply the proper scoring business rules based on 

results of the Classify Scorable Patient Series step. The scoring business rules 

will determine the prioritized patient series. Scoring business rules are specific 

to where the patient is in the overall path to immunity. The complete patient 

series scoring business rules look at factors important when scorable patient 

series are complete. Similarly, in-process patient series scoring business rules 

and no valid doses scoring business rules look at factors important to their 

respective situation. For any given Series Group, only one set of these scoring 

business rules will be applied to each scorable patient series as determined by 

the Classify Scorable Patient Series.

8.7 Select Prioritized Patient Series The goal of this step is to evaluate the scored patient series and determine 

which of the scorable patient series is the one and only prioritized patient series 

for the series group.

8.8 Determine Best Patient Series The goal of this step is to identify a final set of best patient series that apply to 

the patient.

The process model below illustrates the major steps involved in selecting the best patient series. 

FIGURE 8-1 SELECT BEST PATIENT SERIES PROCESS MODEL
Logic Specification for ACIP Recommendations v4.4 Page 87 of 151

The process model below illustrates for a Series Group the major steps involved in selecting the prioritized 

patient series. 
Logic Specification for ACIP Recommendations v4.4 Page 88 of 151

FIGURE 8-2 SELECT PRIORITIZED PATIENT SERIES PROCESS MODEL

8.1 PRE-FILTER PATIENT SERIES

Pre-filter patient series examines each of the patient series for a given Series Group to determine if any series 

should be removed from consideration for best patient series. If a Series Group contains relevant patient series 

of different priorities, only the set of highest priority patient series should be considered when determining the 

best patient series for the Series Group.

TABLE 8-2 PRE-FILTER PATIENT SERIES BUSINESS RULES

Business Rule ID Business Rule

SELECTB-24 A relevant patient series that is the basis of a patient series forecast must be considered a candidate scorable 

patient series if one of the following is true:

• The patient series forecast does not have a patient series status of 'Contraindicated.'

• All the following are true:

o The patient series forecast has a patient series status of 'Contraindicated.'

o Each relevant patient series that is the basis of a patient series forecast that is made for 

the same series group has a patient series status of 'Contraindicated' 

SELECTSCORE-2 A relevant patient series must be considered a scorable patient series if one of the following is true:

• All the following are true for the relevant patient series:

o The relevant patient series tracks an antigen series with a series type of 'Risk.'

o The series priority of the antigen series tracked by the relevant patient series is the same 

as or greater than the series priority of any relevant patient series that tracks an antigen 

series that belongs to the same series group as the relevant patient series. 

o It is a candidate scorable patient series.

• All the following are true for the relevant patient series:

o The relevant patient series tracks an antigen series with a series type of 'Standard.'

o The relevant patient series includes a target dose evaluating at least one vaccine dose 

administered with an evaluation status of 'Valid'. 

o The earliest vaccine dose administered with an evaluation status of 'Valid' associated 

with the relevant patient series has a date administered before the maximum age to start 

date. 

o It is a candidate scorable patient series.

• All the following are true for the relevant patient series:

o The relevant patient series tracks an antigen series with a series type of 'Standard'

o The number of valid doses is 0 for each relevant patient series in the series group. 

o There is no default patient series for the series group.

o It is a candidate scorable patient series.

• All the following are true for the relevant patient series:

o The relevant patient series tracks an antigen series with a series type of 'Evaluation 

Only' 

o The relevant patient series is a complete patient series.

8.2 IDENTIFY ONE PRIORITIZED PATIENT SERIES

Identify one prioritized patient series examines all of the patient series for a given Series Group to determine if 

one of the patient series is superior to all other patient series and can be considered the prioritized patient series.

TABLE 8-3 IS THERE A SINGLE PRIORITIZED PATIENT SERIES IN A SERIES GROUP?
Logic Specification for ACIP Recommendations v4.4 Page 89 of 151

CONDITIONS RULES

How many scorable patient series 

are in the series group?

0 1 >1 >1 >1

How many default patient series are 

in the series group?

1 - - - 1

How many complete patient series 

are in the series group?

- - 1 0 0

How many in-process patient series 

are in the series group?

- - - 1 0

OUTCOMES

Default: No. There is no single 

prioritized patient series. More than 

one scorable patient series has 

potential. All scorable patient series 

are examined to see which should be 

scored and selected as the prioritized 

patient series for the series group.

Yes. The single 

default patient 

series is the 

prioritized patient 

series for the 

series group.

Yes. The single 

scorable patient 

series is the 

prioritized patient 

series for the 

series group.

Yes. The single 

complete patient 

series is the 

prioritized patient 

series for the 

series group.

Yes. The single 

in-process patient 

series is the 

prioritized patient 

series for the 

series group.

Yes. The default 

patient series is 

the prioritized 

patient series for 

the series group.

TABLE 8-4 IDENTIFY ONE PRIORITIZED PATIENT SERIES BUSINESS RULES

Business Rule ID Business Rule

SELECTB-6 A scorable patient series must be considered a complete patient series if the patient series forecast made 

from the scorable patient series has a patient series status of 'Complete'. 

SELECTB-7 A relevant patient series must be considered a default patient series if the default series flag is 'Y' for the 

antigen series. 

SELECTB-16 A scorable patient series must be considered an in-process patient series if all the following are true:

• The scorable patient series includes at least one target dose with a target dose status of 

'Satisfied.' 

• The patient series forecast made from the scorable patient series has a patient series status of 

'Not Complete'.

8.3 CLASSIFY SCORABLE PATIENT SERIES 

Classify scorable patient series is an attempt to reduce the total number of patient series within a Series Group 

to only those which have a chance to be selected as the prioritized patient series.

TABLE 8-5 WHICH SCORABLE PATIENT SERIES SHOULD BE SCORED?

CONDITIONS RULES

Are there 2 or more 

complete patient 

series in the series 

group?

Yes No No
Logic Specification for ACIP Recommendations v4.4 Page 90 of 151

CONDITIONS RULES

Are there 2 or more 

in-process patient 

series and no 

complete patient 

series in the series 

group?

- Yes No

Is the number of 

valid doses = 0 for all 

scorable patient 

series in the series 

group?

- No Yes

OUTCOMES All complete patient series in the 

series group should be scored. 

Apply the complete patient series 

scoring business rules to these 

scorable patient series only. All 

other patient series in the series 

group are not scored and are 

dropped from consideration.

All in-process patient series in the 

series group should be scored. 

Apply the in-process patient series 

scoring business rules to these 

scorable patient series only. All 

other patient series in the series 

group are not scored and are 

dropped from consideration.

All scorable patient series in the 

series group with the number of valid 

doses = 0 should be scored. Apply 

the no valid doses scoring business 

rules to all scorable patient series in 

the series group. All other patient 

series in the series group are not 

scored and are dropped from 

consideration.

TABLE 8-6 CLASSIFY SCORABLE PATIENT SERIES BUSINESS RULES

Business Rule ID Business Rule

SELECTB-6 A scorable patient series must be considered a complete patient series if the patient series forecast made 

from the scorable patient series has a patient series status of 'Complete'. 

SELECTB-16 A scorable patient series must be considered an in-process patient series if all the following are true:

• The scorable patient series includes at least one target dose with a target dose status of 

'Satisfied.' 

• The patient series forecast made from the scorable patient series has a patient series status of 

'Not Complete'. 

SELECTB-21 The number of valid doses for a scorable patient series must be calculated as the count of the target doses 

included in the scorable patient series with a target dose status of 'Satisfied'. 

8.4 COMPLETE PATIENT SERIES

Complete patient series provides the decision table for determining the number of points to assign to a complete 

patient series based on a specified condition. 

TABLE 8-7 HOW MANY POINTS ARE AWARDED TO A SCORABLE PATIENT SERIES THAT IS A COMPLETE 

PATIENT SERIES?

Conditions If this condition is true for the 

scorable patient series

If this condition is true for two or 

more scorable patient series

If this condition is not true for 

the scorable patient series

A scorable patient series has 

the most valid doses.

+1 0 -1
Logic Specification for ACIP Recommendations v4.4 Page 91 of 151

TABLE 8-8 COMPLETE PATIENT SERIES BUSINESS RULES

Business Rule ID Business Rule

SELECTB-6 A scorable patient series must be considered a complete patient series if the patient series forecast made 

from the scorable patient series has a patient series status of 'Complete'. 

SELECTB-19 A scorable patient series has the most valid doses if the number of valid doses is the same as or greater 

than the number of valid doses in all other scorable patient series. 

8.5 IN-PROCESS PATIENT SERIES 

In-process patient series provides the decision table for determining the number of points to assign to an inprocess patient series based on a specified condition.

TABLE 8-9 HOW MANY POINTS ARE AWARDED TO SCORABLE PATIENT SERIES THAT IS AN IN-PROCESS 

PATIENT SERIES?

Conditions If this condition is true for 

the scorable patient series

If this condition is true for 

two or more scorable 

patient series

If this condition is not true 

for the scorable patient 

series

A scorable patient series is a product 

patient series and has all valid doses.

+2 n/a -2

A scorable patient series is 

completable.

+3 n/a -3

A scorable patient series has the 

most valid doses.

+2 0 -2

A scorable patient series is closest to 

completion.

+2 0 -2

A scorable patient series can finish 

earliest.

+1 0 -1

TABLE 8-10 IN-PROCESS PATIENT SERIES BUSINESS RULES

Business Rule ID Business Rule

SELECTB-2 A scorable patient series has all valid doses only if all evaluations based on the target doses that are part of 

the scorable patient series have an evaluation status of 'Valid'. 

SELECTB-3 A patient series must be considered completable if the forecast finish date is less than the maximum age 

date of the last target dose. 

SELECTB-5 A patient series must be the considered the closest to completion if the number of not satisfied target doses 

is less than the number of not satisfied target doses in all other patient series. 

SELECTB-11 A patient series can finish earliest if the patient series is completable and the forecast finish date is on or 

before the forecast finish date in all other completable patient series. 

SELECTB-12 The forecast finish date for a scorable patient series must be calculated as the earliest date of the patient 

series forecast made from the scorable patient series plus the latest minimum interval from the remaining 

target dose(s).
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Business Rule ID Business Rule

SELECTB-16 A scorable patient series must be considered an in-process patient series if all the following are true:

• The scorable patient series includes at least one target dose with a target dose status of 

'Satisfied.' 

• The patient series forecast made from the scorable patient series has a patient series status of 

'Not Complete'.

SELECTB-19 A scorable patient series has the most valid doses if the number of valid doses is the same as or greater 

than the number of valid doses in all other scorable patient series. 

SELECTB-23 A patient series must be considered a product patient series if the product path flag is 'Y' for the select patient 

series. 

8.6 NO VALID DOSES

No valid doses provide the decision table for determining the number of points to assign to a scorable patient 

series when there are no valid doses.

TABLE 8-11 HOW MANY POINTS ARE AWARDED TO A SCORABLE PATIENT SERIES THAT HAS NO VALID 

DOSES?

Conditions If this condition is true for 

the scorable patient series

If this condition is true for two or 

more scorable patient series

If this condition is not true for 

the scorable patient series

A scorable patient series can 

start earliest.

+1 0 -1

A scorable patient series is 

completable.

+1 n/a -1

A scorable patient series is a 

product patient series.

-1 n/a +1

TABLE 8-12 NO VALID DOSES BUSINESS RULES

Business Rule ID Business Rule

SELECTB-3 A patient series must be considered completable if the forecast finish date is less than the maximum age 

date of the last target dose. 

SELECTB-23 A patient series must be considered a product patient series if the product path flag is 'Y' for the select patient 

series. 

SELECTB-12 The forecast finish date for a scorable patient series must be calculated as the earliest date of the patient 

series forecast made from the scorable patient series plus the latest minimum interval from the remaining 

target dose(s). 

SELECTB-14 A patient series must be considered start earliest if the start date is before the start date for all other patient 

series with a start date. 

8.7 SELECT PRIORITIZED PATIENT SERIES

Select prioritized patient series provides the business rules to be applied to the scored patient series which will 

result in the prioritized patient series for the series group.
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TABLE 8-13 SELECT PRIORITIZED PATIENT SERIES BUSINESS RULES

Business Rule ID Business Rule

SELECTBEST-1 The scorable patient series score must be the sum of all points awarded to the scorable patient series. 

SELECTBEST-2 The prioritized patient series must be one of the following:

• The scorable patient series with the highest scorable patient series score.

• The scorable patient series with the best ranked series preference if more than one scorable 

patient series are tied for the highest scorable patient series score. 

8.8 DETERMINE BEST PATIENT SERIES

Determine best patient series provides the decision table to be applied to the set of prioritized patient series, one 

per Series Group, determined above. This step only happens after one prioritized patient series has been 

selected for each Series Group for the antigen. After this process, one or more non-redundant best patient series 

will remain. Each of these best patient series are necessary to fully protect the patient.

TABLE 8-14 IS THE PRIORITIZED PATIENT SERIES THE BEST PATIENT SERIES FOR THE SERIES GROUP?

CONDITIONS RULES

Is the prioritized patient series a 

complete patient series?

Yes No No

Is there a prioritized patient series that 

is a complete patient series in an 

equivalent series group?

- No No

Is the series type of the prioritized 

patient series 'Evaluation Only'?

- No No

Is the series type of the prioritized 

patient series 'Risk'?

- Yes No

Is there a prioritized patient series with 

a series type of 'Risk' in an equivalent 

series group?

- - No

OUTCOMES

Default: No. There is no best patient 

series for the series group.

Yes. The prioritized patient 

series is the best patient 

series for the series group.

Yes. The prioritized patient 

series is the best patient 

series for the series group.

Yes. The prioritized patient 

series the best patient series 

for the series group.
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9 IDENTIFY AND EVALUATE VACCINE GROUP

Identify and evaluate vaccine group combines patient series into a vaccine group-based forecast to provide a 

common and consistent view for a forecast. In the evaluation, forecasting, and select patient series chapters, all 

logic was specified for antigens. At this point it is important to define how those antigen-based evaluation and 

forecasting results can be merged into vaccine group forecasts. For antigens which contain non-equivalent series 

groups (e.g., multiple best patient series), it is important to only blend best patient series of the same series type 

(e.g., risk with risk and standard with standard). Patients in this situation may end up with more than 1 vaccine 

group forecast for a given vaccine group (e.g., a travel-based MMR forecast and an age-based MMR forecast).

Relationship to ACIP Recommendations

• At present, MMR and DTaP/Tdap/Td vaccine groups are comprised of multiple antigens. MMR contains 

the antigens Measles, Mumps, and Rubella. DTaP/Tdap/Td contains the antigens Diphtheria, Tetanus, 

and Pertussis.

TABLE 9-1 IDENTIFY AND EVALUATE VACCINE GROUP PROCESS STEPS

Section Activity Goal

9.1 Apply General Vaccine Group Rules The goal of the activity is to two-fold. The first is to apply general vaccine group 

rules which apply to all vaccine groups. The second is to classify the vaccine 

group type (Single Antigen or Multiple Antigen) for subsequent business rule 

sections (9.2 or 9.3).

9.2 Single Antigen Vaccine Group The goal of this activity is to apply the business rules necessary to generate a 

vaccine group-based forecast in situations where only a single antigen is 

associated with a vaccine group.

9.3 Multiple Antigen Vaccine Group The goal of this activity is to apply the decision logic and business rules 

necessary to generate a vaccine group-based forecast in situations where more 

than one antigen is associated with a vaccine group.

The following figure provides an illustration of the identifying and evaluating vaccine group process.

FIGURE 9-1 IDENTIFY AND EVALUATE VACCINE GROUP PROCESS MODEL
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9.1 APPLY GENERAL VACCINE GROUP RULES

Apply general vaccine group rules provides the business rules which are applied to both types of vaccine groups 

(i.e., Single Antigen and Multiple Antigen). Finally, this table provides rules to classify the vaccine group type 

(Single Antigen or Multiple Antigen) for subsequent business rule sections (9.2 or 9.3).

TABLE 9-2 GENERAL VACCINE GROUP BUSINESS RULES

Business Rule ID Business Rule

FORECASTVG-1 A patient series forecast must be contained in a vaccine group forecast made for a vaccine group if all the 

following are true:

• The patient series forecast is made from a patient series that is identified as a best patient series. 

• The best patient series belongs to the series group for which the vaccine group forecast is being 

made. 

• The best patient series defines the regimen for an antigen classified by the vaccine group. 

FORECASTVG-2 The adjusted recommended date of a vaccine group forecast must be the latest of the following dates:

• The earliest adjusted recommended date of all the patient series forecasts contained in the 

vaccine group forecast. 

• The earliest date of the vaccine group forecast.

FORECASTVG-3 The adjusted past due date of a vaccine group forecast must be the latest of the following dates:

• The earliest adjusted past due date of all the patient series forecasts contained in the vaccine 

group forecast. 

• The earliest date of the vaccine group forecast.

FORECASTVG-4 The latest date of a vaccine group forecast must be the earliest latest date of all the patient series 

forecasts contained in the vaccine group forecast. 

FORECASTVG-5 The unadjusted recommended date of a vaccine group forecast must be the earliest unadjusted 

recommended date of all the patient series forecasts contained in the vaccine group forecast. 

FORECASTVG-6 The unadjusted past due date of a vaccine group forecast must be the earliest unadjusted past due date of 

all the patient series forecasts contained in the vaccine group forecast. 

FORECASTVG-7 The forecast reasons explaining a vaccine group forecast must be the forecast reasons of all the patient 

series forecasts contained in the vaccine group forecast. 

FORECASTVG-8 An antigen must be considered a recommended antigen for a vaccine group forecast if all the following are 

true:

• The antigen is given the regimen of a best patient series.

• The best patient series is the basis of a patient series forecast contained in the vaccine group 

forecast. 

• The best patient series has a patient series status of 'Not Complete'.

FORECASTVG-9 A series dose vaccine must be considered a recommended series dose vaccine for a vaccine group 

forecast if the series dose vaccine is a recommended series dose vaccine for a patient series forecast 

contained in the vaccine group forecast. 

FORECASTDN-2 The forecast dose number for a vaccine group forecast must be one of the following:

• The minimum of the forecast dose numbers of the patient series forecasts contained in the 

vaccine group forecast if the administer full vaccine group flag is 'Y' for the vaccine group that is 

the basis of the vaccine group forecast. 

• The maximum of the forecast dose numbers of the patient series forecasts contained in the 

vaccine group forecast if the administer full vaccine group flag is 'N' for the vaccine group that is 

the basis of the vaccine group forecast. 

VACCINEGROUP-1 A vaccine group must be considered a single antigen vaccine group if it classifies exactly one antigen. 
Logic Specification for ACIP Recommendations v4.4 Page 96 of 151

Business Rule ID Business Rule

VACCINEGROUP-2 A vaccine group must be considered a multiple antigen vaccine group if it classifies more than one antigen. 

9.2 SINGLE ANTIGEN VACCINE GROUP 

The forecasting rules which need to be applied to a single antigen vaccine group are listed in the table below.

TABLE 9-3 SINGLE ANTIGEN VACCINE GROUP BUSINESS RULES

Business Rule ID Business Rule

SINGLEANTVG-1 The vaccine group status of a vaccine group forecast made for a single antigen vaccine group must be the 

patient series status of the patient series forecast contained in the vaccine group forecast. 

SINGLEANTVG-2 The earliest date of a vaccine group forecast made for a single antigen vaccine group must be the earliest 

date of all the patient series forecasts contained in the vaccine group forecast. 

9.3 MULTIPLE ANTIGEN VACCINE GROUP 

The forecasting decisions and rules which need to be applied to a multiple antigen vaccine group are listed 

below.

TABLE 9-4 WHAT IS THE VACCINE GROUP STATUS OF A VACCINE GROUP FORECAST FOR A MULTIPLE 

ANTIGENT VACCINE GROUP?

CONDITIONS RULES

Is there a patient series forecast contained in the 

vaccine group forecast with a patient series status of 

'Contraindicated'?

Yes No No No No No

Is there a patient series forecast contained in the 

vaccine group forecast with a patient series status of 

'Aged Out'?

- Yes No No No No

Is there a patient series forecast contained in the 

vaccine group forecast with a patient series status of 

'Not Recommended'?

- - Yes No No No

Is there a patient series forecast contained in the 

vaccine group forecast with a patient series status of 

'Not Complete'?

- - - Yes No No

Do all patient series forecasts contained in the vaccine 

group forecast have a patient series status of 'Immune'?

- - - - Yes No

Do all patient series forecasts contained in the vaccine 

group forecast have a patient series status of 'Complete' 

or 'Immune'?

- - - - - Yes

OUTCOMES Contraindicated Aged 

Out

Not 

Recommended

Not 

Complete

Immune Complete
Logic Specification for ACIP Recommendations v4.4 Page 97 of 151

TABLE 9-5 MULTIPLE ANTIGEN VACCINE GROUP BUSINESS RULES

Business Rule ID Business Rule

MULTIANTVG-1 The earliest date of a vaccine group forecast for a multiple antigen vaccine group must be one of the 

following:

• The later of the following dates if any patient series forecast contained in the vaccine group 

forecast is a priority patient series forecast: 

o The earliest date of all the patient series forecasts contained in the vaccine group 

forecast.

o The latest date administered of all the vaccine doses administered containing a vaccine 

classified by a vaccine type that belongs to the vaccine group.

• The latest earliest date of all the patient series forecasts contained in the vaccine group forecast if 

there is no priority patient series forecast contained in the vaccine group forecast. 

FORECASTPRIORITY-1 A patient series forecast must be considered a priority patient series forecast if all the following are true:

• The target dose forecasted by the patient series forecast includes at least one preferable interval. 

• Each preferable interval for the target dose has an interval priority flag of 'Y'.
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APPENDIX A: DOMAIN MODEL AND GLOSSARY

DOMAIN MODEL (CONCEPT MODEL, VOCABULARY) OVERVIEW

Purpose

The purpose of employing a domain model (i.e. concept model) is to: 

• Document agreed-upon terms and definitions for the project

• Facilitate discussions of the terms and definitions among project participants and provide tools to capture 

outcomes of these discussions

• Establish a foundation and a reference source (common vocabulary) for other project materials

About Domain Model

A domain is an area of knowledge or activity characterized by a set of concepts and terminology understood by 

the practitioners in the area. A domain model captures vocabulary—terms and definitions. It ensures that all 

terminology and concepts that will appear in the project materials (e.g., business rules, specifications, and 

process descriptions) are known and understood by the domain practitioners (agreed-upon definitions and 

meaning).

A domain model includes:

• A description for each domain models 

• Domain model diagram(s) that shows major business entities, their characteristics (attributes), and their 

relationships (Figure A-1 through A-6)

• Definitional rules for each domain model which establishes the necessity for a concept. (Table A-2 

through A-6)

• A glossary that provides the definitions of vocabulary terms represented on the domain model (Table A7)

Unlike a data model diagram that depicts storage of information or a workflow/process diagram that depicts the 

sequence of steps in a process, a domain diagram is a high-level static representation of the main “things” 

(entities) involved in the clinical decision support process, including a description of how these “things” (entities) 

are related. It is important to note that the domain diagram is not a technical specification. Instead, the domain 

diagram provides the foundation for other modeling diagrams and materials.

How to Read the Domain Model Diagrams

The following figure and glossary provide:

• A legend for the symbols used the Domain Model diagrams

• Definitions for what the symbols represent as well as definitions clarifying related terms.
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FIGURE A-1 NEIGHBORHOOD

The glossary below provides some basic terms related to the methodology used to generate the Domain Model. 

The terms in bold italics relate to the symbols on the diagram above.

TABLE A-1 DOMAIN MODEL GLOSSARY

Term Definition

Binary Verb Concept a verb concept that involves exactly two noun concepts

Business Rule a rule that is practicable and that is under business jurisdiction

Category a general concept whose meaning is more restrictive, but otherwise compliant with, another general 

concept (the super-category)

Concept a notion in a person’s mind that is a unit of knowledge created by a unique combination of definitional 

criteria

Concept Model a set of concepts structured according to the relations among them

Concept Model Diagram a graphical representation of a concept model

General Concept a noun concept that classifies potentially many things on the basis of common properties 

Neighborhood a section of a concept model diagram separated for convenience

Noun Concept a concept that is the meaning of a noun or noun phrase

Property a quality or trait belonging to a thing itself

Role A noun concept that reflects how another noun concept is viewed in the context of a verb concept

Rule a guide for conduct or action; one of a set of usually official regulations by which an activity (as a sport) is 

governed; a standard on which a decision or judgment may be based [MWUD 1a, 1f and [criterion] 2]

Rule-based Term a term for a concept for which some explicit definitional rule(s) is/are specified separately from the 

concept’s definition

Super-Category a general concept that is broader than its related category/ies

Synonym a word (or phrase) having the same meaning as another word (or phrase)

Term a designation for a general concept

U-nary Verb Concept a verb concept that involves exactly one noun concept
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Term Definition

Verb Concept the meaning of a verb phrase (including optional prepositions) along with one or more noun concepts in 

specific relation to that verb phrase

DOMAIN MODEL DIAGRAMS

The domain diagram for the CDSi project is broken into five neighborhoods for enhanced readability and ease 

of printing. Each neighborhood encapsulates a logical grouping of entities.

Patient Neighborhood

The patient neighborhood (Figure A-2) focuses on the patient and the patient’s history. The patient’s history is 

composed of two distinct items of importance. The first is the set of patient observations which may not be directly 

related to a previous immunization event. This includes observations about relevant medical, environmental, 

occupational, and behavioral factors for the patient. The second is the immunization history which is composed 

of vaccine doses administered and adverse reactions.

FIGURE A-2 CDSI DOMAIN DIAGRAM: PATIENT NEIGHBORHOOD

Table A-2 Patient Neighborhood Business Rules 

Business Rule ID Business Rule

DEFPATIENT-001 A vaccine dose administered must be received by exactly one patient.

DEFPATIENT-002 A vaccine dose administered must contain exactly one vaccine.

DEFPATIENT-003 A vaccine dose administered must be part of exactly one immunization history.

DEFPATIENT-004 An evidence of immunity must be documented for exactly one patient.

DEFPATIENT-005 An evidence of immunity must be part of exactly one patient history.

DEFPATIENT-006 A patient must have exactly one patient history.
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Business Rule ID Business Rule

DEFPATIENT-007 A patient history must be for exactly one patient.

DEFPATIENT-008 A patient observation must be part of exactly one patient history.

DEFPATIENT-009 An immunization history must be part of exactly one patient history.

DEFPATIENT-010 An adverse reaction must be part of exactly one immunization history.

DEFPATIENT-011 A vaccine dose administered must have all the following:

• date administered

• dose condition flag.

DEFPATIENT-012 A patient observation must have exactly one observation date.

DEFPATIENT-013 A patient history must include exactly one immunization history.

DEFPATIENT-014 The date administered of a previous vaccine dose administered must be on or before the date administered 

of a current vaccine dose administered. 

DEFPATIENT-015 A dose condition flag must be one of the following for a vaccine dose administered:

• 'Y'

• 'N'.

Schedule Neighborhood

A schedule is the highest-level entity encompassing a collection of recommendations and which is composed of 

antigen series. The schedule neighborhood (Figure A-3) focuses on components that relate directly to the 

schedule rather than to a specific antigen series or dose. These include:

• Immunity considerations which may negate the need for vaccination of the patient

• Contraindications which may alter the risk benefit analysis for a given vaccination, contraindications 

may be either at the antigen or vaccine level

• Vaccine conflicts which indicate adverse interactions between doses of vaccines
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FIGURE A-3 CDSI DOMAIN DIAGRAM: SCHEDULE NEIGHBORHOOD

Table A-3 Schedule Neighborhood Business Rules

Business Rule ID Business Rule

DEFSCHEDULE-001 A contraindication must have all the following:

• Contraindication text description

• Contraindication begin age

• Contraindication end age.

DEFSCHEDULE-002 A clinical guideline observation must have all the following:

• Observation code

• Observation title.

DEFSCHEDULE-003 A birth date immunity must have all the following:

• immunity birth date

• immunity country of birth

• immunity exclusion condition.

DEFSCHEDULE-004 An indication must have all the following:

• indication text description

• indication begin age

• indication end age.

DEFSCHEDULE-005 A clinical history immunity must have an immunity guideline.

DEFSCHEDULE-006 A schedule must include at least one target disease.

DEFSCHEDULE-007 A target disease must be part of at least one schedule.
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Business Rule ID Business Rule

DEFSCHEDULE-008 An immunity must be achieved for exactly one target disease.

DEFSCHEDULE-009 An antigen must protect against exactly one target disease.

DEFSCHEDULE-010 A target disease must be protected by exactly one antigen. 

Antigen Series Neighborhood

A schedule is composed of antigen series. Each antigen series defines a path to immunity for an antigen. An

antigen series focuses on a specific antigen and not a specific vaccine or a vaccine group. Each antigen series 

is composed of series dose(s). A series dose defines the recommendations of the ACIP through dose specific 

entities. The series neighborhood (Figure A-4) focuses on what a vaccine is, how it is related to an Antigen and 

a Vaccine Group, and how those three entities relate to a schedule.

A vaccine has several attributes which uniquely identify it and are important during evaluation and forecasting. 

Each vaccine contains antigen and also belongs to a vaccine group. While not critically important at this stage, 

it should be noted that a vaccine can contain more than one antigen and can belong to more than one vaccine 

group. Combination vaccines – such as Hib-HepB – contain more than one antigen and belong to more than 

one vaccine group.
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FIGURE A-4 CDSI DOMAIN DIAGRAM: ANTIGEN SERIES NEIGHBORHOOD

Table A-4 Antigen Series Neighborhood Business Rules

Business Rule ID Business Rule

DEFANTIGENSERIES001 A default series flag must be one of the following for an antigen series:

• 'Y'

• 'N'.
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Business Rule ID Business Rule

DEFANTIGENSERIES002 A product path flag must be one of the following for an antigen series:

• 'Y'

• 'N'.

DEFANTIGENSERIES003 A from immediate previous dose administered flag must be one of the following if there is an interval for a 

series dose:

• 'Y'

• 'N'.

DEFANTIGENSERIES004 An interval priority flag must be one of the following if there is a preferable interval for a series dose:

• 'Y'

• 'N'

DEFANTIGENSERIES005 A recurring dose flag must be one of the following for a recurring dose:

• 'Y'

• 'N'.

DEFANTIGENSERIES006 A forecast vaccine type flag must be one of the following for a series dose vaccine:

• 'Y'

• 'N'.

DEFANTIGENSERIES007 An administer full vaccine group flag must be one of the following for a multiple antigen vaccine group:

• 'Y'

• 'N'.

DEFANTIGENSERIES008 A series dose may include more than one preferable interval only if the interval priority flag is the same for 

all the preferable intervals. 

DEFANTIGENSERIES009 An antigen series must belong to exactly one series group.

DEFANTIGENSERIES010 An antigen series may belong to a series group only if the antigen series defines a regimen for the same 

antigen as the other antigen series that belong to the series group. 

DEFANTIGENSERIES011 A series group code for a series group must be unique within the antigen that defines the regimen for all 

the antigen series that belong to the series group. 

Evaluation and Forecast Neighborhoods

The evaluation and forecast neighborhoods (Figure A-5 and A-6) are the result of merging the patient 

neighborhood with the schedule and antigen series neighborhoods which apply the recommendations of ACIP. 

That is, it is the result of evaluating vaccine doses administered against the ACIP recommendations and creating 

the forecast for when the next vaccine dose should be administered according to the ACIP recommendations.

While the schedule, antigen series, and series doses from the series and schedule neighborhoods encompass 

the recommendations of the ACIP. When the process of evaluation and forecasting occurs, it is important to 

track the progress of the patient against the goals of the ACIP recommendations to know how close to series 

completion the patient is. This concept is depicted as the patient series and target dose. They are the measuring 

stick tracking the progress of the patient (and their history) against the recommendations of the ACIP. The target 

dose is the “virtual dose” according to the ACIP. The vaccine dose administered is what patient actually received. 

Each vaccine dose administered is evaluated against the target dose and assigned an evaluation status and 

possible evaluation reason. The target dose is also used to create a forecast for the next time an immunization 

is due.
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FIGURE A-5 CDSI DOMAIN DIAGRAM: EVALUATION NEIGHBORHOOD

Table A-5 Evaluation Neighborhood Business Rules

Business Rule ID Business Rule

DEFEVALUATION-001 A patient series must be satisfied by exactly one patient.

DEFEVALUATION-002 A patient series must track exactly one antigen series.

DEFEVALUATION-003 An antigen series must define the regimen for exactly one antigen.

DEFEVALUATION-004 An evaluation must have exactly one evaluation status.

DEFEVALUATION-005 A target dose must have exactly one target dose status.

DEFEVALUATION-006 A patient series must be comprised of at least one target dose.

DEFEVALUATION-007 A target dose must be part of exactly one patient series.

DEFEVALUATION-008 A target dose must track exactly one series dose.

DEFEVALUATION-009 A patient series may include a target dose only if all the following are true:

• The target dose tracks a series dose. 

• The series dose is part of an antigen series. 

• the antigen series is tracked by the patient series.

DEFEVALUATION-012 A series dose must be part of exactly one antigen series.
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FIGURE A-6 CDSI DOMAIN DIAGRAM: FORECAST NEIGHBORHOOD

Table A-6 Forecast Neighborhood Business Rules

Business Rule ID Business Rule

DEFFORECAST-004 A target dose may be forecasted by a patient series forecast only if the target dose is part of the patient 

series that is the basis of the patient series forecast. 

DEFFORECAST-005 A patient series forecast may be made for only one target dose.

DEFFORECAST-006 A target dose may be forecasted by only one patient series forecast.

DEFFORECAST-007 Each patient series must be the basis of exactly one patient series forecast.

DEFFORECAST-008 A patient series forecast must be made from exactly one patient series.

DEFFORECAST-010 A vaccine type may belong to a vaccine group only if the vaccine type contains an antigen that is classified 

by the vaccine group. 

DEFFORECAST-011 An antigen must be classified by exactly one vaccine group.

DEFFORECAST-012 A vaccine group forecast must be made for exactly one series group.

DEFFORECAST-013 Each patient series forecast made from a best patient series must be contained in exactly one vaccine group 

forecast. 

DEFFORECAST-015 A single antigen vaccine group must classify exactly one antigen.
Logic Specification for ACIP Recommendations v4.4 Page 108 of 151

Business Rule ID Business Rule

DEFFORECAST-016 A vaccine group forecast made for a single antigen vaccine group must contain exactly one patient series 

forecast. 

DEFFORECAST-017 A series group may contain only one best patient series.

DEFFORECAST-018 A forecast vaccine type flag may be specified only for a preferable vaccine.

DEFFORECAST-019 A patient series forecast must be made for exactly one series group.

DEFFORECAST-020 A series group may include only one relevant patient series with a default series flag = 'Y'. 

Glossary

The glossary provides the definitions of terms identified by the domain model.

TABLE A-7 GLOSSARY

Term Definition

Absolute Minimum Age an age which may be earlier than the minimum age and allows for a vaccine dose administered to be 

considered valid when administered abnormally early (e.g. grace period) 

Absolute Minimum 

Interval 

an interval which maybe shorter than the minimum interval and allows for a vaccine dose administered to 

be considered valid when administered abnormally early (e.g. grace period) 

Active Patient 

Observation 

a patient observation that is applicable to a patient at the time of an evaluation or forecast 

Administer Full Vaccine 

Group Flag 

an indicator whether a multiple antigen vaccine group should have all antigens in the vaccine group 

recommended to be administered at the same time 

Administrative 

Guidance 

text conveying additional information pertaining to an antigen series, an indication or a contraindication 

Adverse Reaction a negative health consequence experienced by a patient related in time to administration of vaccine (s). 

NOTE: 'In time' means that it happens in some reasonable time after the vaccination event. It might not be 

attributable to a specific vaccine dose administered, especially in cases when the patient receives several 

vaccines in one visit. 

Age the length of time from birth to a specified time

Aged Out A patient series status that indicates the patient exceeded the maximum age prior to completing the patient 

series 

Allowable Interval an interval that is outside of the preferable interval, but still counts towards immunity

Allowable Vaccine a series dose vaccine which is not currently recommended by ACIP for a series dose within an antigen 

series, but still contributes to completing the antigen series 

Allowable Vaccine Type a list of vaccines that are allowed to be administered to a patient if a preferable vaccine is not available 

Antigen a foreign (non-self) substance found in the body that produces an immune response

Antigen 

Contraindication 

a contraindication which applies to all current formulations of vaccine for a given antigen (e.g. all 

formulations of hepatitis a vaccine are contraindicated for a patient with a hypersensitivity to alum) 

Antigen Series one possible path to achieve presumed immunity against a target disease

Antigen Supporting 

Data 

supporting data for a specific antigen

Assessment Date the date for which a forecast is determined
Logic Specification for ACIP Recommendations v4.4 Page 109 of 151

Term Definition

Association Begin Age the earliest age a vaccine type is used for an antigen

Association End Age the latest age a vaccine type is used for an antigen

Best Patient Series a patient series determined to be an optimal choice for a patient

Birth Date Immunity an immunity that may provide protection if a patient was born before an established immunity birth date 

within a country of birth 

Cessation Date the ending date after which a logical component instance is no longer appropriate to use 

Clinical Guideline 

Observation 

an observation defined by an organization (e.g., ACIP) that impacts a patient's immunization 

recommendations 

Clinical History 

Immunity 

an immunity that may provide protection based on patient history

Complete a patient series status that indicates the patient has met all of the ACIP recommendations for the patient 

series 

Conditional Skip a situation where, based on a patient's immunization history or age, the patient may not need a particular 

dose of vaccine. 

Conditional Skip Begin 

Age 

For Conditions of Type Vaccine Count by Age, the Begin Age defines the beginning point of the age range 

to be considered. 

Conditional Skip 

Condition 

a fact about a patient which may impact a patient's need for a particular dose of vaccine. 

Conditional Skip 

Condition ID 

a numeric identifier for a condition within a set

Conditional Skip 

Condition Logic 

When a set consists of more than 1 condition, the Condition Logic determines if all conditions must be met 

or just a single one in order for the set to be met. 

Conditional Skip 

Condition Logic - AND 

When the Condition Logic is 'AND', all conditions in the set must be met in order for the set to be met. 

Conditional Skip 

Condition Logic - OR 

When the Condition Logic is 'OR', only a single condition in the set must be met for the set to be met. 

Conditional Skip 

Context 

The circumstances in which conditional skip rules should be applied during the Evaluation and Forecasting 

process 

Conditional Skip 

Description 

a textual description of the intent of the condition

Conditional Skip Dose 

Count 

For Condition of Types of Vaccine Count by Age or Vaccine Count by Date, the Dose Count indicates the 

critical number of doses for the Condition. The Dose Count works together with Dose Type and Dose 

Count Logic to fully define the Condition. 

Conditional Skip Dose 

Count Logic 

For Condition of Types of Vaccine Count by Age or Vaccine Count by Date, the Dose Count Logic 

indicates for the Condition whether the patient's dose count must be greater than, less than or equal to the 

Dose Count. The Dose Count Logic works together with Dose Count and Dose Type to fully define the 

Condition. 

Conditional Skip Dose 

Type 

For Condition of Types of Vaccine Count by Age or Vaccine Count by Date, the Dose Type indicates for 

the Condition whether or not counted doses must be valid doses for the patient series or not. The Dose 

Type works together with Dose Count and Dose Count Logic to fully define the Condition. 

Conditional Skip End 

Age 

For Conditions of Type Vaccine Count by Age, the End Age defines the ending point of the age range to 

be considered. 

Conditional Skip End 

Date 

For Conditions of Type Vaccine Count by Date, the End Date defines the ending point of the date range to 

be considered. 
Logic Specification for ACIP Recommendations v4.4 Page 110 of 151

Term Definition

Conditional Skip 

Interval 

For Conditions of Type of Interval, the Interval defines the minimum space of time since the last satisfied 

target dose. 

Conditional Skip Series 

Group 

For Conditions of Type of Completed Series, indicates for the condition which series group needs to 

contain a completed series for the condition to be met 

Conditional Skip Set A set is one or more conditions which need to be considered together when determining if a patient can 

skip a particular dose of vaccine. 

Conditional Skip Set ID a numeric identifier for a set of conditions that may lead to the skipping of a series dose 

Conditional Skip Set 

Logic 

When The Conditional Skip section contains more than 1 set, The Set Logic determines if all sets must be 

met or just a single one in order for the dose to be skipped. 

Conditional Skip Set 

Logic - AND 

When the Set Logic is 'AND', all Sets must be met in order for the Dose to be skipped

Conditional Skip Set 

Logic - OR 

When the Set Logic is 'OR', only a single Set must be met for the Set for the Dose to be skipped. 

Conditional Skip Start 

Date 

For Conditions of Type Vaccine Count by Date, the Start Date defines the beginning point of the date 

range to be considered. 

Conditional Skip Type The Type specifies the nature of the condition

Conditional Skip Type -

Age 

If the patient's age at the time of dose administration or forecast is equal to or greater than the Begin Age, 

then the condition is met.

Conditional Skip Type -

Completed Series 

If the patient has completed a patient series in the specified Series Group, then the condition is met.

Conditional Skip Type -

Interval 

If the interval from the administered date of the last satisfied target dose equals or exceeds the Interval, 

then the condition is met.

Conditional Skip Type –

Vaccine Count by Age 

If the patient meets the dose count requirement based on the age range then the condition is met. The 

Dose Count Logic determines if the patient administered dose count should be greater than, less than or 

equal to the Dose Count (either valid or total based on the value of Dose Type). The upper age range 

boundary will be either a discrete age (specified in End Age) or the age of the patient at the time of dose 

administration or forecast (if End Age is n/a). If the Vaccine Types (CVX List) parameter is populated, then 

an administered dose must be of one of the specified CVX codes in order to be counted. If the parameter 

is not populated, then any vaccine valid for the antigen is permitted.

Conditional Skip Type –

Vaccine Count by Date 

If the patient meets the dose count requirement based on the date range then the condition is met. The 

Dose Count Logic determines if the patient administered dose count should be greater than, less than or 

equal to the Dose Count (either valid or total based on the value of Dose Type). If the Vaccine Types (CVX 

List) parameter is populated, then an administered dose must be of one of the specified CVX codes in 

order to be counted. If the parameter is not populated, then any vaccine valid for the antigen is permitted.

Conditional Skip 

Vaccine Type 

the specific types of vaccine dose administered.

Conflict Begin Interval an interval which identifies the start of a vaccine conflict

Conflict End Interval an interval which identifies the end of a vaccine conflict

Conflicting Vaccine 

Type 

the vaccine type of a vaccine that causes a vaccine type conflict if it is administered at too close an interval 

to a different vaccine type 

Contraindicated a patient series status that indicates no further vaccines should be administered at this time for the patient 

series 

Contraindicated Patient 

Series 

a patient series for which there are one or more contraindications that apply to a patient 
Logic Specification for ACIP Recommendations v4.4 Page 111 of 151

Term Definition

Contraindication a clinical guideline observation describing a condition in a patient that greatly increases the chance of a 

serious adverse reaction and renders a vaccination inadvisable for the patient 

Contraindication Begin 

Age 

the earliest age that a contraindication applies to

Contraindication End 

Age 

the latest age that a contraindication applies to

Contraindication Text 

Description 

a recommended action for a clinical guideline observation where there is a contraindication

Country of Birth the birth country where an individual was born

Current Target Dose a target dose being evaluated to determine whether it has been satisfied by a current vaccine dose 

administered 

Current Vaccine Dose 

Administered 

a vaccine dose administered being evaluated to determine whether or not it is valid

CVX Code a numerical identifier used to identify a vaccine type

Date Administered the date of the vaccine dose administered

Date of Birth the date of the patient's birth

Default Patient Series An antigen series which best describes the standard recommendations of the ACIP

Defined by the rule: 

A relevant patient series must be considered a default patient series if the default series flag is 'Y' for the 

antigen series. 

Default Series Flag an indicator that an antigen series is the one that best describes the standard recommendations of the 

ACIP 

Dose Condition Flag an indicator that a dose administered to a patient is considered substandard

Dose Count the number of vaccine doses administered

Dose Number the ordinal number of a series dose in an antigen series

Earliest Date the earliest point in time at which the next target dose could be given

Defined by the rule: 

The earliest date of a patient series forecast made from a relevant patient series must be the candidate 

earliest date. 

Earliest Recommended 

Age 

the preferred age a vaccine should be administered

Earliest Recommended 

Interval 

the lower bound within a range for a preferable interval at which point a patient is recommended to receive 

their next target dose 

Effective Date the starting date at which point a logical component instance is appropriate to use

Equivalent Series 

Group 

a series group which provides protection equivalent to an antigen series

Evaluation the result of the process of applying recommendations for a given series dose. It is the outcome of the 

evaluation process that determines whether a vaccine dose administered is valid. 

Evaluation Only Series an antigen series which outlines recommendations which do not need to be forecasted for completion (e.g. 

non-U.S recommendations, historical US-specific series) 

Evaluation Reason a reason why a vaccine dose administered is or is not valid
Logic Specification for ACIP Recommendations v4.4 Page 112 of 151

Term Definition

Evaluation Status the state of a vaccine dose administered with respect to an assessment of its validity against a target dose 

Evidence of Immunity the proof or written documentation that indicates a patient may have immunity

Extraneous an evaluation status that indicates the vaccine dose administered was not administered according to ACIP 

recommendations, but the dose does not need to be repeated (including maximum age and extra doses) 

Forecast the outcome of determining a patient’s next recommended action based on a set of immunization rules and 

the patient’s particular situation 

Forecast Dose Number the ordinal number of the next target dose in a forecast based on the evaluation of a patient’s 

immunization history

Defined by the rule: 

The forecast dose number for a patient series forecast must be calculated as one of the following:

• The count of all target doses plus 1 where all the following are true:

o The target dose is part of the relevant patient series.

o The target dose has a target dose status of 'Satisfied'

o There is no seasonal recommendation start date for the series dose that is tracked by 

the target dose. 

• The count of all target doses plus 1 where all the following are true:

o The target dose is part of the relevant patient series.

o The target dose has a target dose status of 'Satisfied'

o There is a seasonal recommendation start date for the series dose that is tracked by the 

target dose. 

o The date administered of the vaccine dose administered evaluated by the target dose is 

on or after the seasonal recommendation start date. 

Forecast Reason a reason why a target dose is or is not recommended to be administered

Forecast Vaccine Type 

Flag 

an indicator that a specific vaccine type or vaccine product type should be recommended for a series dose 

vaccine 

From Immediate 

Previous Dose 

Administered Flag 

an indicator that an interval is applied from the date administered of the previous vaccine dose 

administered within an antigen series 

From Most Recent 

(CVX List) 

see From Most Recent Vaccine Type

From Most Recent 

Vaccine Type 

a vaccine type for which an interval is determined from the date administered of the most recent 

occurrence of that vaccine type 

From Relevant 

Observation Code 

a clinical guideline observation from which the interval is determined

From Target Dose 

Number in Series 

the dose number within an antigen series used in calculating an interval

Gender socially constructed roles, behaviors, activities, and attributes that a given society considers appropriate for 

boys and men or girls and women 

Immune a patient series status that indicates the patient has evidence of immunity indicating no further vaccines 

are needed for the patient series 

Immunity a condition of being able to resist a target disease

Immunity Birth Date the date that suggests when a patient may have protection from a specific disease

Immunity Country of 

Birth 

the country where a patient must have been born in order for a birth date immunity to apply 
Logic Specification for ACIP Recommendations v4.4 Page 113 of 151

Term Definition

Immunity Exclusion 

Condition 

a patient factor which precludes a patient from being considered immune without vaccination

Immunity Guideline a statement defined by an organization (e.g., ACIP) that can be used to help determine whether clinical 

history immunity is applicable 

Immunization the process of being made immune or resistant to an infectious disease typically by the administration of a 

vaccine 

Immunization History a collection of information detailing vaccination events for a patient

Impacted Vaccine Dose 

Administered 

a vaccine dose administered that is impacted by a vaccine conflict

Defined by the rule: 

A current vaccine dose administered must be considered an impacted vaccine dose administered if all the 

following are true for the date administered of the current vaccine dose administered: 

• It is on or after the conflict begin interval date.

• It is before the conflict end interval date.

Impacted Vaccine Type the vaccine type of a vaccine that may be ineffective if it is administered at too close an interval to a 

different vaccine type 

Inadvertent 

Administration 

a vaccine dose administered where an inadvertent vaccine was administered

Inadvertent Vaccine a series dose vaccine which is currently prohibited by ACIP for a series dose within an antigen series, and 

which does not contribute to completing the antigen series 

Indication a clinical guideline observation signifying the need for an antigen series because of an increased risk of a 

target disease 

Indication Begin Age the earliest age that an indication applies to

Indication End Age the latest age that an indication applies to

Indication Text 

Description 

a recommended action for a clinical guideline observation where there is an contraindication

Interval a period of time between instances, typically between vaccine doses administered

Interval Priority Flag an indicator of whether the earliest date of a multiple antigen vaccine group forecast must be accelerated 

to recommend earlier vaccination 

Latest Date the latest point in time at which the next target dose should be given

Defined by the rules: 

The latest date of a patient series forecast must be one of the following:

• The maximum age date minus 1 day if there is a maximum age date.

• Blank if there is no maximum age date.

Latest Recommended 

Age 

the age a vaccine must be administered before the patient is considered overdue

Latest Recommended 

Interval 

the upper bound within a range for a preferable interval after which a patient is considered overdue for 

their next target dose 

Logical Component a set of related logical component elements that contribute to the supporting data (e.g. Age, Preferable 

Interval, Allowable Interval and Conditional Skip) 

Logical Component 

Element 

a single concept contained as part of a Logical Component (e.g. elements of the Age logical component 

include Absolute Minimum Age, Minimum Age and Earliest Recommended Age) 
Logic Specification for ACIP Recommendations v4.4 Page 114 of 151

Term Definition

Logical Component 

Instance 

a set of specific values for the Logical Component Elements in a Logical Component

Lot Number Expiration 

Date 

the date after which the vaccine is no longer considered potent

Manufacturer an organization that produces a vaccine product type

Maximum Age the latest age a vaccine may be administered

Maximum Age To Start the latest age an antigen series may be started

Minimum Age the earliest age a vaccine may be administered

Minimum Age To Start the earliest age an antigen series may be started

Minimum Conflict End 

Interval 

an interval which identifies the absolute earliest end of a vaccine conflict

Minimum Interval the shortest interval between two vaccine doses administered

Multiple Antigen 

Vaccine Group 

a vaccine group containing more than one antigen designed to protect against more than one target 

disease (e.g. MMR, DTaP/Tdap/Td)

Defined by the rule: 

A vaccine group must be considered a multiple antigen vaccine group if it classifies more than one antigen. 

MVX Code an identifier established and maintained by the CDC that describes a manufacturer

Next Target Dose a target dose that comes after the current target dose in a patient series

Next Vaccine Dose 

Administered 

a vaccine dose administered that occurred after a current vaccine dose administered

Not Complete a patient series status that indicates the patient has not yet met all of the ACIP recommendations for the 

patient series 

Not Recommended a patient series status that indicates the patient's immunization history provides sufficient protection 

against a target disease and there's no recommended action at this time 

Not Satisfied a target dose status that indicates no vaccine dose administered has met the goals of the target dose 

Not Valid an evaluation status that indicates the vaccine dose administered was not administered according to ACIP 

recommendations and must be repeated at an appropriate time in the future 

Observation a notation of a medical, environmental, behavioral, or occupational situation

Observation Code a unique identifier for a clinical guideline observation

Observation Date the date which a clinician determined the patient observation occurred or will occur

Observation Title a name for a clinical guideline observation

Past Due Date the date a patient should be considered overdue for the next target dose

Patient an individual who is the actual or potential recipient of a vaccine dose administered

Patient Gender the patient's gender

Patient History a narrative or record of current and/or past events and circumstances that are or may be relevant to a 

patient's current state of health 

Patient Observation an observation specific to a patient

Patient Series an antigen series based on a patient's actual progress towards completing ACIP recommendations

Patient Series Forecast a forecast for a patient series
Logic Specification for ACIP Recommendations v4.4 Page 115 of 151

Term Definition

Patient Series Status the state of a patient series with respect to a patient satisfying the goals for the patient series 

Preferable Interval an interval defined by ACIP best practices

Preferable Vaccine a series dose vaccine which is currently recommended by ACIP for a series dose within an antigen series 

and that contributes to completing the antigen series 

Previous Target Dose a target dose that comes before the current target dose in a patient series

Previous Vaccine Dose 

Administered 

a vaccine dose administered that occurred prior to or on the same day as a current vaccine dose 

administered 

Prioritized Patient 

Series 

a scorable patient series within a series group that has been selected for further consideration as a best 

patient series

Defined by the rule: 

The prioritized patient series must be one of the following:

• The scorable patient series with the highest scorable patient series score.

• The scorable patient series with the best ranked series preference if more than one scorable 

patient series are tied for the highest scorable patient series score. 

Product Path Flag an indicator that an antigen series specifically targets a product, vaccine type, and/or trade name 

Reason a rationale or justification for an outcome

Recommended Date the date at which the next target dose should be given

Recurring Dose a dose that is to be repeated

Recurring Dose Flag an indicator that a target dose is a recurring dose

Relevant Patient Series a patient series that has been selected based on the appropriateness for a patient

Required Gender the gender that is recommended for an antigen series

Risk Series an antigen series which outlines immunization recommendations based on underlying indications a patient 

may have 

Satisfied a target dose status that indicates a vaccine dose administered has met the goals of the target dose 

Schedule a collection of guidelines defined by an organization (e.g. ACIP) recommending under what circumstances 

someone should or should not receive a vaccine 

Schedule Supporting 

Data 

supporting data which span antigens including:
Logic Specification for ACIP Recommendations v4.4 Page 116 of 151

Term Definition

Scorable Patient Series a relevant patient series that has been selected for further consideration as a potential prioritized patient 

series

Defined by the rule: 

A relevant patient series must be considered a scorable patient series if one of the following is true:

• All the following are true for the relevant patient series:

o The relevant patient series tracks an antigen series with a series type of 'Risk'

o The series priority of the antigen series tracked by the relevant patient series is the same 

as or greater than the series priority of any relevant patient series that tracks an antigen 

series that belongs to the same series group as the relevant patient series. 

o It is a candidate scorable patient series.

• All the following are true for the relevant patient series:

o The relevant patient series tracks an antigen series with a series type of 'Standard'

o The relevant patient series includes a target dose evaluating at least one vaccine dose 

administered with an evaluation status of 'Valid'. 

o The earliest vaccine dose administered with an evaluation status of 'Valid' associated 

with the relevant patient series has a date administered before the maximum age to start 

date. 

o It is a candidate scorable patient series.

• All the following are true for the relevant patient series:

o The relevant patient series tracks an antigen series with a series type of 'Standard'

o The number of valid doses is 0 for each relevant patient series in the series group. 

o There is no default patient series for the series group.

o It is a candidate scorable patient series.

• All the following are true for the relevant patient series:

o The relevant patient series tracks an antigen series with a series type of 'Evaluation 

Only' 

o The relevant patient series is a complete patient series.

Seasonal 

Recommendation 

a recommendation which is indicated by a seasonal start date and a seasonal end date in conjunction with 

the patient's age 

Seasonal 

Recommendation End 

Date 

the last day a seasonal vaccine should be recommended

Seasonal 

Recommendation Start 

Date 

the first day a seasonal vaccine should be recommended

Select Patient Series a set of properties used in determining one or more best patient series

Series Dose an individual dose within an antigen series

Series Dose Vaccine a vaccine type or a vaccine product type that can be used for a series dose based on adherence to the 

ACIP recommendations 

Series Group a classification of one or more antigen series based on the ACIP recommendations for achieving immunity 

Series Group Code a unique identifier for a series group

Series Group Name a meaningful label for a series group

Series Name a meaningful identifier for an antigen series

Series Preference a ranking given to antigen series within a series group

Series Priority a ranking given to antigen series within a single series group. The series priority is considered when 

selecting relevant patient series to evaluate as a potential best patient series 

Series Type a categorization of a type of antigen series
Logic Specification for ACIP Recommendations v4.4 Page 117 of 151

Term Definition

Sex one’s biological status as either male or female, and is associated primarily with physical attributes such as 

chromosomes, hormone prevalence, and external and internal anatomy 

Single Antigen Vaccine 

Group 

a vaccine group containing one antigen designed to protect against one target disease (e.g., Hib, HepB, 

Polio)

Defined by the rule: 

A vaccine group must be considered a single antigen vaccine group if it classifies exactly one antigen. 

Skipped a target dose status that indicates no vaccine dose administered has met the goals of the target dose. Due 

to the patient's age and/or interval from a previous dose, the target dose does not need to be satisfied. 

Standard Series an antigen series which outlines routine immunization recommendations

Sub-standard an evaluation status that indicates the vaccine dose administered has a known dose condition (e.g., 

expired, sub-potent, and recall) which requires the dose to be repeated at an appropriate time in the future 

Supporting Data a structured representation of ACIP recommendations

Target Disease a disease where a vaccine can be administered to a patient to reduce the risk of contracting the disease by 

working with the body's natural defenses to help it develop an immunity to the disease 

Target Dose a patient-specific dose required to satisfy a recommendation of the ACIP

Target Dose Number the ordinal number of a target dose in a patient series

Target Dose Status the state of a vaccine dose administered with respect to the vaccine dose administered meeting the goals 

of the target dose against which it is being evaluated 

Trade Name the manufacturer's proprietary name for a vaccine type

Vaccination the use of vaccines to produce immunity to a disease

Vaccine a dose of substance administered during a vaccination event

Vaccine Conflict a condition that occurs when a vaccine type is administered at too close of an interval with another 

substance such as a vaccine type causing a reduction in the effectiveness of a vaccine 

Vaccine 

Contraindication 

a contraindication which is specific to a particular vaccine (e.g. a prefilled syringe with a latex plunger 

would trigger a vaccine contraindication for patients with a latex allergy but other formulations of vaccine 

for the same antigen may be safe to give) 

Vaccine Dose 

Administered 

a medical occurrence of administering one Vaccine to a Patient

Vaccine Group a classification of antigens that describes broad categories of target diseases

Vaccine Group 

Forecast 

a forecast for a vaccine group

Vaccine Group Status the state of a vaccine group forecast with respect to meeting the goals of the vaccine group for which the 

vaccine group forecast is being made 

Vaccine Product Type a classification that describes the manufacturer, and presentation of a vaccine type

Vaccine Type a classification of vaccines that describes the target disease(s) to which it provides immunity 

Vaccine Type Antigen 

Association 

a vaccine type that is used for an antigen

Vaccine Type Begin 

Age 

the earliest age a series dose vaccine is applicable

Vaccine Type Conflict a vaccine conflict between two vaccine types
Logic Specification for ACIP Recommendations v4.4 Page 118 of 151

Term Definition

Vaccine Type 

Description 

a description of a vaccine type

Vaccine Type End Age the latest age a series dose vaccine is applicable

Valid An evaluation status that indicates the vaccine dose administered was administered according to ACIP 

recommendations 

Volume the amount of space a substance takes up
Logic Specification for ACIP Recommendations v4.4 Page 119 of 151

APPENDIX B: ACRONYMS AND ABBREVIATIONS

The table below provides the meanings of acronyms and abbreviations stated within the document.

TABLE B-1 ACRONYMS AND ABBREVIATIONS

Term Meaning

ACIP Advisory Committee on Immunization Practices

CDC Centers for Disease Control and Prevention

CDS Clinical Decision Support

CDSi Clinical Decision Support for Immunization

DHHS U.S. Department of Health and Human Services

DT Diphtheria and tetanus toxoids adsorbed (children)

DTaP Diphtheria and tetanus toxoids and acellular pertussis vaccine adsorbed

EHR Electronic Health Record

FDA Federal Drug Administration

Flu Influenza

HECB Health Education and Communication Branch

HepA Hepatitis A vaccine

HepB Hepatitis B vaccine

Hib Haemophilus influenza type b conjugate vaccine

HIE Health Information Exchange

HIS Health Information System

HIV Human Immunodeficiency Virus

HPV Human papillomavirus vaccine

IDAB Informatics and Data Analytics Branch

IIS Immunization Information System

JE Japanese Encephalitis vaccine

MCV Meningococcal conjugate vaccine

MMR Measles, Mumps, and Rubella vaccine

MMRV Measles, Mumps, Rubella, and Varicella vaccine

MMWR Morbidity and Mortality Weekly Report

NCIRD National Center for Infectious Diseases

Mpox & Smallpox Orthopoxvirus vaccine

PCV Pneumococcal conjugate vaccine

PPSV Pneumococcal polysaccharide vaccine

Polio Poliomyelitis vaccine

Rota Rotavirus vaccine
Logic Specification for ACIP Recommendations v4.4 Page 120 of 151

Term Meaning

RSV Respiratory Syncytial Virus vaccine

SME Subject Matter Expert

Td Tetanus and diphtheria toxoids adsorbed (adult)

Tdap Tetanus toxoid, reduced diphtheria toxoid and acellular pertussis vaccine, adsorbed

TBE Tick-borne Encephalitis vaccine

VZ Varicella vaccine

YF Yellow Fever vaccine
Logic Specification for ACIP Recommendations v4.4 Page 121 of 151

APPENDIX C: RETIRED ITEMS 

The table below provides a list of terms, rules and tables used in previous versions of the document but which 

are no longer in use.

TABLE C-1 RETIRED ITEMS

Version Item Name Motivation

4.4 Business 

Rule

ALLOWABLE-1 This rule is no longer used.

4.4 Business 

Rule

ALLOWABLE-2 This rule is no longer used.

4.4 Business 

Rule

CALCDTINT-7 This rule is no longer used.

4.4 Business 

Rule

CALCDTLIVE-1 This rule is no longer used.

4.4 Business 

Rule

CALCDTLIVE-2 This rule is no longer used.

4.4 Business 

Rule

CALCDTLIVE-3 This rule is no longer used.

4.4 Business 

Rule

CALCDTLIVE-4 This rule is no longer used.

4.4 Business 

Rule

CONFLICT-1 This rule is no longer used.

4.4 Business 

Rule

CONFLICT-2 This rule is already covered under the CONFLICT-1 business rule.

4.4 Business 

Rule

EVALINADVERT-1 This rule is no longer used.

4.4 Business 

Rule

EVALINT-1 This rule is no longer used.

4.4 Business 

Rule

EVALINT-2 This rule is no longer used.

4.4 Business 

Rule

MULTIANTVG-2 This rule is being replaced with new rule FORECASTVG-2.

4.4 Business 

Rule

MULTIANTVG-3 This rule is being replaced with new rule FORECASTVG-3.

4.4 Business 

Rule

MULTIANTVG-4 This rule is being replaced with new rule FORECASTVG-4.

4.4 Business 

Rule

MULTIANTVG-5 This rule is being replaced with new rule FORECASTVG-5.

4.4 Business 

Rule

MULTIANTVG-6 This rule is being replaced with new rule FORECASTVG-6.

4.4 Business 

Rule

MULTIANTVG-7 This rule is being replaced with new rule FORECASTVG-7.

4.4 Business 

Rule

MULTIANTVG-8 This rule is being replaced with new rule FORECASTVG-8.
Logic Specification for ACIP Recommendations v4.4 Page 122 of 151

Version Item Name Motivation

4.4 Business 

Rule

MULTIANTVG-9 This rule is being replaced with new rule FORECASTVG-9.

4.4 Business 

Rule

PREFERABLE-1 This rule is no longer used.

4.4 Business 

Rule

PREFERABLE-2 This rule is no longer used.

4.4 Business 

Rule

SINGLEANTVG-10 This rule is being replaced with new rule FORECASTVG-9.

4.4 Business 

Rule

SINGLEANTVG-3 This rule is being replaced with new rule FORECASTVG-2.

4.4 Business 

Rule

SINGLEANTVG-4 This rule is being replaced with new rule FORECASTVG-3.

4.4 Business 

Rule

SINGLEANTVG-5 This rule is being replaced with new rule FORECASTVG-4.

4.4 Business 

Rule

SINGLEANTVG-6 This rule is being replaced with new rule FORECASTVG-5.

4.4 Business 

Rule

SINGLEANTVG-7 This rule is being replaced with new rule FORECASTVG-6.

4.4 Business 

Rule

SINGLEANTVG-8 This rule is being replaced with new rule FORECASTVG-7.

4.4 Business 

Rule

SINGLEANTVG-9 This rule is being replaced with new rule FORECASTVG-8.

4.4 Business 

Rule

VALIDATEREC-1 This rule is no longer used.

4.4 Decision 

Table

Could the Two Vaccine 

Doses Administered be 

in Conflict?

This decision table was replaced by new rules CALCDTCONFLICT-1 & 2.

4.4 Decision 

Table

Is the Current Vaccine 

Dose Administered in 

Conflict With a Previous 

Vaccine Dose 

Administered?

This decision table was replaced by new rule CONFLICT-3.

4.4 Decision 

Table

Should the Current 

Vaccine Dose 

Administered be 

Evaluated for a Live 

Virus Conflict?

This decision table was replaced with new rule CONFLICT-3.

4.4 Decision 

Table

What is the Vaccine 

Group Type?

The decision table was replaced by new rules VACCINEGROUP-1 & 2.

4.4 Term Antigens Needed This term is no longer used.

4.4 Term Conflicting vaccine dose 

administered

This term is no longer used.

4.4 Term Current Vaccine Type This term is no longer used.

4.4 Term Live Virus Vaccine This term is no longer used.

4.4 Term Previous Vaccine Type This term is no longer used.
Logic Specification for ACIP Recommendations v4.4 Page 123 of 151

Version Item Name Motivation

4.4 Term Total Count of Valid 

Doses

This term is no longer used as of v2.1.

4.2 Business 

Rule

SELECTB-1 This rule is no longer needed and therefore was retired in version 4.2.

4.2 Business 

Rule

SELECTB-8 This rule is no longer needed and therefore was retired in version 4.2.

4.1 Term Observation Description Term was replaced by Indication Text Description and Contraindication Text 

Description which are used in Supporting Data.

4.1 Term Relevant Environmental 

Observation

The term is not being used.

4.1 Term Vaccination 

Administrative Guidance

Retired term due to update to rule FORECASTGUIDANCE-1 which no longer 

references the term.

4.0 Business 

Rule

SELECTSCORE-1 Rule was retired and condensed into one rule in SELECTSCORE-2.

4.0 Term Organization Term is not used anywhere.

4.0 Term Relevant Behavioral 

Observation

The term is not being used.

4.0 Term Relevant Medical 

Observation

The term is not being used.

4.0 Term Schedule Name Term is not used anywhere.

3.0 Business 

Rule

SELECTB-10 This rule is no longer needed and therefore was retired in version 3.0.

3.0 Business 

Rule

SELECTB-15 This rule is no longer needed as gender consideration was moved to the create 

relevant series section.

3.0 Business 

Rule

SELECTB-22 The Rule was retired when the term “candidate patient series” was replaced by 

“scorable patient series”.

3.0 Business 

Rule

SELECTB-4 The Rule was retired when the term “candidate patient series” was replaced by 

“scorable patient series”.

3.0 Business 

Rule

SELECTB-9 This rule is not used use anywhere in the Logic Spec per the Domain Model 

Analysis.

3.0 Decision 

Table

Is the Patient's Gender 

One of the Required 

Genders?

The decision table was retired when the gender logic was incorporated into the 

selection of relevant patient series.

3.0 Term Candidate Patient Series The term was retired when replaced by "scorable patient series" when the 

evolution of patient series was rethought.

3.0 Term CVX List Has no clear definition and is only a descriptor of Supporting Data elements

3.0 Term Date Administered of 

First Satisfied Target 

Dose

Is only a conglomeration of individual terms

3.0 Term Exceeded Maximum Age 

To Start

This term is no longer needed and therefore was retired in version 3.0.

3.0 Term First Dose Begin Age Not used in version 3.0. It should have been removed/retired in version 2.1.

3.0 Term First Dose End Age Not used in version 3.0. It should have been removed/retired in version 2.1.
Logic Specification for ACIP Recommendations v4.4 Page 124 of 151

Version Item Name Motivation

3.0 Term Gender-Specific Patient 

Series

This rule is no longer needed as gender consideration was moved to the create 

relevant series section.

3.0 Term Medical History Replaced in version 3.0 by Patient History.

3.0 Term Preferable Vaccine Trade 

Name

The term was retired because Trade Name is an attribute of Vaccine of which 

there are two flavors, preferable and allowable. See the term Trade Name for a 

definition.

3.0 Term Preferable Vaccine 

Volume

The term was retired because Volume is an attribute of Vaccine of which there 

are two flavors, preferable and allowable. See the term Volume for a definition.

3.0 Term Select Best Patient 

Series

Replaced with Select Patient Series in V3.0

3.0 Term Vaccine Type Begin Age 

Date

This is a duplicate of Preferable Vaccine Type Begin Age Date

3.0 Term Vaccine Type End Age 

Date

This is a duplicate of Preferable Vaccine Type End Age Date.

2.1 Business 

Rule

CALCDTCOND-1 The rule was no longer needed in the Logic Specification due to the newly 

added conditional skip logic.

2.1 Business 

Rule

CALCDTCOND-2 The rule was no longer needed in the Logic Specification due to the newly 

added conditional skip logic.

2.1 Business 

Rule

CALCDTSKIP-1 The rule was no longer needed in the Logic Specification due to the newly 

added conditional skip logic.

2.1 Business 

Rule

CALCDTSKIP-2 The rule was no longer needed in the Logic Specification due to the newly 

added conditional skip logic.

2.1 Business 

Rule

CALCDTSUB-1 The rule was no longer needed in the Logic Specification due to the newly 

added conditional skip logic. It was replaced with CALCDTSKIP-3 rule.

2.1 Business 

Rule

CALCDTSUB-2 The rule was no longer needed in the Logic Specification due to the newly 

added conditional skip logic. It was replaced with CALCDTSKIP-4 rule.

2.1 Business 

Rule

CONDNEED-1 The rule was no longer needed in the Logic Specification due to the newly 

added conditional skip logic.

2.1 Business 

Rule

CONDNEED-2 The rule was no longer needed in the Logic Specification due to the newly 

added conditional skip logic.

2.1 Business 

Rule

Number of Doses 

Remaining

The rule was no longer needed in the Logic Specification due to the newly 

added conditional skip logic.

2.1 Business 

Rule

SELECTBEST-3 The rule was incorporated into SELECTBEST-2.

2.1 Business 

Rule

SUBDOSE-1 The rule was no longer needed in the Logic Specification due to the newly 

added conditional skip logic.

2.1 Business 

Rule

SUBDOSE-2 The rule was no longer needed in the Logic Specification due to the newly 

added conditional skip logic.

2.1 Decision 

Table

Can Target Doses Be 

Substituted? (Evaluation)

The decision table was retired in favor of new conditional skip logic.

2.1 Decision 

Table

Can Target Doses Be 

Substituted? (Forecast)

The decision table was retired in favor of new conditional skip logic.

2.1 Decision 

Table

Is the Condition Met? This decision table was retired in favor of new Conditional Skip logic.
Logic Specification for ACIP Recommendations v4.4 Page 125 of 151

Version Item Name Motivation

2.1 Decision 

Table

Is the Target Dose 

Conditionally Needed?

This decision table was retired in favor of new Conditional Skip logic.

2.1 Term Conditional Begin Age The term was replaced with the Conditional Skip term.

2.1 Term Conditional Begin Age 

Date

The term was replaced with the Conditional Skip term.

2.1 Term Conditional End Age The term was replaced with the Conditional Skip Begin Age term.

2.1 Term Conditional End Age 

Date

The term was replaced with the Conditional Skip term.

2.1 Term Conditional End Date The term was replaced with the Conditional Skip Condition term.

2.1 Term Conditional Need The term was replaced with Conditional Skip Type- Vaccine Count by Age term.

2.1 Term Conditional Need Dose 

Count

The term was replaced with the Conditional Skip Dose Count term.

2.1 Term Conditional Need 

Vaccine Count

The term was replaced with the Conditional Skip term.

2.1 Term Conditional Need 

Vaccine Type

The term was replaced with the Conditional Skip term.

2.1 Term Conditional Set The term was replaced with the Conditional Skip Type-Vaccine Count by Date 

term.

2.1 Term Conditional Start Date The term was replaced with Conditional Skip Vaccine Types (CVX List) 

supporting data concept.

2.1 Term Conditionally Needed 

Administrations

The term was replaced with Number of Conditional Doses Administered rule.

2.1 Term First Dose Begin Age 

Date

The term was replaced with Conditional Skip.

2.1 Term First Dose End Age Date The term was replaced with Conditional Skip.

2.1 Term Forecast Status The term was retired because it is not used anywhere (except on Domain 

model for versions 1.3) in the Logic Specification.

2.1 Term Number of Doses 

Remaining

The term was replaced with Conditional skip.

2.1 Term Number of Target Doses 

to Substitute

The term was replaced with Conditional Skip.

2.1 Term Skip Target Dose The term was replaced with the Conditional Skip.

2.1 Term Substitute Dose This term was replaced with Conditional Skip.

2.1 Term Substituted This term was replaced with Conditional Skip.

2.1 Term Target Doses with a 

Target Dose Status 

“Satisfied”

This term was replaced with Conditional Skip.

2.1 Term Trigger Age The term was replaced with Conditional Skip.

2.1 Term Trigger Age Date The term was replaced with Conditional Skip.

2.1 Term Trigger Doses 

Administered

The term was replaced with Conditional Skip.

2.1 Term Trigger Interval The term was replaced with Conditional Skip.
Logic Specification for ACIP Recommendations v4.4 Page 126 of 151

Version Item Name Motivation

2.1 Term Trigger Interval Date The term was replaced with Conditional Skip.

2.1 Term Trigger Target Dose The term was replaced with Conditional Skip.

2.1 Term Unnecessary

1.7 Term Conflict End Date The term was replaced with Conflict End Interval Date term.

1.5 Business 

Rule

SATISFIEDVG-1 The rule was retired due to restructuring of Chapter 7 in v1.5.

1.5 Business 

Rule

SATISFIEDVG-2 The rule was retired due to restructuring of Chapter 7 in v1.5.

1.5 Business 

Rule

SATISFIEDVG-3 The rule was retired due to restructuring of Chapter 7 in v1.5.

1.5 Business 

Rule

SATISFIEDVG-4 The rule was retired due to restructuring of Chapter 7 in v1.5.

1.5 Business 

Rule

UNSATPARTVG-1 The rule was retired due to restructuring of Chapter 7 in v1.5.

1.5 Business 

Rule

UNSATPARTVG-2 The rule was retired due to restructuring of Chapter 7 in v1.5.

1.5 Business 

Rule

UNSATPARTVG-3 The rule was retired due to restructuring of Chapter 7 in v1.5.

1.5 Business 

Rule

UNSATPARTVG-4 The rule was retired due to restructuring of Chapter 7 in v1.5.

1.5 Business 

Rule

UNSATPARTVG-5 The rule was retired due to restructuring of Chapter 7 in v1.5.

1.5 Business 

Rule

UNSATPARTVG-6 The rule was retired due to restructuring of Chapter 7 in v1.5.

1.5 Business 

Rule

UNSATPARTVG-7 The rule was retired due to restructuring of Chapter 7 in v1.5.

1.5 Business 

Rule

UNSATPARTVG-8 The rule was retired due to restructuring of Chapter 7 in v1.5.

1.5 Business 

Rule

UNSATPARTVG-9 The rule was retired due to restructuring of Chapter 7 in v1.5.

1.5 Decision 

Table

What is the Type and 

Condition of the Vaccine 

Group?

The decision table was retired and replaced by 'What is the Vaccine Group 

Type?'

1.5 Decision 

Table

What Is the Vaccine 

Group Forecast?

The decision table was retired and replaced by 'What is the Vaccine Group 

Status?'

1.5 Term Satisfied Patient Series The tern was retired due to redesign of chapter 7 in v1.5

1.5 Term Satisfied Vaccine Group The term was retired due to redesign of chapter 7 in v1.5

Term Preferred Candidate 

Patient Series

Term is not being used.
Logic Specification for ACIP Recommendations v4.4 Page 127 of 151
Logic Specification for ACIP Recommendations v4.4 Page 128 of 151

APPENDIX D: ACKNOWLEDGEMENTS

Subject Matter Experts

• Bill Adams, MD, Boston University School of Medicine

Dr. William Adams is an epidemiologist, medical informatician, and practicing pediatrician at 

Boston Medical Center (BMC). He is Director of BU-CTSI Clinical Research Informatics, Director of 

Child Health Informatics, and Professor of Pediatrics at Boston University School of Medicine. His 

research focuses on developing and evaluating information technology (IT)-based solutions for 

improving the quality of health and healthcare for children. His focuses include immunization 

registries, the child health EHR, patient-centered IT and clinical data warehousing for quality 

improvement and research. He is a member of the Massachusetts Immunization Information 

System (MIIS) technical and programmatic teams. He is a founding member of the American 

Academy of Pediatrics (AAP) Partnership for Policy Implementation (PPI), a group of child health 

informaticians committed to improving AAP guideline quality including computability. He also 

serves as advisor to the AAP Center for Child Health Informatics and is a member of the AAP Steering 

Committee for the Quality Innovation Network.

• Greg Anderson, Connexin Software

• Judy Anderson, Hewlett Packard (HP)

Judy Anderson has nearly 8 years of hands-on immunization registry experience that includes direct 

interaction with the Georgia registry users, in-depth knowledge of the immunization schedules, 

requirement analysis, and test case development. Past projects have included data exchange and 

inventory management/reporting enhancements, in addition to a supporting role on the current 

VTrckS implementation for the Georgia registry. One of her strengths, as a member of the HP 

Immunization Evaluator Workgroup, is to interpret CDC/ACIP recommendations into logical 

solutions that can be implemented across the WIR-based registries. She is a graduate of Loyola 

University of Chicago with a Bachelor of Arts degree in Communication Arts/Mass Media.

• Regina Austin, HLN Consulting

Regina Austin has over 20 years of healthcare-related experience and expertise in analysis, 

requirement elicitation, writing, testing, and training in her current position as Senior Analyst and 

Project Specialist with HLN Consulting. Her focus in recent years has been assisting public health 

clients with the development and deployment of customized, cutting-edge software meeting the 

latest standards in healthcare IT. She is current the lead business analyst on several major HLN 

immunization-related projects. Regina attends and participates in key industry conferences, most 

recently as a facilitator of HL7 2.5.1 and clinical decision support round-table discussions at the 

2012 and 2013 AIRA conferences. Regina is also a member of American Immunization Registry 

Association’s (AIRA) Standards and Interoperability Steering Committee (SISC). Among her recent 
Logic Specification for ACIP Recommendations v4.4 Page 129 of 151

publications is a white paper in the HIMSS Journal of Healthcare Information Management on open 

source clinical decision support for immunizations.

• Freddie Barber, RN, BA, MSHCA, Scientific Technology Company (STC)

Freddie Barber became a Registered Nurse in 1983. She started her nursing career as a critical care 

nurse spending 20 years at various levels in the acute care setting in monitored units. In 1997 she 

received her BA in Sociology and Anthropology and her MS in Health Care Administration in 2003. 

In 2011 Freddie completed a Certificate in Informatics in Public Health from Johns Hopkins 

Bloomberg School of Public Health. Freddie began working in Public Health as a Vaccines for 

Children Representative in Arkansas and then as the Vaccines for Children Coordinator. She is 

currently a Data Transfer Coordinator/Public Health Advisor for Scientific Technologies 

Corporation working with State IIS on interfacing with EHRs.

• Janis Betten, Oregon Immunization System (OIS)

Janis has worked in Oregon with the development of immunization forecasting logic and testing for 

use with clinical evaluation programs and school student information system immunization 

modules since the early 1990’s. Her other professional interests include all activities involved with 

Oregon school immunization law—a passion for over 30 years. 

• Gerry Bragg, MBA, Altarum Institute / Michigan Care Improvement Registry (MCIR)

Gerry Bragg has over 20 years of experience in systems analysis and programming and for the past 

15 years, has supported the Michigan Care Improvement Registry (MCIR) as a Senior Systems 

Developer. He has supported the MCIR system in a variety of capacities, including the development 

of patient de-duplication/match-merge processes and clinical decision support/immunization 

forecasting algorithms. Mr. Bragg also specializes in database/SQL performance, scalability, tuning, 

refactoring, design, technical planning, and configuration management. The system currently 

supports more than 25,000 users. Mr. Bragg holds an MBA in Management Information Systems 

from the University of Minnesota in Minneapolis, Minnesota, and a BA in Accounting from Hillsdale 

College in Hillsdale, Michigan.

• Kahil Branton, Advanced Strategies

Kahil Branton has had an 18+ year career in the Information Technology industry, with experience 

in business requirements analysis, JDA facilitation, systems architecture, software development, 

and user interface design. Kahil has facilitated groups through the development of business object 

models (aka. conceptual data models), architectural designs and business process models. Kahil also 

has extensive experience in event, location and socio-political modeling. As a Senior Consultant with 

Advanced Strategies, Inc., Kahil teaches courses on business analysis and consults with government 

and private sector organizations. He has facilitated numerous sessions for public health and 

healthcare organizations, including: The CDC, AIRA, and Hospital Corporation of America. Kahil has 

both a Master’s and Bachelor’s degree in Computer Science and Engineering from Massachusetts 

Institute of Technology.
Logic Specification for ACIP Recommendations v4.4 Page 130 of 151

• Nathan Bunker, Dandelion Software & Research, LLC

Nathan Bunker is a software developer and public health consultant for public and private agencies; 

focusing specifically on immunization software and data exchange. His work has given him 

experience with key immunization registry functions, including: immunization 

recommendation/forecast, HL7 interfacing, data quality analysis, vaccination matching, patient 

matching, and vaccine barcoding.

• John Canning, Physicians Computer Company (PCC)

• Daryl Chertcoff, BSE, HLN Consulting, LLC

Mr. Chertcoff has been providing information technology consulting services and delivering 

electronic healthcare systems to public health agencies and their partners for the past 12 years. He 

has worked with a wide range of technologies throughout his career, is an ongoing student of Health 

Information Technology standards, and believes strongly in participating in volunteer efforts to 

further the adoption of Health IT nationwide. Mr. Chertcoff offers each new business process 

analysis or development effort a combination of project management and technical leadership skills 

to get the job done. He enjoys collaborating with partners and considers each new challenge an 

opportunity to make sense of the problem in a practical manner, by drawing on experience from 

past projects as well as from involvement in standards groups and technology forums.

• Joan Christison-Lagay, Connecticut Immunization Registry and Tracking System (CIRTS)

Joan Christison-Lagay, a former Peace Corps volunteer, is a graduate of Smith College and holds 

master’s degrees from both Brown University and the UNC. She began her public health career for 

the City of Hartford, CT in 1980 working on projects to reduce the incidence of low birth weight 

infants. In 1993 she was named the director of the first immunization registry in New England, now 

known as the CT Immunization Registry and Tracking System (CIRTS). She currently contracts with 

CT DPH, MA DPH and Community Health Centers, CT on issues relating to immunization assessment 

and training. 

• Rebecca Coyle, MS Ed, American Immunization Registry Association (AIRA)

• Rachel Cunningham, MPH, Texas Children’s Hospital

Rachel M. Cunningham, MPH, is the immunization registry and educational specialist at Texas 

Children’s Hospital in the Immunization Project. Rachel is the primary author of VaccinePreventable Disease: The Forgotten Story of which more than 130,000 copies have been distributed. 

Rachel also worked with Nathan Bunker and other Immunization Project staff to develop the TCH 

Immunization Forecaster and TCH Forecast Tester. The TCH Immunization Forecaster is used 

through Texas Children’s Hospital as well as its private pediatric network, Texas Children’s 

Pediatrics (TCP), which has 48 practices throughout the greater Houston area. The TCH 

Immunization Forecaster is also currently being utilized by Indian Health Services and the Virginia 
Logic Specification for ACIP Recommendations v4.4 Page 131 of 151

Department of Health while the TCH Forecast Tester is being utilized by multiple organizations 

across the U.S. Rachel has been at Texas Children’s since 2007. She earned her Bachelor of Science 

degree from Oral Roberts University and has a master’s in public health from The University of 

Texas Health Science Center at Houston. 

• Gail DeCosta, Advanced Strategies

Gail DeCosta has had a 30+ year career in the Information Technology industry, with experience in 

business requirements analysis, JDA facilitation, software development, and project management. 

She has facilitated groups through the documentation of current business processes and the 

transformation to a desired future state of “To-Be” business process models. Additionally, Gail also 

has extensive experience in event, location, socio-political, and business object/data modeling and 

project management. Gail is employed by Advanced Strategies, Inc. and both teaches courses on 

business analysis and consults with government and private sector organizations. She has 

facilitated numerous sessions for public health and health care organizations, including: The CDC, 

AIRA, MN Department of Health, Hospital Corporation of America and the National Cancer Institute. 

Gail holds a Bachelor of Arts degree in Psychology from Brown University and a Master’s degree in 

Education from Georgia State University.

• Mark Dente, MD, General Electric (GE) Healthcare

Dr. Dente’s informatics career spans over 19 years, focusing on new approaches to increase patient

safety and creating new methods to implement evidence-based medicine. As Chief Medical Officer 

for GE Healthcare IT, his responsibilities include: Leading the organization’s clinical and Informatics 

strategy; representing GE on government, health ministries, and advocacy committees; evaluating 

and executing on strategic corporate, industry and research objectives as well as supporting GE 

Healthcare IT’s regulatory needs.

• Kristen Forney, MPH, New York Citywide Immunization Registry (CIR)

Kristen Forney is a public health professional who has led a variety of health IT projects for the 

Citywide Immunization Registry at the New York City Department of Health and Mental 

Hygiene. She has participated in the Immunization Calculation Engine (ICE) project as the lead 

analyst for New York City. As lead analyst for NYC, Kristen co-facilitated the subject matter expert 

workgroup responsible for developing and documenting the rules and test cases used to implement 

the ICE algorithm.

• Anita Geevarughese, MD, New York Citywide Immunization Registry (CIR)

Dr. Anita Geevarughese serves as the Adult Immunization Medical Specialist for the Bureau of 

Immunization at the New York City (NYC) Department of Health and Mental Hygiene. In this role, 

Dr. Geevarughese works on a variety of programmatic and policy initiatives to support 

immunizations in NYC, including improvement of healthcare personnel influenza vaccination 

coverage, development of school-located influenza vaccination programs and utilization of 

electronic health record data to create feedback reports for adult providers on practice-level 

influenza and pneumococcal vaccination coverage. Dr. Geevarughese assists in the development of 
Logic Specification for ACIP Recommendations v4.4 Page 132 of 151

both public and provider communications and offers provider education on a number of topics 

related to adult immunization. She current serves on the executive committee for the National 

Adult Immunization Coordinators Partnership and has previously served as the principal NYC 

contact for a CDC-sponsored pilot to field test the National Quality Forum measure on standardized 

reporting of healthcare personnel influenza vaccination. 

• Shaun Grannis, MD, MS, FAAFP, Regenstrief Institute / Indiana University 

Dr. Shaun Grannis is a Research Scientist at Regenstrief Institute, Inc. and Assistant Professor of 

Family Medicine at the Indiana University School of Medicine. He received an Aerospace 

Engineering degree from the Massachusetts Institute of Technology, and underwent post-doctoral 

training in Medical Informatics and Clinical Research at Regenstrief Institute. He joined Indiana 

University in 2001 and collaborates closely with national and international public health 

stakeholders to advance the technical infrastructure and data-sharing capabilities. He is a member 

of World Health Organization (WHO) Collaborating Center for the Design, Application, and Research 

of Medical Information Systems, where he provides consultancy on issues related to health 

information system identity management and implementing automated patient record matching 

strategies.

Dr. Grannis completed an analysis of an automated regional electronic laboratory reporting system 

that revealed substantial increases in the capture rates for diseases of public health significance 

when compared to manual, paper-based procedures. He is project director for an initiative 

integrating data flows from over 120 hospitals across the state of Indiana for use in public health 

disease surveillance. For the last 5 years this system has received real-time data from hospitals 

amounting to more than 2 million transactions per year, and has detected public health outbreaks 

of gastrointestinal illness, carbon monoxide poisoning, and other events of interest to public health. 

Most recently this system was leveraged to monitor H1N1 influenza disease burden across the state 

of Indiana. As co-chair of the U.S. Health Information Technology Standards Panel (HITSP) 

Population Health technical work group, Dr. Grannis helped lead development of technical 

Interoperability Specifications for nationally recognized public health IT use cases.

Dr. Grannis also serves as the Director of the Indiana Center of Excellence in Public Health 

Informatics, which recognizes that public health practice is driven by a wide variety of data types, 

data sources, and data management techniques.

• Christine Marr Gray, MPH, CHES, Virginia Immunization Information System (VIIS)

Christine Gray has been working with the Virginia Immunization Information System (VIIS) since 

March 2009. Currently as the VIIS Business Plan and Data Quality Manager, Ms. Gray develops and 

evaluates data quality standards for registry data; coordinating and executing VIIS application 

testing, proposed changes and system enhancements, immunization scheduling. Prior to this 

position, Ms. Gray was the VIIS Consultant for the South Central region of Virginia. Primarily she 

trained interested providers and other health care workers to use the registry, and acted as a liaison 

to the rest of the VIIS staff. Ms. Gray received her Master in Public Health from The George 

Washington University in 2009 and is a Certified Health Education Specialist. She graduated from 

Virginia Tech in 2004 with a Bachelor’s of Science in Economics. Before her tenure at the Virginia 
Logic Specification for ACIP Recommendations v4.4 Page 133 of 151

Department of Health, Ms. Gray worked for five years with the National Turkey Federation (NTF) 

improving worker safety and decreasing food borne illness. 

• Amy Groom, MPH, Indian Health Service (IHS)

Amy Groom is a Public Health Advisor with the Centers for Disease Control and Prevention, assigned 

to work with the Indian Health Service’s Division of Epidemiology and Disease Prevention. She has 

served as the National IHS Immunization Program manager since 2001. In this capacity,she works 

with IHS and tribal immunization programs across the country to develop immunization policy, 

implement immunization programs, and monitor immunization coverage. In addition, she is the 

lead for the development of the IHS clinical decision support software for immunizations, and 

provides training to end-users on the use of the sofwtare. She is the ex-officio representative for IHS 

on both the Advisory Committee on Immunization Practices and the National Vaccine Advisory 

Committee. She holds a Masters in Public Health from Boston University. 

• Ruth Gubernick, MPH, HLN Consulting, LLC

Ruth Gubernick is an independent consultant. For over 15 years, she has been part of a consulting 

team with HLN, LLC which has performed needs assessments regarding immunization registries in 

WA, UT, KY, NH and VT. She was a subject matter expert (SME) for registry planning in MN and LA 

and registry evaluation and enhanced development in CA, RI, OH, New York City and Philadelphia. 

Ruth has been a participant, as a SME, on the American Immunization Registry Association (AIRA)’s 

Modeling Immunization Registry Operations Workgroup (MIROW). Ruth works with the Pediatric 

Council on Research and Education (PCORE), the Foundation of the American Academy of 

Pediatrics, NJ Chapter (AAPNJ), as a Program Specialist facilitating quality improvement efforts with 

pediatric medical home teams and practice-based systems change. She is also working with the 

National AAP’s Quality Improvement Innovation Network (QuIIN) as a Quality Improvement 

Advisor. 

• Chip Hart, Physician’s Computer Company (PCC)

Chip Hart is the Director of PCC's Pediatric Solutions and author of the blog "Confessions of a 

Pediatric Practice Consultant" (chipsblog.pcc.com). Chip's two decades of pediatric practice 

management expertise have been focused on the support and development of independent 

pediatric practices. Chip spends nearly all of his time working in and with private practices around 

the country. He has worked as a consultant for the American Academy of Pediatrics (AAP) and the 

AAP Section on Administration and Practice Management (SOAPM). Chip leads educational 

seminars and consults for pediatric professionals nationwide for organizations like the AAP, state 

chapter AAP programs, the MGMA, and various physician and hospital organizations around the 

country. Chip was a member of the CCHIT Child Health Work Group and the CDC Clinical Decision 

Support working group. Chip contributes articles on practice management and health care 

information technology for Pediatric Coding Alert, the AAP's SOAPM Newsletter, and Medical Group 

Management Association.

• Mari Hilleman, Hewlett Packard (HP)
Logic Specification for ACIP Recommendations v4.4 Page 134 of 151

Mari Hilleman is a business analyst with Hewlett Packard and has been focused on statewide 

immunization information systems for 11 years. Mari has worked with five different State 

immunization programs to define requirements and test plans for the development of 

enhancements to their Immunization Information Systems. Currently Mari is supporting the Idaho 

Immunization Reminder Information System in the implementation and testing of the Wisconsin 

Immunization Evaluator module used for forecasting and evaluation of Idaho’s ACIP schedule as 

well as school and childcare eligibility.

• Robert Hopkins, Jr., MD, FACP, FAAP, American College of Physicians (ACP)

Dr. Hopkins is Professor of Internal Medicine and Pediatrics and director of the division of the 

Division of General Internal Medicine at the University of Arkansas for Medical Sciences. He has 

active teaching and faculty practices in Internal Medicine and Pediatrics at UAMS and also directs 

the Combined Internal Medicine-Pediatrics residency at UAMS. He is recognized nationally as an 

expert in adult immunization, clinical practice guidelines review and development, medical 

education and quality improvement and has published well over 100 articles on these topics. He is 

the immediate past governor of the Arkansas Chapter of the American College of Physicians and has 

served on numerous national ACP committees in addition to his roles at the University of Arkansas 

for Medical Sciences. Currently, he serves on the Adult Immunization Technical Advisory 

Committee and the ACP Performance Measurement Committee and the Arkansas Department of 

Health Vaccine Medical Advisory Committee.

• Paul Hunter, MD, American Academy of Family Physicians (AAFP)

As Associate Medical Director of the City of Milwaukee Health Department (MHD), Dr. Paul Hunter 

focuses on clinical aspects of local public health, especially immunizations, sexually transmitted 

diseases, tuberculosis, and obesity. He writes the medical orders that MHD nurses use to vaccinate 

Milwaukeeans. He represents MHD on the Wisconsin Council on Immunization Practices and on the 

Immunization Work Group of the National Association of County and City Health Officials. He helped 

develop Immunize Milwaukee! (IM!), a coalition of stakeholders from health systems, health 

departments, schools, neighborhood centers, health insurers, and others, which focuses on raising 

vaccination rates of all residents of Metro-Milwaukee. As an Assistant Professor of Family Medicine 

at the University of Wisconsin School of Medicine and Public Health, he teaches medical and public 

health students about practical aspects of implementing community health interventions. Dr. 

Hunter practiced family medicine for 19 years in underserved neighborhoods in Milwaukee and 

Rockford. 

• Janel Jorgenson, Utah Statewide Immunization Information System (USIIS)

Janel Jorgenson is a graduate of the University of Utah with a degree in Health Education & 

Promotion. She has an interest in children’s health issues and has been with the Utah Department 

of Health Immunization Program since 2000. Janel is currently the Provider Relations Coordinator 

where she provides supervision, support, training, and education for both the Utah VFC Program 

and the Utah Statewide Immunization Information System (USIIS).
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• Erin Kennedy, DVM, MPH, Centers for Disease Control and Prevention (CDC)

Dr. Erin Kennedy is a Medical Officer in the Immunization Services Division, National Center for 

Immunization and Respiratory Diseases at the Centers for Disease Control. Dr. Kennedy has a DVM 

and Masters in Anatomy and Neurobiology from Colorado State University and an MPH in 

Epidemiology from Emory University. Dr. Kennedy first joined the CDC as a fellow on the Rabies 

Team and then became an Epidemic Intelligence Service Officer in 2008 where she worked 

primarily on 2009 H1N1 pandemic influenza surveillance. Her career in public health has included 

research and policy on vaccine preventable diseases, pandemic preparedness, and improving 

coverage for recommended adult vaccines.

• Brady Kerr, RN, Texas Children’s Hospital

Brady Kerr is a graduate of the University of Utah with a bachelor’s degree in Nursing. He is 

currently working as the Health Education Nurse for the Immunization Project at Texas Children’s 

Hospital. An important part of his role in the Immunization Project is working to maintain, improve 

and promote the immunization forecaster for Texas Children’s Hospital. Previous roles have 

included caring for geriatric patients as a Home Health RN Case Manager and working as an 

Immunization Nurse for the Salt Lake County Health Department.

• Pinar Keskinocak, PhD, Georgia Institute of Technology School of Industrial and Systems 

Engineering 

Pinar Keskinocak is the Joseph C. Mello Professor in the School of Industrial and Systems 

Engineering and the co-founder and co-director of the Center for Humanitarian Logistics at the 

Georgia Institute of Technology. She also serves as the Associate Director for Research at the Health 

Systems Institute at Georgia Tech. Her research focuses on applications of operations research and 

management science with societal impact (particularly health and humanitarian applications), 

supply chain management, pricing and revenue management, and logistics/transportation. She has

worked on projects in several industries including automotive, semiconductor, paper 

manufacturing, printing, healthcare, hotels, and airlines. Her research has been published in 

journals such as Operations Research, Management Science, Manufacturing & Service Operations 

Management, Production and Operations Management, IIE Transactions, Naval Research Logistics, 

and Interfaces. 

• Alean Kirnak, Software Partners (SWP), LLC

• Chandra Klein, Envision Technology

Chandra Klein works with Envision Technology Partners, Inc. as a Subject Matter Expert. She has 

developed test cases for the forecast feature of the WebIZ immunization registry. Chandra has been 

a public health nurse for over 10 years. She has worked in many areas of public health including 

Tuberculosis Case Management, Perinatal Hep B Case Management, and Immunizations. Most 

recently she was the Immunization Program Supervisor for the Larimer County Health Department 

in Fort Collins, Colorado.
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• Nichole Lambrecht, Envision Technology Partners, Inc.

Nichole Lambrecht is a Senior Project Manager with Envision Technology Partners, Inc. and has 

been with the company for two years. Envision Technology Partners, Inc. has developed the 

immunization information system (IIS) called WebIZ in which several state and city governments 

utilize. In Nichole’s current role, she works with state and city governments to develop and manage 

their WebIZ application, as well as provides training and system quality assurance. Nichole 

previously worked with the Kansas Immunization Registry where she served a total of five years in 

all aspects of the project, including user support and Project Manager. Nichole has participated in 

several national workgroups with the Centers of Disease Control (CDC) and American Immunization 

Registry (AIRA) and she has served as a subject matter expert regarding aspects of IIS functionality 

and best practices. During this project she helped test and develop the test case toolkit.

• Carl Lauter, MD, FACP, American College of Physicians (ACP)

Dr. Carl Lauter, currently the Governor of the Michigan Chapter, American College of Physicians, 

graduated from Wayne State University and Wayne State University School of Medicine. He 

completed his residency in internal medicine followed by a NIH fellowship in infectious diseases 

and subsequently a fellowship in allergy and immunology. He is board certified in all three 

specialties. He was on the full time faculty of Wayne State University School of Medicine from 1973 

– 1980 and has been at William Beaumont Hospital, Royal Oak, Michigan, since that time. In the 

past, Dr. Lauter was an internal medicine residency director in two different programs and Chief, 

Department of Medicine at William Beaumont Hospital for ten years. He is Professor of Medicine at 

Oakland University School of Medicine and Section Head of Allergy and Immunology, as well as 

Clinical Professor of Medicine at Wayne State University. Dr. Lauter is an editorial reviewer for 

several peer reviewed journals. He is a contributor to the medical literature. At the national level 

he sits on the Immunization Technical Advisory Committee of the American College of Physicians 

and the Primary Immunodeficiency Committee and the Altered Immune Response Committee of the 

American Academy of Allergy, Asthma and Immunology. His clinical and teaching interests involve 

immunology, immunodeficiency and adverse and allergic reactions to vaccinations.

• Susan Lett, MD, MPH, Massachusetts Department of Health

Dr. Susan Lett has been the medical director of the Massachusetts Immunization Program for over 

25 years and has played a key role in the development of the Massachusetts Immunization 

Information System (MIIS). For the past 5 years, she has co-lead with Dr. Bill Adams, the MIIS 

immunization decision support team. The MIIS uses a web-service based immunization forecasting 

module (IFM) which is supported by Drs. Lett and Adams, and their technical team. The IFM 

includes forecasting rules for children and adults and also supports advanced decision support 

related to clinical features such as contraindications, immunities, and special indications. All MIIS 

IFM rules are based on ACIP recommendations. The team has also developed an extensive set of test 

cases designed to provide comprehensive, automated testing of rules. Dr. Lett is also an internist 

who has served as a voting member on the Advisory Committee for Immunization Practices 

(ACIP). She is currently active on 4 ACIP working groups: Adult Schedule, Harmonized (Childhood) 
Logic Specification for ACIP Recommendations v4.4 Page 137 of 151

Schedule, General Recommendations on Immunization and Influenza. Susan also helped to review 

phase1 of the Logic Specification for ACIP Recommendations.

• Tom Maerz, Wisconsin immunization Registry (WIR)

Tom Maerz is an Applications Developer, Computer Electronics Builder and Network Specialist by 

trade. He’s worked with Health Care records and integration with Electronic Medical Record (EMR) 

systems since 1979 and Vital Records de-duplication of information since 1990. In addition, his 

experience includes working with Health Care providers, HMO’s, Schools and EMR vendors 

regarding an Immunization Registry for the State of Wisconsin since 1995.

• Judy Merritt, Scientific Technologies Corporation (STC)

Judy Merritt is the Clinical Decision Support Specialist and Senior Developer for Scientific 

Technologies Corporation focusing on interfaces between immunization forecasting services and 

health applications. She has over 17 years’ experience with design, development, implementation 

and support of immunization systems in public health. She also served as the Immunization 

Registry Coordinator for one of the first state immunization registry systems in the nation 

implemented as an early CDC immunization registry pilot project.

• Ninad Mishra, MD, MS, CDC Public Health Informatics and Technology Program Office (PHITPO)

• Saad Omer, MBBS, MPH, PhD, Emory University Schools of Public Health & Medicine & Emory 

Vaccine Center

Dr. Saad Omer is an Assistant Professor of Global Health, Epidemiology, and Pediatrics at Emory 

University, Schools of Public Health & Medicine and an affiliate faculty of the Emory Vaccine Center. 

He has worked on studies in the United States, Guatemala, Ethiopia, India, Pakistan, Uganda and 

South Africa. Dr. Omer has conducted several studies to evaluate the roles of schools, parents, health 

care providers, and state-level legislation in relation to immunization coverage and disease 

incidence. Dr. Omer’s research portfolio includes clinical trials to estimate efficacy and/or 

immunogenicity of influenza, polio, measles and pneumococcal vaccines; studies on the impact of 

spatial clustering of vaccine refusers; and clinical trials to evaluate drug regimens to reduce motherto-child transmission of HIV in Africa. Dr. Omer is the principal investigator for the Georgia site of 

the Vaccine Safety Datalink -based at Kaiser Permanente, Georgia. He is also the principal 

investigator of a cohort study in Georgia (United States) for evaluating the impact of influenza 

vaccine receipt in pregnancy and fetal/birth outcomes. He was awarded the Maurice Hilleman 

Early-stage Investigator award in vaccinology by the National Foundation of Infectious Diseases.

• Vikki Papadouka, PhD, MPH, New York Citywide Immunization Registry (CIR)

Vikki Papadouka worked for the New York City Immunization Registry in NYC’s Department of 

Health and Mental Hygiene since 1997, and has been the director of research and evaluation since 

2003. Her work includes designing systems and protocols to ensure data quality for the IIS, working 

with internal and external agencies in collaborative research projects that use CIR data, working 
Logic Specification for ACIP Recommendations v4.4 Page 138 of 151

with clinical experts to translate immunization schedule rules into algorithms, and working with 

vendors to improve registry operations and data capture.

• Priya Rajamani, MBBS, PhD, MPH, Minnesota Immunization Information Connection (MIIC)

Sripriya Rajamani is a physician with medical training from India. She holds a public health and 

doctoral degree in Health Informatics from the University of Minnesota. She is actively involved 

with the Minnesota e-Health Initiative and staffing its Standards and Interoperability workgroup 

for the last five years. She is currently with the Minnesota Immunization Registry (MIIC) program 

as part of the EHR-IIS Interoperability grant. One of the deliverables of the MN grant is the upgrade 

of vaccine forecasting. She got interested in clinical decision support and volunteered for the 

Process, Communications and Sustainability panel of CDC Clinical Decision Support (CDS) team. 

• Shadkashara “Shad” Rajashekarappa, General Electric (GE) Healthcare

• Kim Salisbury-Keith, MBA, KIDSNET, Rhode Island Department of Health

Kim Salisbury-Keith has worked in Public Health for over 25 years. She has an undergraduate 

degree from the University of North Carolina at Chapel Hill and an MBA from the University of Rhode 

Island. Kim has worked in a variety of public health programs including WIC, Lead poisoning 

prevention, and Newborn screening. She has served as Rhode Island’s Immunization Program 

Manager and is currently the Development Manager for KIDSNET, RI’s integrated childhood 

information system. Kim was a founding member of the American Immunization Registry 

Association (AIRA) and has served as an officer and board member for that organization. She has 

also served on a variety of CDC and AIRA work groups and panels including two MIROW initiatives.

• Bobby Sanchez, New Mexico Statewide Immunization Information System (NMSIIS)

• Rob Savage, Northrop Grumman Corporation

Rob Savage has been involved in the Immunization Information Systems arena since 1989, playing 

a number of roles including system architect, developer, business analyst and technical writer. 

While working on the development of the Wisconsin Immunization Registry (WIR), he was the 

architect of the CDS engine evaluating immunization history and forecasting next doses due. He has 

been involved in HL7 standards development since 2005. He represented the American 

Immunization Registry Association for a number of years. He continues to be involved as a Northrup 

Grumman contractor to the Immunization Information Systems Support Branch at CDC. He is the 

author of the Version 2.5.1 Implementation Guide for Immunization Messaging. In this role he 

provided consultation to NIST for their development of Meaningful Use Certification. Rob is a cochair of the Public Health and Emergency Response workgroup and participates in a number of 

other work groups. Based on his experience in public health and immunization messaging, he has 

presented tutorials and seminars on the role of HL7 in supporting public health and on 

implementing Version 2.5.1 immunization messaging. 

• Mark Sawyer, MD, American Immunization Registry Association (AIRA)
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Dr. Sawyer is a Professor of Clinical Pediatrics and a Pediatric Infectious Disease specialist at the 

UCSD School of Medicine and Rady Children’s Hospital San Diego. He is the medical director of the 

UCSD San Diego Immunization Partnership, a contract with the San Diego County Agency for Health 

and Human Services to improve immunization delivery in San Diego. He is also the Past-President 

of the California Immunization Coalition and a member of the CDC Advisory Committee on 

Immunization Practices (ACIP). 

• Eric Schuh, Hewlett Packard (HP) / Oregon Immunization Program (OIP)

Eric Schuh is a business analyst with Hewlett Packard and has been focused on statewide 

immunization information systems for 11 years. During this time Eric has provided support for the 

Georgia Registry of Immunization Transactions and Services (GRITS) and is currently working with 

the Oregon ALERT Immunization Information System. While working on the Georgia and Oregon 

projects, Eric played a key role in the design, testing, and implementation of multiple upgrades to 

the immunization evaluation and forecasting tool utilized by the states. Eric is an active member of 

the WIR-based Immunization Evaluator Workgroup and the WIR Consortium. Eric was also a 

member of the Phase I Clinical Decision Support for Immunizations Expert Panel for childhood 

vaccinations.

• Richard Shiffman, MD, MCIS, Yale University School of Medicine 

• Rosalyn Singleton, MD, MPH, Alaska Native Tribal Health Consortium (ANTHC)

Dr. Rosalyn Singleton received her medical degree from Northwestern University Medical School, 

Chicago in 1982, and completed a Pediatric residency at Children’s Memorial Hospital, Chicago, and 

a MPH from Loma Linda University. During 1984-88 Dr. Singleton worked in a small Navajo hospital 

in Chinle, Arizona as a pediatrician. Since 1988 Dr. Singleton has worked as a part-time pediatrician 

at Alaska Native Medical Center, an Immunization Consultant for Alaska Native Tribal Health 

Consortium and a visiting research associate with Arctic Investigations Program – Centers for 

Disease Control and Prevention (CDC). Her research grants and publications have been in the areas 

of RSV, Hib, and Pneumococcal disease and chronic respiratory disease. 

• Shane Speciale, Avanza Systems, Inc.

Shane Speciale is the President of Avanza Systems, Inc., an immunization registry product 

manufacturer. Shane has been personally involved in the planning, design, development, 

implementation, and/or support of more than 20 immunization registries at the local, state, and 

federal (DOD) levels over the past 19 years and has intimate knowledge of and experience with 

immunization-related recommendations and clinical decision support. Shane was also a member 

of the Clinical Decision Support for Immunizations Expert Panel for childhood vaccinations in 2011 

and 2012.

• Rosemary Spence, RN, Colorado Immunization Information System (CIIS)

Rosemary Spence is a public health nurse consultant with the Colorado Immunization Section. She 

has been a nurse consultant in the Section for 14 years. Previous roles have included managing 
Logic Specification for ACIP Recommendations v4.4 Page 140 of 151

Colorado’s Vaccines for Children Program. She currently serves as the nurse consultant for the 

Colorado Immunization Information System (CIIS) and provides clinical guidance for updating the 

registry’s vaccine forecasting algorithm. Rosemary was the immunization coordinator and child 

health nursing manager at the Weld County Department of Public Health and Environment in 

Greeley, CO prior to working at the Colorado Department of Public Health and Environment.

• Amanda Timmons, Oregon Immunization Program (OIP) / ALERT Immunization Information 

System

Amanda Timmons has worked with computerized forecasting algorithms for the past twelve years; 

first in Oregon’s home-grown mmunization registry, Oregon Immunization ALERT and more 

recently, with Oregon’s new implementation of WIR. Amanda’s other professional interests include 

providing technical support to immunization providers, conducting ongoing training and learning 

whatever new skills will be required in the ever-changing world of immunization.

• Narasimha Velagaleti, Epic Systems Corporation

• Bryan Volpp, MD, Veterans Health Administration

Dr. Bryan Volpp is an Infectious Diseases Physician at the VA Northern California Healthcare System 

and the Chief Health Informatics Officer for the regional office. Dr. Volpp attended Duke University 

Medical School and did his residency and fellowship training at the University of Iowa. Dr. Volpp 

has been involved with the implementation of the VA EHR and the decision support tools in the VA 

EHR since 1994. Dr. Volpp has served on the VA/DOD National Clinical Practice Guideline Council 

and has built, tested and supported most of the existing National VA clinical reminders and all of the 

regional reminders which include reminders for many immunizations.

• Kent Ware, Ohio Statewide Immunization Information System (SIIS)

Kent Ware was privileged to lead a great team in Ohio for 26 years through many program areas 

including VFC, outbreak management, Strategic National Stockpile, Pandemic Influenza and the IIS 

program. Managing and directing these programs have been simultaneously humbling and 

rewarding, for the tasks were often daunting. Mr. Ware is now VP of Health Integration at Esah 

Health Integration Services. Working with the CDS team continues to strengthen his perspective 

that there are many talented individuals applying their skills for the betterment of public health.

• Stuart Weinberg, MD, FAAP, Vanderbilt University School of Medicine

Stuart Weinberg’s involvement with immunization registries began in 1992 with his participation 

as an informatics consultant in an "All Kids Count" Planning Grant. Dr. Weinberg also served as CoChair of the Pennsylvania Statewide Immunization Information System (SIIS) Task Force from 

1994-1997. His recent activities at Vanderbilt have included developing two-way functionalities 

between Vanderbilt's electronic medical record and Tennessee's immunization registry, and 

piloting immunization assessment and forecasting through web services. In 2012, Dr. Weinberg was 

the recipient of Tennessee's first Childhood Immunization Champion Award from the Centers for 

Disease Control and Prevention (CDC).
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• Gary Wheeler, Hewlett Packard (HP)

Communication and Education Branch (CEB) Liaison 

• Andrew Kroger, Center for Disease Control and Prevention (CDC)

Current CDSi Project Team

• Stuart Myerburg, JD, Centers for Disease Control and Prevention (CDC)

• Eric Larson, Peraton

• Patricia Speights, MPH, Peraton

• Hana Tesfamichael, Peraton
Logic Specification for ACIP Recommendations v4.4 Page 142 of 151

APPENDIX E: REFERENCES

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States. MMWR 2005;54(No. RR-16). Available at http://www.cdc.gov/mmwr/PDF/rr/rr5416.pdf

2. CDC. Addition of Severe Combined Immunodeficiency as a Contraindication for Administration of Rotavirus 

Vaccine. MMWR 2010; 59(No. 22). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5922a3.htm?s_cid=mm5922a3_e%0d%0a

3. CDC. FDA Approval of Expanded Age Indication for a Tetanus Toxoid, Reduced Diphtheria Toxoid and Acellular 

Pertussis Vaccine. MMWR 2011; 60(No. 37). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6037a3.htm?s_cid=mm6037a3_e%0d%0a

4. CDC. FDA Approval of Expanded Age Indication for a Tetanus Toxoid, Reduced Diphtheria Toxoid and Acellular 

Pertussis Vaccine. MMWR 2009; 58(No. 14). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5814a5.htm

5. CDC. FDA Licensure of Bivalent Human Papillomavirus Vaccine (HPV2, Cervarix) for Use in Females and Updated 

HPV Vaccination Recommendations from the Advisory Committee on Immunization Practices (ACIP). MMWR 

2010; 59(No. 20). Available at http://www.cdc.gov/mmwr/PDF/wk/mm5920.pdf

6. CDC. FDA Licensure of Quadrivalent Human Papillomavirus Vaccine (HPV4, Gardasil) for Use in Males and 

Guidance from the Advisory Committee on Immunization Practices (ACIP). MMWR 2010; 59(No. 20). Available at 

http://www.cdc.gov/mmwr/PDF/wk/mm5920.pdf

7. CDC. General Recommendations on Immunizations. MMWR 2011;60(No. RR-2). Available at 

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8. CDC. Haemophilus b Conjugate Vaccines for Prevention of Haemophilus influenzae Type b Disease Among Infants 

and Children Two Months of Age and Older. MMWR 1991;40(No. RR-01). Available at 

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9. CDC. Interim Recommendations for the Use of Haemophilus influenzae Type b (hib) Conjugate Vaccines Related 

to the Recall of Certain Lots of Hib-Containing Vaccines (PedvaxHIB® and Comvax®). MMWR 2007; 56(No. 50). 

Available at http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5650a4.htm

10. CDC. Licensure of 13-Valent Pneumococcal Conjugate Vaccine for Adults Aged 50 Years and Older. MMWR 2012; 

61(No. 21). Available at http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6121a3.htm

11. CDC. Licensure of a 13-Valent Pneumococcal Conjugate Vaccine (PCV13) and Recommendations for Use Among 

Children --- Advisory Committee on Immunization Practices (ACIP), 2010. MMWR 2010; 59(No. 09). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5909a2.htm

12. CDC. Licensure of a Haemophilus influenzae Type b (Hib) Vaccine (Hiberix) and Updated Recommendations for 

Use fo Hib Vaccine. MMWR 2009;58(No. 36). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5836a5.htm?s_cid=mm5836a5_e

13. CDC. Licensure of a Meningococcal Conjugate Vaccine (Menveo) and Guidance for Use --- Advisory Committee 

on Immunization Practices (ACIP), 2010. MMWR 2010; 59(No. 09). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5909a5.htm

14. CDC. Licensure of a Meningococcal Conjugate Vaccine for Children Aged 2 Through 10 Years and Updated 

Booster Dose Guidance for Adolescents and Other Persons at Increased Risk for Meningococcal Disease ---

Advisory Committee on Immunization Practices (ACIP), 2011. MMWR 2011; 60(No. 30). Available at 

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15. CDC. Measles, Mumps, and Rubella - Vaccine Use and Strategies for Elimination of Measles, Rubella, and 

Congenital Rubella Syndrome and Control of Mumps: Recommendations of the Advisory Committee on 

Immunization Practices (ACIP). MMWR 1998; 47(No. RR-8). Available at 

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16. CDC. Notice to Readers: Alternate Two-Dose Hepatitis B Vaccination Schedule for Adolescents Aged 11-15 Years. 

MMWR 2000;49(No. 12). Available at http://www.cdc.gov/mmwr/preview/mmwrhtml/mm4912a5.htm

17. CDC. Notice to Readers: FDA Approval for a Combined Hepatitis A and B Vaccine. MMWR 2001;50(No. 37). 

Available at http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5037a4.htm
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18. CDC. Notice to Readers: FDA Approval of an Alternate Dosing Schedule for a Combined Hepatitis A and B Vaccine 

(Twinrix®). MMWR 2007;56(No. 40). Available at 

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19. CDC. Notice to Readers: FDA Approval of Havrix® (Hepatitis A Vaccine, Inactivated) for Persons Aged 1 - 18 Years. 

MMWR 2005;54(No. 48). Available at 

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20. CDC. Notice to Readers: FDA Approval of VAQTA® (Hepatitis A Vaccine, Inactivated) for Children Aged >1 Year. 

MMWR 2005;54(No. 40). Available at http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5440a7.htm

21. CDC. Notice to Readers: FDA Licensure of Diphtheria and Tetanus Toxoids and Acellular Pertussis Adsorbed, 

Hepatitis B (Recombinant), and Poliovirus Vaccine Combined, (PEDIARIXTM) for Use in Infants. MMWR 

2003;52(No. 10). Available at http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5210a8.htm

22. CDC. Notice to Readers: Licensure of a Combined Live Attenuated Measles, Mumps, Rubella, and Varicella 

Vaccine. MMWR 2005; 54(No. 47). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5447a4.htm?s_cid=mm5447a4_e

23. CDC. Notice to Readers: Updated Recommendations of the Advisory Committee on Immunization Practices (ACIP) 

for the Control and Elimination of Mumps. MMWR 2006; 55(No. 22). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5522a4.htm?s_cid=mm5522a4_e

24. CDC. Notice to Readers: Revised Recommendations of the Advisory Committee on Immunization Practices to 

Vaccinate All Persons Aged 11--18 Years with Meningococcal Conjugate Vaccine. MMWR 2007; 56(No. 31). 

Available at http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5631a3.htm?s_cid=mm5631a3_e

25. CDC. Pertussis Vaccination: Use of Acellular Pertussis Vaccines Among Infants and Young Children. MMWR 

1997;46(No. RR-7). Available at http://www.cdc.gov/mmwr/PDF/rr/rr4607.pdf

26. CDC. Poliomyelitis Prevention in the United States. MMWR 2000; 49(No. RR-5). Available at 

http://www.cdc.gov/mmwr/PDF/rr/rr4905.pdf

27. CDC. Preventing Tetanus, Diphtheria, and Pertussis Among Adolescents: Use of Tetanus Toxoid, Reduced 

Diphtheria Toxoid and Acellular Pertussis Vaccines. MMWR 2006; 55(No. RR-3). Available at 

http://www.cdc.gov/mmwr/PDF/rr/rr5503.pdf

28. CDC. Prevention and Control of Influenza with Vaccines Recommendations of the Advisory Committee on 

Immunization Practices (ACIP), 2010. MMWR 2010; 59(No. RR-08). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/rr5908a1.htm

29. CDC. Prevention and Control of Influenza with Vaccines: Recommendations of the Advisory Committee on 

Immunization Practices (ACIP) - United States, 2012-13 Influenza Season. MMWR 2012; 61(No. 32). Available at 

http://www.cdc.gov/mmwr/pdf/wk/mm6132.pdf

30. CDC. Prevention and Control of Influenza with Vaccines: Recommendations of the Advisory Committee on 

Immunization Practices (ACIP), 2011. MMWR 2011; 60(No. 33). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6033a3.htm

31. CDC. Prevention and Control of Meningococcal Disease. MMWR 2005; 54(No. RR-7). Available at 

http://www.cdc.gov/mmwr/PDF/rr/rr5407.pdf

32. CDC. Prevention and Control of Seasonal Influenza with Vaccines Recommendations of the Advisory Committee 

on Immunization Practices (ACIP), 2009. MMWR 2009; 58(No. RR-8). Available at 

http://www.cdc.gov/mmwr/pdf/rr/rr5808.pdf

33. CDC. Prevention of Hepatitis A Through Active or Passive Immunization. MMWR 2006;55(No. RR-7). Available at 

http://www.cdc.gov/mmwr/PDF/rr/rr5507.pdf

34. CDC. Prevention of Pneumococcal Disease Among Infants and Children — Use of 13-Valent Pneumococcal 

Conjugate Vaccine and 23-Valent Pneumococcal Polysaccharide Vaccine. MMWR 2010; 59(No. RR-11). Available 

at http://www.cdc.gov/mmwr/pdf/rr/rr5911.pdf

35. CDC. Prevention of Pneumococcal Disease: Recommendations of the Advisory Committee on Immunization 

Practices (ACIP). MMWR 1997; 46(No. RR-08). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/00047135.htm

36. CDC. Prevention of Rotavirus Gastroenteritis Among Infants and Children. MMWR 2009; 58(No. RR-2). Available 

at http://www.cdc.gov/mmwr/PDF/rr/rr5802.pdf

37. CDC. Prevention of Varicella. MMWR 2007; 56(No. RR-4). Available at http://www.cdc.gov/mmwr/pdf/rr/rr5604.pdf
Logic Specification for ACIP Recommendations v4.4 Page 144 of 151

38. CDC. Quadrivalent Human Papillomavirus Vaccine. MMWR 2007; 56(No. RR-2). Available at 

http://www.cdc.gov/mmwr/PDF/rr/rr5602.pdf

39. CDC. Recommendation of the ACIP for Use of Quadrivalent Meningococcal Conjugate Vaccine (MenACWY-D) 

Among Children Aged 9 Through 23 Months at Increased Risk for Invasive Meningococcal Disease. MMWR 2011; 

60(No. 40). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6040a4.htm?s_cid=mm6040a4_e%0d%0a

40. CDC. Recommendations for Use of Haemophilus b Conjugate Vaccines and a Combined Diphtheria, Tetanus, 

Pertussis, and Haemophilus b Vaccine. MMWR 1993;42(No. RR-13). Available at 

http://www.cdc.gov/mmwr/PDF/rr/rr4213.pdf

41. CDC. Recommendations on the Use of Quadrivalent Human Papillomavirus Vaccine in Males - Advisory Committee 

on Immunization Practices (ACIP), 2011. MMWR 2011; 60(No. 50). Available at 

http://www.cdc.gov/mmwr/pdf/wk/mm6050.pdf

42. CDC. Recommended Immunization Schedules for Persons Aged 0 through 18 years - United States, 2011. MMWR 

2011;60(No. 5). Available at http://www.cdc.gov/mmwr/pdf/wk/mm6005.pdf

43. CDC. Recommended Immunization Schedules for Persons Aged 0 through 18 years - United States, 2012. MMWR 

2012;61(No. 5). Available at http://www.cdc.gov/mmwr/pdf/wk/mm6105.pdf

44. CDC. Report from the Advisory Committee on Immunization Practices (ACIP): Decision Not to Recommend Routine 

Vaccination of All Children Aged 2--10 Years with Quadrivalent Meningococcal Conjugate Vaccine (MCV4). MMWR 

2008; 57(No. 17). Available at http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5717a4.htm

45. CDC. Update: Recommendations from the Advisory Committee on Immunization Practices (ACIP) Regarding 

Administration of Combination MMRV Vaccine. MMWR 2008; 57(No. 10). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5710a3.htm?s_cid=mm5710a3_e

46. CDC. Updated Recommendation from the Advisory Committee on Immunization Practices (ACIP) for Use of 7-

Valent Pneumococcal Conjugate Vaccine (PCV7) in Children Aged 24--59 Months Who Are Not Completely 

Vaccinated. MMWR 2008; 57(No. 13). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5713a4.htm?s_cid=mm5713a4_e

47. CDC. Updated Recommendations for Use of Haemophilus influenzae Type b (Hib) Vaccine: Reinstatement of the 

Booster Dose at Ages 12-15 Months. MMWR 2009; 58(No. 24). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5824a5.htm?s_cid=mm5824a15e

48. CDC. Updated Recommendations for Use of Meningococcal Conjugate Vaccines --- Advisory Committee on 

Immunization Practices (ACIP), 2010. MMWR 2011; 60(No. 03). Available at 

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6003a3.htm

49. CDC. Updated Recommendations for Use of Tetanus Toxoid, Reduced Diphtheria Toxoid and Acellular Pertussis 

(Tdap) Vaccine from the Advisory Committee on Immunization Practices, 2010. MMWR 2011; 60(No. 01). Available 

at http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6001a4.htm

50. CDC. Updated Recommendations of the Advisory Committee on Immunization Practices (ACIP) Regarding Routine 

Poliovirus Vaccination. MMWR 2009; 58(No. 30). Available at http://www.cdc.gov/mmwr/PDF/wk/mm5830.pdf

51. CDC. Use of Combination Measles, Mumps, Rubella and Varicella Vaccine. MMWR 2010; 59(No. RR-3). Available 

at http://www.cdc.gov/mmwr/pdf/rr/rr5903.pdf

52. CDC. Use of Diphtheria Toxoid-Tetanus Toxoid-Acellular Pertussis Vaccine as a Five-Dose Series. MMWR 

2000;49(No. RR-13). Available at http://www.cdc.gov/mmwr/preview/mmwrhtml/rr4913a1.htm

53. CDC. U.S. Vaccine Abbreviations---Advisory Committee on Immunization Practices (ACIP), 2012. Available at 

http://www.cdc.gov/vaccines/acip/committee/guidance/vac-abbrev.pdf
Logic Specification for ACIP Recommendations v4.4 Page 145 of 151

APPENDIX F: SUPPLEMENTAL MATERIAL
Logic Specification for ACIP Recommendations v4.4 Page 146 of 151

APPENDIX G: DOCUMENT MANAGEMENT

Date Changed By Comments Version 

#

8/31/12 L. McKenzie, 

E. Larson

Draft distributed to Expert Panel and Reviewers 0.1

10/05/12 L. McKenzie, 

E. Larson

Final Draft distributed to CDC leadership 0.2

10/29/12 L. McKenzie, 

E. Larson

Initial publication 1.0

11/14/12 L. McKenzie, 

J. Wain

Updated Executive Summary (1.3 and 1.4)

Updated to meet section 508 requirements

1.1

01/09/13 J. Wain Fixed minor errors in Acknowledgements Appendix 1.2

09/19/13 E. Larson Select Best Patient Series language clarifications

o Sections 6.1, 6.2, 6.3, 6.5, and 6.6

Select Best Patient Series Decision Table correction

o Section 6.3

Updated Date Calculation Intervals to define intervals to only be from Valid or 

Not Valid doses. Substandard doses do not need an interval.

o Section 3.4

Assessment date was added to the domain model and a typo was corrected in 

the definition of the term assessment date

o Appendix A

Evaluation and Forecasting for Skipping Doses were updated to incorporate a 

Trigger Interval in addition to the existing Trigger Age to address issues found 

while testing polio, guidance from EIPB, and the harmonized schedule.

o Sections 3.4, 4.2, 5.1, Appendix A

Updated business rule numbers to an improved identification scheme for 

referencing business rules and improved ability to insert newly needed business 

rules in the future.

o Sections 3.4, 5.4, 6.7, 7.3, 7.4, 7.6

Minor wording updates in various business rules to improve clarity and ability to 

implement

o Section 3.4

1.3

11/07/13 E. Larson Updates to properly select the catch-up schedule when children start late by 

age. A new concept (Maximum Age To Start) was defined in the appendix and 

added to the select best patient series logic.

o Sections 6.1, 6.5, Appendix A

Added new appendix to address multiple paths to immunity concept as 

supplemental material and references to the new appendix in various sections.

o Sections 2.1, 2.8, Appendix E

Updates to Forecast sections regarding Conditional Need. The logic remained 

the same as previously but moved Conditional Need into its own section (New 

section 5.3) and added a specific target dose status for improved clarity on the 

use of conditional need.

o Changes to Sections 3.2, 5, 5.3 (New), 5.4 (previously 5.3)

Document editorial consistency improvements

o Entire document

1.4

01/09/14 E. Larson Evaluation and Forecasting for Skipping Doses were updated to incorporate a 

Trigger Target Dose to address issues found while testing Tdap/Td, guidance 

from EIPB, and the harmonized schedule.

o Sections 4.2, 5.1, Appendix A

Identify and Evaluate Vaccine Group (Chapter 7) was refactored to apply a 

cleaner process model, decision tree, and business rules based on Tdap/Td and 

MMR testing and research.

o Chapter 7

1.5

03/20/14 E. Larson Updated inconsistencies found in Supplemental Material graphics.

o Appendix E

1.6
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Added Business Rule to Calculate Dates to ensure consistent application of 

date calculations

o Section 3.4 – See CALCDT-6 Business Rule

08/14/14 E. Larson Updated definition of Maximum Age to Start

o Section 6.1

Added/improved diagrams and process models

o chapters 4, 5, 6, 7, 8 and appendices

Updated attribute tables to cross-reference with date calculation business rules

o chapters 4 and 5

Added a new Patient Series Status and associated usage of new “Aged Out” 

status.

o Section 3.2 and chapters 5 and 6

Improved decision table and business rule language to fully utilize vocabulary.

o Chapters 5 and 6

Assigned Patient Series Status to outcomes section of decision table.

o Section 5.4

New Evaluation section was added to accommodate clarifications from EIPB on 

Hep A intervals after a not valid dose.

o Section 4.6 was created (Allowable Interval).

o Other updates due to this were in the chapter 4 process model, 

section 4.11, and Appendix A.

1.7

12/16/14 E. Larson Added support for maximum doses by age (i.e.: 6 doses by 7 years in DTaP

o Section 5.1 and Appendix A

1.8

05/11/15 P. Speights,

E. Larson

Added Zoster to the Vaccine Groups in Table 1-1

Added Age base Adult Recommendations to the Additional Items in scope 

include.

Added Not Recommended status and definition in Table 3-3

Updated Table 3.5 to include business rule CALCDTINT-8, CALCDTCOND-1, 

and CALCDTCOND-2

Added From Most Recent explanation under the Relationship to ACIP 

Recommendation in Section 4.5

Added Figure 4-10 From Most Recent timeline in section 4.5

Added Supporting data “From Most Recent” to table 4-11

Updated the Activity and Goal in Table 5-1 to incorporate sections 5.4

Updated the processing model in Figure 5-1 to add section 5.4

Added the new Immunity section in section 5.4

Updated Table 5-7 to add supporting data for Begin and End Age Date.

Updated Table 5-14 to add Not recommended status info.

Added the term Minimum Age to Start Date and Definition in table 6.2

Updated Table 7-3 to add business rule SINGLEANTVG-10

Updated Table 7-4 to add business rule MULTIANTVG-9

Updated the Figure 8-2

Updated the Domain models Figure A-1, Figure A-2, and Figure A-3

Added new terms and definitions to the Table A-1 Glossary section. Included 

are Conditional begin age, Birth Date Immunity, Clinical History Immunity, 

Country of Birth, Conditional End Age, Exclusion Condition, Forecast Vaccine 

Type, From Most Recent, Immunity Date, Immunity Guideline, Minimum Age to 

Start, and Recommended Vaccine,

2.0

12/22/15 C. Newman,

P. Speights,

E. Larson

Updated the fourth paragraph in Background and Goals 1.1.

Updated text in section 2.4.

Updated text in the first paragraph of section 3.1.

Removed Substituted from Target Dose Statuses Table 3.2

Updated Supporting Data text in 3.3

Updated Logical Component Date Rules in Table 3-5

Added Table 3-8 What Exercises Should I do today in section 3.5.

Updated Evaluation Process Steps in Table 4-1

Updated Evaluation Process Model in Figure 4-1

2.1
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Removed Skip Target Dose section and replaced with Evaluate Conditional Skip 

in section 4.2.

Removed Substitute Target dose section

Updated Evaluate Interval in Section 4.4.

Updated Live Virus Conflict Business Rules in Table 4-23 of section 4.6

Updated Forecast Dates and Reasons Process steps in Table 5-1.

Updated Forecast Dates and Reason Process Model in Figure 5-1.

Replaced Skip Target dose with Evaluate Conditional Skip in section 5.1.

Removed Substitute Target Dose section.

Updated text in Determine Evidence of Immunity section 5.2.

Updated decision table 5-3: “Does the patient have evidence of immunity?”

Updated Generate Forecast Date and Recommended Vaccine Business rules in 

Table 5-7.

Updated Select Best Patient Series Vocabulary/Definition in Table 6-2.

Updated Select Best Patient Series Business Rules in Table 6-8.

Updated Organize Immunization History Process Model in Figure 8-2.

Updated the CDSI Domain Diagram: Patient Neighborhood in Figure A-1.

Updated the CDSI Domain Diagram: Vaccine and Schedule Neighborhood in 

Figure A-2.

Updated the CDSI Domain Diagram: Evaluation and Forecasting Neighborhood 

in Figure A-3.

Updated the Glossary in Table A-1.

Added PPSV to the Acronym’s and Abbreviations in Appendix B.

6/20/16 C. Newman,

P. Speights,

E. Larson

Expanded scope to include coded contraindications and series based on patient 

risk

Updated description of CDSi resources in Section 1.4

Moved Processing Model description from Chapter 8 to Chapter 4

Updated Patient Series descriptions to include Relevant, Scorable, Prioritized 

and Best Patient Series

Enhanced Create Patient Series discussion in the Processing Model

Inserted a new Create Relevant Patient Series chapter (Chapter 5)

Inserted a new Evaluate for Inadvertent Vaccine section (Section 6.3)

Updated references to Interval to Preferable Interval to distinguish from 

Allowable Interval

Updated From Most Recent interval type accommodate a list of CVX codes in 

Section 6.5

Added a new interval type of From Relevant Observation in Section 6.5

Moved Evaluate Gender logic from Chapter 6 to Chapter 5

Inserted a new Determine Contraindications section (Section 7.3)

Updated Select Patient Series Business Rules (Table 8-2)

Inserted a new Pre-Filter Patient Series section (Section 8.2)

Enhanced Patient Series selection in Chapter 8 to include Series Groups

Split the Vaccine and Schedule neighborhood into separate Schedule and 

Series neighborhoods in Appendix A

Updated Table A-1 Glossary

Created new Appendix to contain the new Retired Items table

3.0

02/22/19 C. Newman, 

P. Speights, 

E. Larson

Added support for Historical Recommendation Supporting Data

Added support for conditional skip context to control when skipping occurs (e.g., 

only in evaluation, only in forecast, both evaluation and forecast)

Updated the location of the business rules in Chapter 8

Updated the description of the Supporting Data

Updated processing model in section 4.6 (Identify and Evaluation Vaccine 

Group)

Added section 7.6 for validating recommendations

Updated Decision Table 6-8

Updated CALCDTSKIP-5

4.0
Logic Specification for ACIP Recommendations v4.4 Page 149 of 151

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Consolidated SELECTSCORE-1 and SELECTSCORE-2 into one rule to 

eliminate unnecessary referencing. This also resulted in the retirement of a term 

(Exceeded Maximum age to start).

06/02/20 P. Speights, 

E. Larson

Substantive Improvements

The following rules and decision tales were improved based on implementer 

feedback and will result in improved consistency/accuracy.

• FORECASTDT-1: Added additional date for calculating the earliest date

• MULTIANTVG-1: Better logic for selecting logical dates for forecasting

• MULTIANTVG-8: Better logic for selecting antigens needed

• SELECTBEST-2: Better logic to define how to break a tie

• SELECTSCORE-2: The previous version was ambiguous with regards to 

starting age requirements and was incorrectly excluding series which should 

not have been excluded.

• Evaluate Age and Evaluate Preferable Interval Decision Tables were refined 

to allow grace period to be used following an invalid dose if it has been at 

least 1 year.

Additional Improvements

The following improvements will improve the overall consistency of the Logic 

Specification, but should not change any rules, decisions, or processing. The 

domain model is was redrawn using a new tool which gives it a new look. A new 

section was added in Appendix A entitled “How to read the Domain Model 

Diagram”. As part of this process improvements were made to the domain 

model, terminology, and definitions. These improved terms were then used 

within the logic specification rules and decision tables.

4.1

06/29/21 P. Speights,

E. Larson

The following rules and decision tales were improved based on implementer 

feedback and will result in improved consistency/accuracy.

• CALCDTLIVE-2: Added additional rule text for doses not yet evaluated.

• CALCDTLOTEXP-1 Added new logic for lot expiration dates when only 

month and year known.

• MULTIANTVG-8: Reverted to version 4.0 business rule.

• SELECTSCORE-2: Added additional logic for Contraindicated series and 

Evaluation Only series

• SELECTB-24: Created new rule for use in SELECTSCORE-2 related to 

contraindicated series

• Table 8-8: “How Many Points Are Awarded to a Complete Patient Series?” 

When 2 or More Scorable Patient Series are Complete, the scoring table 

was simplified (now a single condition). This was to address issues in 

selecting the proper series in 65+ patients receiving PPSV followed by PCV.

This can also happen in other antigens and will change previous evaluation 

status from “extraneous” to “valid” in cases where a patient now fits into a 

better series with the most valid doses. Evaluating these doses as “Valid” 

vs. “Extraneous” is preferable since ACIP recognizes those administration 

patterns (e.g., HepB 4-dose vs. HepB 3-dose).

• Table 7-9: “Should the Patient Receive Another Target Dose?” was updated 

to change the flu outcome to “Not Recommended” when the seasonal end 

date is past.

• Table 6-4: “Conditional Skip Attributes” was updated to include an 

“Assumed Value if Empty” values for CALDTSKIP-3 and CALCDTSKIP-4.

• Table 5-4: “Is the Series Relevant for the Patient?” was updated to add 

Evaluation Only Series text to the second condition.

• Updated the Glossary in Table A-1

• “Evaluation Only” series were created as a new series type. This will enable 

support for Non-FDA authorized COVID-19 vaccines that are authorized by 

WHO and can count towards U.S. vaccination. The impact on the logic can 

be found in Chapters 5 (Table 5-4) and 8 (SELECTSCORE-2).

4.2
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• Select Patient Series Process Model (beginning of chapter 8) was updated 

to improve clarity regarding the last step in the process (Determine Best 

Patient Series).

09/10/21 P. Speights,

E. Larson

The following decision table was improved based on implementer feedback and 

will result in improved consistency/accuracy.

• Table 8-14: “Is the Series a Best Patient Series? was updated to improve 

selection of the best patient series by adding a new rule for “Evaluation 

Only” series.

4.3

01/11/24 P. Speights,

E. Larson

Overarching Updates Impacting All Chapters

• Improved relationship between the Domain Model and the usage of the 

domain model terms in rules and decision tables throughout the Logic 

Specification

o This includes better use of Antigen Series, Relevant Patient Series, 

Scorable Patient Series, Prioritized Patient Series, and Best Patient 

Series. Numerous references to “series” have been updated to be 

more specific to which type of series. See Chapter 4 for a graphic of 

these terms (Figure 4.2).

• Simplified some decision tables with a “Default Outcome”. The default 

outcome is used when no conditions are met. This reduces the number of 

columns in decision tables.

• Created consistent terminology always ending in “Flag” for terms which can 

only have a value of Yes or No (or n/a).

o Note: these have not been applied to supporting data yet. That will 

happen in version 5.0.

Chapter 3 Logic Specification Concepts

• Rule improvements (for clarity) throughout. None had a functional change 

in the outcome.

• A few were retired (see Appendix C) due to lack of use and the Live Virus 

rework in Chapter 6.

Chapter 4 Processing Model

• Processing Models and other graphics were updated to align with other 

chapters.

Chapter 5 Create Relevant Patient Series

• Rule and decision table improvements (for clarity) throughout. None that 

had a functional change in the outcome.

Chapter 6 Evaluate Vaccine Dose Administered

• Rule and decision table improvements (for clarity) throughout. None that 

had a functional change in the outcome.

• Section 6.7 (Evaluation Vaccine Conflict) was completely revamped to be 

less process driven and more business rule driven.

• Section 6.7 also moved from “Live Virus Conflict” to “Vaccine Conflict” to 

support more conflicts than just live virus. 

• New terms Conflicting Vaccine Type and Impacted Vaccine Type have 

been created. These replace the previously used live virus conflict terms 

Previous Vaccine Type and Current Vaccine Type. Please see the 

Implementer Note in Section 6.7 regarding the Logic Specification terms 

and Supporting Data terms for more information.

Chapter 7 Forecast Dates and Reasons

4.4
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• Rule and decision table improvements (for clarity) throughout. The next 

bullets describe the biggest changes.

• Section 7.3 (Determine Contraindications) expanded the decision table to 

be more explicitly tied to the domain model by adding references to 

Adverse Events along with Active Patient Observations. 

• Section 7.6.2 (Validate Forecasted Dates Business Rules) was removed. 

This logic is now in section 7.4 (Determine Forecast Need) with the 

creation of FORECASTDTCAN-1 and included in the Decision Table. This 

new term is also used in section 7.5 (Generate Forecast Dates). This 

should not result in a functional change.

• Introduction of the new rule (FORECASTDN-1) to standardize the forecast 

dose number calculation across the community. This is also referenced in 

Chapter 9.

Chapter 8 Select Best Patient Series

• Rule and decision table improvements (for clarity) throughout. The next 

bullets describe the biggest changes.

• SELECTSCORE-2 introduced a new condition for situations when no 

series in a Standard series group is identified as a default series.

• The decision table in Section 8.2 (Identify One Prioritized Patient Series) 

was refactored to simplify the conditions column so each condition is a 

distinct question with a count of series for an answer. The functional 

outcome should be identical to the previous decision table in version 4.3.

Chapter 9 Identify and Evaluate Vaccine Group

• Refactored this chapter to generalize many rules which could be applied to 

both Single Antigen and Multiple Antigen series. This should not result in a 

functional change.

• Introduction of new rule (FORECASTDN-2) to standardize the forecast 

dose number calculation across the community. 

Appendix A Domain Model and Glossary

• Several improvements throughout all neighborhoods which are now 

integrated into the rules and decision tables throughout the logic 

specification.

• Split the Evaluation and Forecast Neighborhood into two neighborhoods. 

One for Evaluation and one for Forecast for readability purposes.

• Introduced Definitional Rules to help describe the relationship between two 

terms on a domain model. These help set expectations (e.g., A vaccine 

dose administered must be received by exactly one patient)