NISTIR 7684 

U.S. DEPARTMENT OF COMMERCE

Gary Locke, Secretary

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY

Patrick D .Gallagher, Director

Modeling and Simulation for 

Emergency Management and Health 

Care Systems: Workshop Summary 

Susan O’Hara, RN, MPH 

O’Hara HealthCare Consultants, LLC

735 Hemenway Street

Marlborough, MA 01752 

Charles R. McLean 

Y. Tina Lee 

Manufacturing Systems Integration Division

Manufacturing Engineering Laboratory

National Institute of Standards and Technology

Gaithersburg, MD 20899 

April 21, 2010
2

ACKNOWLEDGMENTS 

Workshop sponsors: 

National Institute of Standards and Technology (NIST) 

U.S. Department of Homeland Security (DHS) 

SPARTA, Inc. 

AEgis Technologies 

Workshop Organizing Committee: 

Charles R. McLean, NIST 

Susan O’Hara, O’Hara HealthCare Consultants, LLC 

Swee Leong, NIST 

Sanjay Jain, George Washington University/ NIST 

Mark Sullivan, Mark Sullivan Architects 

A Dedication 

To 

Mark Sullivan (1950-2009) 

The work I have done on the NIST symposium would never have been possible without the knowledge, 

enthusiasm, and support of Mark Sullivan, principal and founder of Mark Sullivan Architects. Mark died quite 

suddenly before he could be properly cited as the champion he was for furthering the integration of architecture as 

a critical patient care variable. When I began working for him in 1993, he had a vision that together we could 

improve the way patient care spaces were designed, not only for the patients, but for the nurses and other staff 

working in those spaces. 

Mark always pushed to make sure architecture was represented in an evidenced-based and regulatory manner in 

our simulation models. He helped us shift paradigms with his work on space commissioning and his futuristic 

thinking and planning on how to combine the architectural software with modeling software. He was my 

colleague, friend, and husband. The work behind the scenes to make the symposium successful would not have 

been accomplished without him. I hope I have represented his work in the field of architecture in the best possible 

way. 

Susan O’Hara 
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PREFACE 

The National Institute of Standards and Technology (NIST) is working with the U.S. Department of Homeland 

Security (DHS) to establish modeling and simulation (M&S) technical interest groups (TIGs) with technical experts 

from the various M&S domains associated with homeland security. The TIGs will share information, promote crossfertilization of ideas, identify community research and standards needs, review plans, and help to establish consensus 

on a number of technical issues. The proposed objectives of the TIGs are listed below. 

 Refinement and/or expansion of definition of the technical scope for each TIG within the general guidelines 

that have been established 

 Specification/ratification of simulation user needs and modeling requirements 

 Recommendation of appropriate modeling techniques for specific domains 

 Identification of: 

o subject matter experts 

o relevant existing models and tools 

o data sources and reference data sets 

o guidelines, methods, specifications, and standards 

o best practices 

 Recommendations for M&S evaluation approaches such as verification, validation, and accreditation 

The initial set of M&S TIG domains include: 

 Incident Command 

 Hazardous Material Release 

 Critical Infrastructure 

 Health Care 

A workshop on “Modeling and Simulation for Emergency Management and Health Care Systems” was held from July 

24 to July 25, 2008, sponsored by the National Institute of Standards and Technology (NIST). The workshop is the 

first TIG meeting on Health Care. 

This document provides a summary of the talks and working sessions during the “Modeling and Simulation for 

Emergency Management and Health Care Systems” workshop that was held on July 24 and July 25, 2008. We elected 

to provide a summary including the key points of the talks instead of including the presentation materials. We believe 

the readers will find this more useful. 

For the six working sessions, we have included the summaries as provided by the group representatives for the 

sessions. Care has been taken to capture the presenters’ material as it was presented, and we have refrained from 

adding any new material. Presenters were invited to submit other relevant material for this report. A number of 

acronyms have been expanded in this material for better readability. The acronyms and abbreviations are also listed in 

Appendix D for reference. 

While we have made every effort to summarize the talks and working sessions without losing the key message, 

readers and presenters are invited to send their comments, and/or suggestions for improvement of this document to 

simresponse@cme.nist.gov by September 30, 2010. 

Appendix A of this document identifies organizations with a stake in health care simulation. Appendices B and C 

identify relevant existing and evolving standards. The information on standards has been collected using Internet 

searches and information provided by attendees of the workshop. The information presented for each standard has 

been adapted from referenced websites. Readers are invited to send information on additional standards, and 

suggested revisions on the information currently included to simresponse@cme.nist.gov by September 30, 2010. An 

updated version of this report may be prepared by December 31, 2010, if the feedback received warrants such a 

revision. 
4

We would like to thank all the speakers who shared their valuable knowledge and time, all the participants in the 

workshop for providing their contribution through the working sessions, and the facilitators of the working sessions 

for capturing the discussions and presenting them to all attendees. We thank the vendors who provided displays of 

relevant tools and techniques during the workshop. We would also like to thank all the sponsors of this workshop 

including: NIST, DHS, SPARTA, Inc, and AEgis Technologies. Many people contributed to the success of the 

workshop and it will be hard to list everyone. We would specifically like to acknowledge the efforts of the workshop 

organization committee who worked with the authors including: Swee Leong, Sanjay Jain, and Mark Sullivan.

Additional persons we need to thank include Jim Fackler, MD, Holger Hansen, MD, MPH, DrPH, F.A.C.E., and 

Joshua Kilbridge. 

 Susan O’Hara 

Charles R. McLean 

Y. Tina Lee 
5

TABLE OF CONTENTS 

ACKNOWLEDGMENTS............................................................................................................................................. 2

PREFACE ...................................................................................................................................................................... 3

1 EXECUTIVE SUMMARY.................................................................................................................................. 9

2 OPENING REMARKS: CHARGE TO THE GROUP - MODELING AND SIMULATION FOR 

EMERGENCY PREPAREDNESS: A NEEDS ANALYSIS OVERVIEW ............................................................ 12

3 KEYNOTE ADDRESSES ................................................................................................................................. 14

3.1 KEYNOTE ADDRESS – DAY 1: MERGING COMMUNITY, EMERGENCY PLANNING WITH

EMERGENCY DEPARTMENT HEALTH CARE SYSTEMS: THE FUTURE OF SIMULATION ...................... 14

3.2 KEYNOTE ADDRESSES – DAY 2........................................................................................................... 16

3.2.1 KEYNOTE ADDRESS – DAY 2, PART 1: THE TIME AND MOTION STUDY: HOW DO MEDICALSURGICAL NURSES SPEND THEIR TIME?...................................................................................................... 16

3.2.2 KEYNOTE ADDRESS – DAY 2, PART 2: MODELING THE TIME AND MOTION STUDY ............... 18

4 ENHANCING OPERATIONAL READINESS BY LEVERAGING MODELS, SIMULATION, AND 

DECISION-MAKING TOOLS: PERSPECTIVES FROM THE FIELD .............................................................. 20

4.1 DEFINING THE BUSINESS CASE .......................................................................................................... 20

4.2 EXPLORING A SOLUTION ..................................................................................................................... 22

5 THE USE OF HI-FIDELITY SIMULATION FOR PEDIATRIC EMERGENCY TRAINING FOR 

MASS CASUALTY INCIDENT EXERCISES......................................................................................................... 24

6 MODELING AND SIMULATION INTEROPERABLE TOOLSET FOR HEALTH CARE SYSTEMS 

ANALYSIS................................................................................................................................................................... 26

7 THE NEXT INFLUENZA PANDEMIC AND MODELING: THE NEED TO KEEP IT SIMPLE .......... 28

8 APPROACHES TO MODEL AND SIMULATION DEVELOPMENT AND EVALUATION IN 

FEDERAL AGENCIES .............................................................................................................................................. 30

9 SIMULATION’S POTENTIAL ROLE IN EMERGENCY MEDICAL CARE SYSTEMS AND 

MEDICAL EMERGENCY PREPAREDNESS........................................................................................................ 32

10 SUSTAINABLE MODELING? SELECT FOR SUCCESS ........................................................................... 34

11 MODELING AND OPTIMIZING THE PUBLIC HEALTH INFRASTRUCTURE FOR EMERGENCY 

RESPONSE.................................................................................................................................................................. 36

11.1 OPTIMIZING HEALTH CARE RESOURCES FOR EFFICIENT USAGE, AND INTEGRATION

WITHIN AN EMERGENCY RESPONSE CONTEXT FOR LARGE-SCALE POPULATION PROTECTION .... 36

11.2 EMERGENCY DEPARTMENT AND HOSPITAL THROUGHPUT MODELING................................. 38

12 EMERGENCY MANAGEMENT .................................................................................................................... 40

12.1 TOWARDS A SIMULATION AND GAMING FRAMEWORK FOR INCIDENT MANAGEMENT..... 40

12.2 WALKING ON BROKEN GLASS – A CASE FOR DEVELOPING STANDARDS IN EMERGENCY PREPAREDNESS.. 42

12.3 LEVERAGING UBIQUITOUS TECHNOLOGIES FOR REAL-TIME DISASTER SIMULATION ................................. 44

13 HEALTH CARE SYSTEMS............................................................................................................................. 46

13.1 TRENDS IN ED DESIGN – ARCHITECTURAL PLANNING AND SIMULATION MODELING INTEGRATION .......... 46

13.1.1 From Feasibility Studies to Programming to Post-Occupancy – Patient Safety Perspective (Patient 

Safety and the Need for Simulation Modeling) .................................................................................................... 46

13.1.2 FROM FEASIBILITY STUDIES TO PROGRAMMING TO POST-OCCUPANCY – BUILDING 

DESIGN AND TECHNOLOGY PERSPECTIVE ................................................................................................. 48

13.2 BEST PRACTICES FOR SEMANTIC DISCOVERY AND LEVERAGE OF ENTERPRISE DATA IN SIMULATIONS ..... 50

13.3 BACK TO THE FUTURE: THE ONGOING NEED TO PROMOTE SIMULATION MODELING TO HEALTH CARE 

DECISION MAKERS .................................................................................................................................................... 52
6

14 PREPARING FOR WHEN THE RUBBER MEETS THE ROAD: ALIGNING SIMULATION AND 

TECHNOLOGY FOR DISTRIBUTED HIGH-ACUITY PATIENT CARE......................................................... 54

15 BREAKOUT SESSIONS/CHARGE TO THE GROUP: CASE STUDY GROUP REPORTS AND 

RECOMMENDATIONS ............................................................................................................................................ 56

15.1 PUBLIC HEALTH DATA REQUIREMENTS FOR MODELING AND SIMULATION......................... 57

15.2 DATA REQUIREMENTS FOR MODELING AND SIMULATION IN EMERGENCY MANAGEMENT

AND HEALTH CARE SYSTEMS........................................................................................................................... 58

15.3 FACILITIES-BASED MODELING AND SIMULATION PLANNING FOR DISASTER

PREPAREDNESS .................................................................................................................................................... 60

15.4 SPECIFICATIONS FOR MODELING, SIMULATION, AND GAMING................................................ 61

15.5 MODELING AND SIMULATION NEEDS ASSESSMENT FOR HEALTH CARE SYSTEMS –

CLINICAL NEEDS .................................................................................................................................................. 63

15.6 MODELING AND SIMULATION NEEDS ASSESSMENT FOR HEALTH CARE SYSTEMS –

ENGINEERING NEEDS.......................................................................................................................................... 65

16 CONCLUSION................................................................................................................................................... 66

APPENDIX A. MEDICAL AND HEALTH CARE ORGANIZATIONS .............................................................. 67

APPENDIX B. RELEVANT HEALTH CARE STANDARDS AND EFFORTS .................................................. 69

Reference resource............................................................................................................................................... 72

Operational guidelines......................................................................................................................................... 72

APPENDIX C. RELEVANT ARCHITECTURE STANDARDS AND EFFORTS............................................... 73

APPENDIX D. ACRONYMS AND ABBREVIATIONS ......................................................................................... 81
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FIGURES 

Figure 2-1: Simulation System Needs .............................................................................................13 

Figure 3-1: Preparedness Cycle .......................................................................................................15 

Figure 3-2: Data Collection Overview.............................................................................................17 

Figure 3-3: Architecture + Clinicians + Engineering = Whole System Design ..............................19 

Figure 4-1: Support the Planning and Exercise Cycle.....................................................................21 

Figure 4-2: Opportunity for Integration with M&S.........................................................................23 

Figure 5-1: Lifelike Mannequin Functions......................................................................................25 

Figure 6-1: Pyramid Construct to Organize Domain Toolset..........................................................27 

Figure 7-1: When Will the Next ‘Flu Pandemic Occur? Time between Start of Pandemics ..........29 

Figure 8-1: Economics of Modeling & Simulation .........................................................................31 

Figure 9-1: Federal Emergency Preparedness .................................................................................33 

Figure 10-1: RIGHT – On Latest Thinking .....................................................................................35 

Figure 11-1: Infrastructure of RealOpt – A Tool for Emergency Operational & Strategic Planning

..........................................................................................................................................................37 

Figure 11-2: Patient Care Throughput .............................................................................................39 

Figure 12-1: System Reference Architecture Concept ....................................................................41 

Figure 12-2: To Stay or Leave? .......................................................................................................43 

Figure 12-3: Accident Simulation....................................................................................................45 

Figure 13-1: Swiss Cheese Model ...................................................................................................47 

Figure 13-2: Trends in ED Design – Building Design and Technology..........................................49 

Figure 13-3: Connecting Simulators with Semantic Data Services.................................................51 

Figure 13-4: Impact Variability .......................................................................................................53 

Figure 14-1: Movement of Patients and Data within an Institution.................................................55 
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9

1 EXECUTIVE SUMMARY 

The “Modeling and Simulation for Emergency Management and Health Care Systems” workshop brought together 

people from different backgrounds including health care clinicians, health care administrators, health care facility 

architects, modeling and simulation professionals, government agency personnel, and academicians. This provided 

an opportunity to explore opportunities that allowed implementation of ideas that capitalized on the potential synergy 

among the diverse backgrounds. Below are two perspectives on the value of the workshop. 

A. A perspective from health care professional 

Modeling and simulation is a new frontier for most managers, providers, and architects working in the health care 

industry. Although successfully utilized in other industries, modeling and simulation is slowly gaining acceptance 

as a tool for health care process improvement, patient flow, and capacity planning and design. Barriers for 

utilizing simulation modeling within the three distinct but unified entities of health care, architecture, and 

simulation engineering have prohibited its standardization as both a tool and a trusted resource. 

Our experience in simulation modeling began after we attended a lecture on the subject at a health care 

architectural design conference. We were sold on the process and the tool but encountered a number of barriers, 

such as data availability and quality, reluctance by health care managers and facility directors to use these 

innovative tools, and the ability of health care providers to allocate precious dollars for an “untested” process. 

Our journey for innovation began by trying to understand the variety of software products, model building 

processes, and engineering approaches. The model we developed was very successful; however, along the way, 

we learned that not all software, processes, and engineers are alike, equal, or interchangeable. We are still 

searching for answers to questions such as: 

 What standards exist for simulation models and engineers? 

 How can I determine if a model built in one software package has the same results as one built in 

another? 

 What data collection process should I expect to be universal to simulation model building? 

Too often we adapt tools from other industries without truly understanding the commonalties and differences for 

application in health care. This can be seen today as the health care industry makes efforts to implement new 

technologies that “solve a problem” but add more work, cost, or frustration for the managers and providers 

required to use the technologies. The result is reluctance or refusal to use simulation modeling and reap its 

benefits. 

Furthermore, new technologies often do not work with one another. For example, (1) data collection software 

often can not be integrated with the database software used for building simulation models; (2) capacity planning 

solutions arrived at by using simulation software may not meet minimum standards for health care architecture 

design; (3) simulation engineers may use different software packages and the end user must learn more than one 

software system. In other words, interoperability between health care systems and simulation software is as 

lacking as interoperability between simulation software packages and the engineers who use them. If tools such as 

simulation modeling are to find their place as the evidence-based phenomenon they can be, standards must be set 

for health care providers to embrace the process, the tools, and the engineers who build them. 

This workshop brought together representatives from industries, technology providers, government agencies, and 

research institutions to discuss the challenges associated with the application of simulation technology in health 

care. The objectives of the workshop were to: 
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 Explore the issues, concerns, needs, and requirements relevant to the application of modeling and 

simulation technology to health care clinics and systems of clinics. 

 To understand and identify the needs and requirements regarding information sharing, exchange, 

interoperability, and interface standards among the various information systems and simulation 

systems in health care. 

 Explore future directions and roadmap proposals in research, development, and application of 

modeling and simulation technology in the health care industry. 

 Explore ways to promote modeling and simulation technology in the health care industry. 

The time has come for the movement to surge forward. This workshop marks a formal and systematic way to 

bring health care simulation modeling into its rightful and evidence-based place for patient flow and capacity 

planning. 

B. A perspective from modeling and simulation professional 

Modeling and simulation techniques are a powerful means to analyze complex systems and support 

improvements in their planning and operations. While they are powerful techniques, they should not be used 

everywhere. The techniques are useful for complex scenarios where other decision analysis techniques cannot be 

applied due to infeasibility or very high computation infrastructure requirements. Application of modeling and 

simulation can require a large effort for the steps involved including data gathering, input analysis, model 

building, verification and validation, output analysis and providing recommendations. Potential opportunities for 

application of modeling and simulation have to be carefully assessed based on detailed input and discussions with 

experts from the domain that is being targeted. This workshop provided an excellent forum to discuss the 

opportunities for application of modeling and simulation to health care, the potential use of a standard framework 

for reducing the effort required for such implementations, and the challenges going in to the future. 

Modeling and simulation has been employed at the National Institute of Standards and Technology (NIST) in 

multiple areas for several decades. The Manufacturing Modeling and Simulation Group at NIST has contributed 

to the growth of modeling and simulation implementations in manufacturing through leading edge applications 

and promotion and development of standards that facilitate such efforts. Most recently, the group has 

spearheaded the development of the Core Manufacturing Simulation Data (CMSD) standard under the auspices of 

the Simulation Interoperability Standards Organization (SISO) 

(http://www.sisostds.org/index.php?tg=fileman&idx=list&id=49&gr=Y&path=Specifications.) Modeling and 

simulation has helped manufacturing become more efficient through a wide range of applications in analysis, 

planning, operations, and system acquisitions. The modeling and simulation professionals are looking to bring 

the same benefit to the health care industry through the application of these techniques. 

Simulation technology can be used to improve facilities and services within the health care industry. To realize 

this vision, it must be possible to integrate simulation models and data that were developed using different 

simulation tools and other software applications. Because such integration and interoperability is not currently 

possible, the goal of this workshop was to identify barriers to that vision and efforts needed to attain that vision. 

The key focus of the workshop was to promote the development of a simulation framework to allow distributed, 

integrated execution of a broad range of simulation systems and presentation of results that captures the 

interdependencies among the systems modeled. 

The workshop explored: 

 Simulation opportunities and requirements for health care. 

 What is needed to develop, demonstrate, and deploy a framework to enable simulations to share 

information within the industry? 
11

We believe this workshop provided a strong foundation for collaboration among government agencies, the 

emergency response community, health care industry, and academia. 

This document provides brief summaries of the presentations and workshop sessions. The summaries are 

arranged in the sequence of the workshop agenda. The appendices provide related information on modeling and 

simulation, standards associated with health care systems for emergency management, and related medical and 

health care organizations collected by the authors. 
12

2 OPENING REMARKS: CHARGE TO THE GROUP - 

MODELING AND SIMULATION FOR EMERGENCY 

PREPAREDNESS: A NEEDS ANALYSIS OVERVIEW 

Charles McLean 

Group Leader, Manufacturing Simulation and Modeling Group, Manufacturing Engineering Laboratory, 

National Institute of Standards and Technology 

There exists a burgeoning need for coordination of modeling and simulation (M&S) activities between sponsoring 

government agencies. To-date, M&S efforts have been largely ad hoc. In response, the National Institute of Standards 

and Technology (NIST) has undertaken a needs analysis to determine what types of M&S efforts are required, and, 

ultimately, to develop M&S standards for governmental agencies, communities, and private organizations. 

The current problem is that numerous federal, state, and local agencies share responsibility for aspects of emergency 

response, and each may have different needs for and perspectives on M&S techniques. There exists no common 

framework or definition for potential M&S applications, and little interoperability between M&S products and 

datasets. 

Leaders at NIST have articulated a vision for the future, in which integrated M&S and gaming techniques could be 

used to support education and training, research and development, system and process improvement, and planning for 

the emergency response community. This vision relies on the development, testing, and deployment of interoperable 

systems through the identification or creation of interface standards. 

Accomplishing this vision will require establishing order through a common taxonomy to categorize potential and 

existing uses of M&S applications. Such a classification scheme would incorporate the objectives, target organizations 

and mission areas, context, and implementation characteristics of M&S applications. Major categories of objectives 

include decision support, planning levels, systems engineering, training and performance measurement, and 

intelligence and risk analysis. Target organizations vary by type (international, federal, private, etc.), level or scope, 

and persistency (permanent versus temporary). The contexts for M&S include different modeling domains; types of 

incidents, events, and activities; emergency support functions; and life cycle phases. Implementation characteristics 

include techniques for representation, modes of interaction, interfaces, standards, data sets, and planning scenarios. 

The needs for M&S in emergency response include examples of social behavior; physical phenomena; environmental, 

economic, and organizational factors; infrastructure systems; and other aspects of emergency response (see Figure 2-

1.) The NIST needs analysis will also identify categories of data set requirements for M&S. 

The scope of the health care domain includes patients, medical personnel and hospitals, pharmaceutical companies, 

and other health care organizations. There are important use case scenarios and uses for M&S in this domain. The use 

case describes who is involved, what their roles are, what data are needed, and other features of M&S. The uses of 

M&S in health care include system applications, physiological processes, simulator facilities, and devices. 

The charge to the workshop prompted participants to help identify experts, information sources, and existing 

applications for M&S in the health care domain; to specify needs for M&S in health care, and to validate those needs; 

to define needed M&S functions, data, and integration capabilities; and to determine which existing standards are 

relevant, and what gaps exist in current standards. 
13

Simulation System Needs

Technically Technically-correct correct 

simulations of: simulations of:

- physical phenomena physical phenomena

- the environment the environment

- social behaviors social behaviors

- organizations organizations

- infrastructure systems infrastructure systems

Computing and Modular Standard Validated 

communications simulation information scenarios and 

infrastructure and gaming models, sample data

reference databases, and sets

architecture message formats

Acknowledgment: The building fire screenshot has been adapted from Sim City 4 website.

Realistic role playing Realistic role playing 

user interfaces for: user interfaces for:

- first responders first responders

- incident management incident management

- support personnel support personnel

- civilian population civilian population

- opposing forces opposing forces

- live elements live elements

Simulation System Needs

Technically Technically-correct correct 

simulations of: simulations of:

- physical phenomena physical phenomena

- the environment the environment

- social behaviors social behaviors

- organizations organizations

- infrastructure systems infrastructure systems

Computing and Modular Standard Validated 

communications simulation information scenarios and 

infrastructure and gaming models, sample data

reference databases, and sets

architecture message formats

Acknowledgment: The building fire screenshot has been adapted from Sim City 4 website.

Realistic role playing Realistic role playing 

user interfaces for: user interfaces for:

- first responders first responders

- incident management incident management

- support personnel support personnel

- civilian population civilian population

- opposing forces opposing forces

- live elements live elements

Figure 2-1: Simulation System Needs 

 
14

3 KEYNOTE ADDRESSES 

3.1 KEYNOTE ADDRESS – DAY 1: MERGING COMMUNITY, 

EMERGENCY PLANNING WITH EMERGENCY DEPARTMENT 

HEALTH CARE SYSTEMS: THE FUTURE OF SIMULATION 

Duane C. Caneva, MD 

Director, Medical Preparedness Policy, White House Homeland Security Council 

The approach undertaken by the Department of Homeland Security (DHS) to emergency preparedness includes 

understanding the meaning and needs of preparedness, and the taxonomy and ontology of creating a national 

preparedness framework. Questions that drive this process include what it means to be “ready”, what is 

“preparedness”, how to manage risk, what responses are sustainable, how does current status impact other plans and 

programs, how visible are resources, and what is the best use of resources? 

The National Response Framework, released in early 2008, was discussed. This document outlines an overall strategic 

approach to disaster response. There is a need for a national preparedness framework, which would describe the 

taxonomy and ontology of disaster preparedness in a “system of systems” approach. The goals of a model for 

preparedness include measuring readiness, providing root cause analysis for degrees of readiness, and allowing for 

optimization of response (see Figure 3-1.) In creating the model, it is important to identify the types of complexity that 

it must accommodate, including structural complexity (number of parts in system), interactive complexity (behavior 

of parts in system), and the perspective of complex adaptive systems, which exhibit coherence under change. 

The ontologic character of the model is not a hierarchy per se, but a system in which all nodes have relevance and 

centeredness. Determining the connections between these nodes, and how they are formed, is an important 

consideration in creating models for preparedness. The model for national preparedness will be highly complex, 

capturing the dimensions, layers, spectra, and quanta of the preparedness framework. A “system of systems” 

architecture is created capturing relevant organizations, programs, types of incidents, capabilities, and system 

components. 

The model should capture a readiness factor (statistical representation of readiness), resource utility function 

(statistical representation of likelihood of success), and preparedness factor (statistical representation of stage of 

preparedness). These factors help to determine the preparedness of a given unit or other entity. Other important factors 

in the model include a responsiveness factor, deployability factor, and resource donor impact factor. Together these 

and other factors provide a means to gauge and manage operational risk. Modeling and simulation play a key role in 

helping to determine these factors and their relationships in specific incidents. 

A national preparedness model should also provide for the visibility of layers of command structure and resource node 

capabilities, as well as allowing for appropriate sharing of data across a national hierarchy. The layers of response – 

local, regional, national – may each have their own set of best practices, and their own characteristics (e.g., urban, 

suburban, rural) and potential disaster scenarios (e.g., earthquake, hurricane). A strong national preparedness 

framework, therefore, must be dynamic, multidimensional, and pragmatic; it should allow for analysis of 

preparedness, best deployment of resources, and the efficiency of resource utilization; it should provide for views 

across the framework to examine current status, hazards and vulnerabilities, risk management, strengths, and trends. 
15

Target Capabilities List (Sep 2007)

Figure 3-1: Preparedness Cycle

 
16

3.2 KEYNOTE ADDRESSES – DAY 2 

3.2.1 KEYNOTE ADDRESS – DAY 2, PART 1: THE TIME AND MOTION 

STUDY: HOW DO MEDICAL-SURGICAL NURSES SPEND THEIR TIME? 

Marilyn Chow, RN, DNSc, FAAN 

Vice President, Patient Care Services, Kaiser Permanente 

There is currently a pressing need for change to the hospital work environment, as well as a unique opportunity for 

change. Results of a recent Time and Motion study provide direction for potential improvements to the hospital work 

environment. The worsening nursing shortage dictates the need for changes to the nurse work environment so that 

hospitals can maintain necessary staffing levels. An ongoing, unparalleled building boom in hospital construction and 

renovation provides an opportunity to affect the design of hospitals for the next generation. The Time and Motion 

study was undertaken to better understand how nurses spend their time, and to identify environmental variables in the 

nursing workplace that can be altered to improve the efficiency of nursing care, and ultimately, patient safety. 

The study included 36 diverse hospitals across the U.S. A total of 763 medical-surgical nurses participated, accounting 

for more than 2200 work shifts and nearly 22000 hours of nursing time. The methods employed four study protocols 

that assessed, in brief, a sampling of nurse activities through a personal digital assistant; nurse location and movement 

through radio frequency identification (RFID) tags; and nurse physiologic response through the use of specialized 

armbands that collected basic physiologic data (see Figure 3-2.) 

The results showed that nurses divided their time similarly between the patient room, nurse station, and other locations 

on and off the unit. In all, nurses spent 30.8 % of their time in the patient room. By activity category, nurses spent 77.7 

% of all time on nursing practice activities, and only 6.6 % on activities considered to be waste. When nursing practice 

time was broken down into subcategories, the data demonstrated that nurses spent most of this time on activities other 

than patient care. Documentation (35.3 %), care coordination (20.6 %), and medication administration (17.2 %) 

consumed the majority of all nursing practice time, compared to 19.3 % for patient care activities, and only 7.2 % for 

patient assessment and surveillance; this last category is the reason that patients are in the hospital in the first place. 

Nurses traveled long distances, a median of 4.8 km (3.0 miles) per 10 hour daytime shift. Distance traveled varied 

widely between nurses, from 1.6 km to 8.0 km (1 mile to 5 miles) per shift. This finding was one of the most 

surprising of the study; factors that may affect inter-nurse variation include nurse assignments (which patients in 

which rooms), adaptability, and approach to organization of work. 

The results illustrate the complex and demanding hospital work environment, and suggest opportunities to improve 

the efficiency of nursing care. Changes to the processes and technologies of documentation, medication 

administration, and care coordination could affect nursing efficiency and the safe delivery of care. 
17

Protocol C:

RFID tracking 

tags

Data: Checked 

for quality and 

loaded into 

Oracle DB

R objects 

generation

R statistical 

software

Graphs 

& reports

Protocol A:

Documentation 

time

Protocol B:

Nurse work 

sampling

Secure data 

transfer to 

24x7 

research 

Server

Wireless 

Receivers

Protocol D:

Armband

Data 

download 

to laptop

Data Collection Overview

Protocol C:

RFID tracking 

tags

Data: Checked 

for quality and 

loaded into 

Oracle DB

R objects 

generation

R statistical 

software

Graphs 

& reports

Protocol A:

Documentation 

time

Protocol B:

Nurse work 

sampling

Secure data 

transfer to 

24x7 

research 

Server

Wireless 

Receivers

Protocol D:

Armband

Data 

download 

to laptop

Data Collection Overview

Figure 3-2: Data Collection Overview
18

3.2.2 KEYNOTE ADDRESS – DAY 2, PART 2: MODELING THE TIME AND 

MOTION STUDY 

Susan O’Hara, RN, MPH 

President, O’Hara HealthCare Consultants, LLC 

M&S tools were employed on a subset of data from the Time and Motion study with the goal of identifying features of 

nurse practice or unit architecture that affect the movement and performance of nurses. To do so, experts developed a 

modeling approach based on both qualitative and quantitative data, including nurse assignments, patient 

characteristics, policies and processes, and architectural floor plans. The approach incorporated clinical, architectural, 

and engineering components. The model development process included a Bayesian Belief Network (BBN) to model 

nurse behavior, a probability movement and time spent model, and distance matrices, among other features; the results 

were incorporated into a data visualization model. Data visualization was used to obtain a real picture of what goes on 

in a nurse’s day. A “typical” day could be mapped out on an influence diagram of nurse performance metrics and 

factors. 

The goal is to form models that can provide answers regarding nursing policies and procedures and unit architecture, 

and to evaluate scenarios such as surge, different admissions processes, and so forth (see Figure 3-3.) Modeling the 

admission-discharge process, for example, has implications for surge response, as well as day-to-day operations. 

Modeling the process allows operations mangers to identify the sequence of response to surge, to understand the 

difficulties that arise, and to test what-if scenarios for how to improve unit performance. 

Suggestions for improving surge capacity include a reverse-triage approach – identifying which inpatients could be 

discharged to increase capacity in case of a surge – which, if performed daily, may help units prepare for potential 

surges. The use of a dedicated admissions nurse also drastically reduces the time committed by unit nurses to 

admission processes, and could ease throughput during a surge. The addition of a dedicated admissions nurse was 

evaluated in a model of a unit from the Time and Motion study. In this model, the use of an admissions nurse can 

increase patient care time. However, this finding is dependent upon nurse-to-patient ratios. At a 1:5 ratio, time with 

the patient increases with an admissions nurse; at a 1:6 ratio, patient care time improves little, while distance traveled 

increases; at 1:7, these results are further exaggerated. 

 M&S tools have significant utility in relation to nursing practice. Nurses must be engaged in the process, and process 

maps should be introduced as a part of clinician training. Finally, models should not be limited to the Emergency 

Department (ED) or an inpatient unit; they should encompass the whole hospital. 
19

Figure 3-3: Architecture + Clinicians + Engineering = Whole System Design 
20

4 ENHANCING OPERATIONAL READINESS BY LEVERAGING 

MODELS, SIMULATION, AND DECISION-MAKING TOOLS: 

PERSPECTIVES FROM THE FIELD

4.1 DEFINING THE BUSINESS CASE 

Paul Hewett 

Deputy Director, Center for Integrated Emergency Preparedness, Decision and Information Sciences Division, 

Argonne National Laboratory 

Experts at the Center for Integrated Emergency Preparedness have taken M&S tools to users at state and local levels 

to encourage their integration into the planning process. M&S tools must be useful, usable, and used; tools without all 

three characteristics will not help end users. The usage rate with current models is only about 30 % to 50 %. The 

question is why these tools are not more widely adopted. 

One part of the answer is defining where M&S tools fit. Across mission areas, operational phases, and domains, M&S 

and decision-support tools are used to support the planning process (see Figure 4-1.) Models are used to formulate 

plans for operations in terms of training, organization, planning, people, leadership, equipment, and facilities. By 

using models, emergency planners attempt to better understand emergency situations by generating assumptions (not 

facts), visualizing disaster dimensions, evaluating the evolution of hazards, and identifying needs and demands. 

Knowing these features of a disaster allows planners to assign appropriate capabilities, which may be defined as “the 

ability to take a course of action.” M&S and decision-support tools, therefore, inform incident action plans (tactical 

level), emergency operations plans (operations level), and concept plans (strategic level). They also define the range 

and scale of operations by predicting the assets that will be required. The foundation that M&S provide can be 

described by the acronym, GRRRS: Goals, Roles, Relationships, Resources, Structure. 

Other reasons that M&S tools are not used include characteristics of the planning team and the tools themselves. 

Members of planning teams are rarely full-time and often only one-deep at the local level. They may be 

uncomfortable with basic information technology (IT), have little time to devote to M&S, and have unrealistic 

expectations of what M&S can accomplish. Common complaints describe M&S tools as inaccessible, complicated, 

expensive, and insufficiently reflective of local conditions or the domain in question. An example of a model that 

practitioners like is the hurricane evacuation model HUREVAC. Users note that it is free and easy to access and use, 

requires no data input, and provides easily understood output in a variety of useful formats. It is also important for 

users to practice using the tools to ensure proper interpretation of output, and the full use of model capabilities. 

Good M&S tools, therefore, should have a familiar and simple layout; data input should be easy, such as drag-anddrop, rather than a large data grid. Practitioner-defined needs suggest that M&S tools should be customizable based on 

local data and demographics, and contain decision tools that address GRRRS. Tools should be robust and 

comprehensive, while keeping the technical aspects of the tool out of view. Ease-of-access is important; models may 

be hosted centrally or locally, and could be accessible by laptop or personal digital assistant (PDA). Tools should also 

account for the full range of hazards relevant to the user. 
21

Response Visualization

Desired End-State

Sequence of Actions

Organization of Response Area 

Integrated/Synchronized 

Emergency Operations 

Plans and Procedures 

Responders’

Insight/Feedback

Observer Data

Formulate Concept 1 of Operations 

Assess Current 

Status

Anticipate Future 4

Response

Execute/Exercise Plans 3 and Procedures

Write Plans and 2 Procedures

•Hazard 

Identification

•Capability 

Assessment

•Risk 

Assessment

•Data Analysis

•After Action 

Reviews

•Exercise 

Reports •Exercise 

Programs

•Hazard 

Response

•Game 

Planning

•Resource 

Loading

© 2000 Argonne National Laboratory

Maintain/Improve/Change

Training, Organizations, 

Plans, People,

Leadership, Equipment, Facilities

Models,

simulations,

decision-support

tools inform these

processes

Support the Planning and Exercise Cycle

Response Visualization

Desired End-State

Sequence of Actions

Organization of Response Area 

Integrated/Synchronized 

Emergency Operations 

Plans and Procedures 

Responders’

Insight/Feedback

Observer Data

Formulate Concept 1 of Operations 

Assess Current 

Status

Anticipate Future 4

Response

Execute/Exercise Plans 3 and Procedures

Write Plans and 2 Procedures

•Hazard 

Identification

•Capability 

Assessment

•Risk 

Assessment

•Data Analysis

•After Action 

Reviews

•Exercise 

Reports •Exercise 

Programs

•Hazard 

Response

•Game 

Planning

•Resource 

Loading

© 2000 Argonne National Laboratory

Maintain/Improve/Change

Training, Organizations, 

Plans, People,

Leadership, Equipment, Facilities

Models,

simulations,

decision-support

tools inform these

processes

Response Visualization

Desired End-State

Sequence of Actions

Organization of Response Area 

Integrated/Synchronized 

Emergency Operations 

Plans and Procedures 

Responders’

Insight/Feedback

Observer Data

Formulate Concept 1 of Operations 

Assess Current 

Status

Anticipate Future 4

Response

Execute/Exercise Plans 3 and Procedures

Write Plans and 2 Procedures

•Hazard 

Identification

•Capability 

Assessment

•Risk 

Assessment

•Data Analysis

•After Action 

Reviews

•Exercise 

Reports •Exercise 

Programs

•Hazard 

Response

•Game 

Planning

•Resource 

Loading

•Hazard 

Identification

•Capability 

Assessment

•Risk 

Assessment

•Hazard 

Identification

•Capability 

Assessment

•Risk 

Assessment

•Data Analysis

•After Action 

Reviews

•Exercise 

Reports

•Data Analysis

•After Action 

Reviews

•Exercise 

Reports •Exercise 

Programs

•Hazard 

Response

•Exercise 

Programs

•Hazard 

Response

•Game 

Planning

•Resource 

Loading

•Game 

Planning

•Resource 

Loading

© 2000 Argonne National Laboratory

Maintain/Improve/Change

Training, Organizations, 

Plans, People,

Leadership, Equipment, Facilities

Models,

simulations,

decision-support

tools inform these

processes

Support the Planning and Exercise Cycle

Figure 4-1: Support the Planning and Exercise Cycle 
22

4.2 EXPLORING A SOLUTION

W. Chris Metz 

Lead Software Engineer, Center for Integrated Emergency Preparedness, Argonne National Laboratory 

The approach to bringing together M&S and emergency planning is to create a combined solution – a tool or set of 

tools – that is scalable to and usable in the typical community (see Figure 4-2.) Several of the challenges to this 

process include difficulties in bringing key stakeholders together (travel costs, schedule conflicts, hard to maintain 

team momentum), integrating technologies (many disparate M & S tools exist, most tools have limited scenario 

support), and tracking multiple simulation outputs in one place. 

Optimally, the process begins with a face-to-face planning meeting to exchange information and build strong 

relationships. However, it can be a challenge to bring everyone to the table. When in-person meetings are not possible, 

distance planning methods can be used. Such methods are easily scaled to large teams, and allow for easy 

dissemination of information. If the plan is created using an online solution, it is easier to reuse the profiles and plan 

data for the exercise. Distant exercises (DISTEX) allow for leverage of existing profiles from the distance planning 

team, and live video feed can be incorporated into the exercise. Finally, data is captured and correlated providing the 

team with an analysis for review. This gives the team the advantage of capturing and correlating the analysis. 

The creation of an emergency response plan should answer questions such as what should happen, when, and at whose 

direction. After the scenario is defined it is important to identify the associated departments’ resources used over a 

scale of time. The departments are guided to filter the plan to show ‘their’ responsibilities. 

Mass collaboration technology that supports distance planning is fast becoming widely accepted. Examples include 

Microsoft SharePoint, O3 spaces, and E-GroupWare. Each of these support document management, version control, 

discussion boards, email alerts, and other features, and do so out of the box. Mass collaboration technology also 

supports workflows, which control information flow either by individual (who needs to know) or time (when it needs 

to be known). Workflows may be sequential (linear) or state (more fluid). Workflows support decision making by 

disseminating appropriate information to the right people in real-time. 

The goals of employing mass collaboration technology include incorporating outputs from disparate M&S tools, 

establishing trusted electronic contacts, and generating increased visibility of decision criteria, thereby contributing to 

higher quality plans. These tools can increase the confidence of decision makers, enhance the buy-in of participants, 

and maintain the momentum of the planning team. 

A key concept is the creation of the emergency response plan. This plan defines the scenario in question, dictates what 

should happen, when, and at whose direction; and associates’ departments and resources. 
23

Figure 4-2: Opportunity for Integration with M&S 
24

5 THE USE OF HI-FIDELITY SIMULATION FOR PEDIATRIC 

EMERGENCY TRAINING FOR MASS CASUALTY INCIDENT 

EXERCISES 

Phillip Jacobson, MD 

Pediatric Critical Care, Rush University Medical Center and John H. Stronger Jr. Hospital of Cook County 

The Rush Simulation Laboratory is a patient simulator lab established early this decade. High-tech simulators are used 

in medicine for teaching and assessing psychomotor skills. The central advantage of simulation in this setting is that 

clinicians can gain experience, while no harm can come to patients. 

While simulators are not well established in pediatric medicine, they may be of even greater utility in this setting since 

life-threatening emergencies are relatively rare with children, meaning that clinicians do not gain as much hands-on 

experience. Children also have unique physiology and disease processes, and interventional procedures can be more 

difficult. 

The Rush Simulation Laboratory employs life-sized (adult, child, infant), hi-fidelity, computerized mannequins. 

Because they simulate pulse, breath, heart tones, vital signs, and pupil changes, the mannequins allow for physical 

examinations and interventions (see Figure 5-1.) The simulation lab runs monthly life-threatening emergency 

simulations for pediatric residency programs. The lab is also used to train licensed physicians and nurses (as opposed 

to students and residents) for disaster preparedness. Pediatric disaster training is important because children have 

unique characteristics, including airway differences, thinner skin (that absorbs more toxins), less blood and volume 

reserve, and greater risk of hypothermia due to larger surface-to-mass ratio, among other features. 

Using the simulation lab, different pediatric disaster scenarios can be simulated, such as chemical or blast events. 

Mass casualty training is particularly important. Physicians are not trained to handle mass casualties, as real events are 

relatively rare. The design of mass casualty simulation is based on overwhelming medical staff with victims, and 

limiting time and resources. This scenario reveals the interrelationship between the care of each patient; time given to 

one patient is time taken from another victim. The use of the hi-fidelity mannequins forces clinicians to perform actual 

interventional procedures. Live actors are combined with simulation mannequins to create a life-like scenario that 

demands clinicians’ interaction and challenges their decision making. 

The results of a simulation of a mass casualty scenario run at the laboratory were described. Regarding participant 

actions and victim outcomes, four of five medical teams resuscitated an infant that should have been assessed as 

“expectant,” meaning that the victim should have been passed over in triage. Four of five teams also ran out of Type O

negative blood, resulting in the death of some victims. Some teams also ran out of ventilators, meaning that a team 

member had to hand ventilate. Organizers noted that the participants “thought on their feet” throughout the exercise, 

and noted the importance of this ability in disaster response. 

When simulation participants were surveyed for feedback, all respondents said that this type of exercise should be 

required for disaster training, and would recommend the exercise to colleagues. 
25

Lifelike Mannequin Functions

Figure 5-1: Lifelike Mannequin Functions
26

6 MODELING AND SIMULATION INTEROPERABLE TOOLSET 

FOR HEALTH CARE SYSTEMS ANALYSIS 

James Walsh 

Research and Development Division Manager, SPARTA, Inc. 

Model interoperability can be defined as the ability to use tools together to solve a problem or meet an overall 

modeling need. An example would be using a queuing model to derive a statistical distribution for use in a discrete 

event simulation. Interoperability stands in contrast to integration of models, which is also important for the efficiency 

of the modeling effort. 

The domain tool set can be understood as a “pyramid” construct. Component models form the base of the pyramid; 

these models have scope and detailed component representation. System models comprise the middle of the pyramid, 

building on the performance of lower level models to simulate the behavior of a system, such as an emergency 

department. The tip of the pyramid consists of system of systems (SoS) models, which are broad in scope, and involve 

the overall effectiveness of a system, such as an emergency management system (see Figure 6-1.) Building an SoS 

model can be accomplished through interoperation of two system models, or by developing an integrated SoS model. 

Different levels of interoperability range from the level of data (“my model can use your data”), to the file level (“my 

file can be read by your application”), to the application level (“my model can work directly with your model”). The 

specific interoperability requirements emerge from analysis of the modeling need and type of models selected. Certain 

functions are key to model interoperability, including data formatting and sharing, model synchronization, display and 

animation, and data storage and analysis. Interoperability standards are needed in these functions to facilitate the tool 

set approach. 

Agent-based models differ from process models by focusing on behavior and interactions, rather than activities and 

queues. An example of an agent-based model is the Health Care Efficacy Architectural Analysis Tool (HEAT), 

developed with O’Hara HealthCare Consultants, LLC and Mark Sullivan Architects, which uses a data-driven 

approach to create new “what if” scenarios by changing only data input, eliminating the need for developing a new 

model. The HEAT model incorporates architectural, movement, process, and capability data to simulate nurse 

behavior. 

The HEAT model is also extensible to play a SoS modeling role, which requires the ability to interoperate at different 

levels of abstraction. In terms of interoperability, the HEAT model demonstrates several key functional requirements. 

HEAT employs Bayesian Belief Networks (BBN) to model nurse behavior, and XML schema as a mechanism to 

format modeling data and to describe model meta data. The BNN’s utilize an interface – an opportunity for 

standardization – so that different BNN’s can be employed without reconstructing the interface. 
27

Successfully applied to Missile Successfully applied to Missile 

Defense, Space Architecture, Defense, Space Architecture, 

Commercial Satellite, Others Commercial Satellite, Others

Vertical Interoperability Allows Flexible Representation 

of Broader System Behavior

System of 

Systems 

Models

System of 

Systems 

Models

System

Models

System

Models

Component

Models

Component

Models

• Broad in Scope

• System Performance From 

Lower Level Models

• Narrow Scope

• Detailed 

Component 

Representation

• Behavior of a System

• Performance From 

Lower Level Models

• Emergency Management 

System

• Measures SOS Effectiveness

• Communications

• Scheduling 

Algorithm

• Effects Models

• Triage System

• Emergency 

Responders 

• Emergency Department

Pyramid Construct to Organize 

Domain Tool Set

Successfully applied to Missile Successfully applied to Missile 

Defense, Space Architecture, Defense, Space Architecture, 

Commercial Satellite, Others Commercial Satellite, Others

Vertical Interoperability Allows Flexible Representation 

of Broader System Behavior

System of 

Systems 

Models

System of 

Systems 

Models

System

Models

System

Models

Component

Models

Component

Models

• Broad in Scope

• System Performance From 

Lower Level Models

• Narrow Scope

• Detailed 

Component 

Representation

• Behavior of a System

• Performance From 

Lower Level Models

• Emergency Management 

System

• Measures SOS Effectiveness

• Communications

• Scheduling 

Algorithm

• Effects Models

• Triage System

• Emergency 

Responders 

• Emergency Department

Successfully applied to Missile Successfully applied to Missile 

Defense, Space Architecture, Defense, Space Architecture, 

Commercial Satellite, Others Commercial Satellite, Others

Vertical Interoperability Allows Flexible Representation 

of Broader System Behavior

System of 

Systems 

Models

System of 

Systems 

Models

System

Models

System

Models

Component

Models

Component

Models

System of 

Systems 

Models

System of 

Systems 

Models

System

Models

System

Models

Component

Models

Component

Models

• Broad in Scope

• System Performance From 

Lower Level Models

• Narrow Scope

• Detailed 

Component 

Representation

• Behavior of a System

• Performance From 

Lower Level Models

• Emergency Management 

System

• Measures SOS Effectiveness

• Communications

• Scheduling 

Algorithm

• Effects Models

• Triage System

• Emergency 

Responders 

• Emergency Department

Pyramid Construct to Organize 

Domain Tool Set

Figure 6-1: Pyramid Construct to Organize Domain Toolset
28

7 THE NEXT INFLUENZA PANDEMIC AND MODELING: THE 

NEED TO KEEP IT SIMPLE 

Martin Meltzer, PhD, MS 

Division of Emerging Diseases and Surveillance Systems, Centers for Disease Control and Prevention 

Mathematical modeling tools have been used to inform planning for the next influenza pandemic. In terms of models, 

policy makers want answers, options (“what if” scenarios), and solutions that relate to their specific domain (e.g., 

state, and city). What policy makers do not need is a black box; they must understand the “mixing bowl” that is the 

model. In the case of public health, mathematical models mix epidemiological and other data related to the disease, 

and in this case, influenza and its dynamics. 

Influenza is a viral disease that has a short incubation period (1 day to 5 days) and is highly communicable (1 day to 2 

days before onset of symptoms, and 4 days to 5 days after onset). The short incubation period and communicability 

are a public health challenge, meaning that approaches to disease control such as quarantine most likely would not 

work. New strains of influenza arise through the close interaction between poultry, pigs, and people. Many influenza 

viruses derive from avian origins, but most avian viruses are not well adapted to human infection. In areas where 

poultry and pigs are kept in close proximity (such as Southeast Asia), however, the viruses shed from poultry are 

taken up by pigs. Pigs become infected with a wide range of viruses, including strains that are better adapted to 

humans, which allows for the resorting of avian flu viruses to become infectious to humans. It is possible that rare 

avian flu strains could cross directly to humans; the 1918 pandemic virus may be an example. 

The epidemiology of influenza describes who gets sick, how many people get sick, when they get sick, and what 

happens when they get sick. Each strain of influenza (even those with similar phenotypes) is associated with different 

epidemiologic characteristics, including mortality. In general, individuals over the age of 65 years are at greatest risk 

for death. Pre-existing medical conditions also confer high risk for hospitalization with influenza-associated health 

problems. Evaluating the potential impact of influenza, therefore, involves accounting for age, risk (high versus low), 

and the virus itself. 

Estimates of the number of deaths for the next influenza pandemic range from 7 millions to 100 millions, based on 

extrapolations from previous pandemics (see Figure 7-1.) The questions for public health policy makers include: how 

to estimate which scenario is most probable, when it may occur, and how to respond. A key policy problem is to 

decide who to vaccinate first during the next influenza pandemic. One way to solve this problem is first vaccinate all 

those at the highest risk of death or vaccinate those who would provide the greatest economic return to vaccination. If 

risk of death is used as the criterion, then people over 65 years of age are the first in line to be vaccinated; if return on 

investment is the priority, and then the young, productive members of society are the first to be vaccinated. 

Four pandemic flu planning models are available from the Centers for Disease Control and Prevention (CDC) for free 

on-line (www.pandemicflu.gov/plan/tools.html): FluAid, FluSurge, Instructions, and FluWorkLoss. Each is simple, 

and addresses one question. FluAid, for example, provides state-level estimates of pandemic impact. The output is a 

simple graph showing impact by gross attack rate (a measure of infectivity), with maximum, minimum, and mean 

rates of death and hospitalization. It does not describe the spread of disease or costs of impact. FluSurge estimates 

surge in demand for hospital-based resources. It depends on knowing the features of specific sites, such as numbers of 

physicians and hospital beds. The results estimate hospitalizations by week of pandemic. FluWorkLoss calculates 

work days lost to illness in a pandemic. The inputs include proportion of population in different age groups (e.g., 0-19, 

20-64, and 65+ years). The output estimates proportion of lost workdays by day of outbreak. Finally, there is also a 

set of instructions that a user can follow to aid calculating 1968-type and 1918-type impact scenarios. 

These models and associated manuals have been downloaded approximately 85000 times since 2000. They have been 

used by most, if not all, U.S. State health departments, U.S. Federal government agencies, as well as several other 

national governments. Although these models are undeniably simple, simple does not mean that these models are 

automatically simplistic. These models do not provide “the answer” or a complete pandemic plan. Rather, they aim to 

provide data to aid decision makers as they make plans for responding to the next influenza pandemics. For planners 

and policy makers unfamiliar with mathematical models, simple models and tools have a great deal of utility.
29

Not a pandemic

1729

1781 1830

1898

1918

1957

1968

52 49 68 10 10 39

Source: Adapted from: Potter CW. J Applied Microbiol. 2001;91:572-579

Years between start of pandemics

When will the next ‘flu pandemic occur?

Time between start of pandemics

Not a pandemic

1729

1781 1830

1898

1918

1957

1968

52 49 68 10 10 39

Source: Adapted from: Potter CW. J Applied Microbiol. 2001;91:572-579

Years between start of pandemics

Not a pandemic

1729

1781 1830

1898

1918

1957

1968

52 49 68 10 10 39

Source: Adapted from: Potter CW. J Applied Microbiol. 2001;91:572-579

Years between start of pandemics

When will the next ‘flu pandemic occur?

Time between start of pandemics

Figure 7-1: When Will the Next ‘Flu Pandemic Occur? Time between Start of Pandemics
30

8 APPROACHES TO MODEL AND SIMULATION 

DEVELOPMENT AND EVALUATION IN FEDERAL 

AGENCIES 

Charles W. Hutchings, PhD 

Deputy Director, Modeling & Simulation Department of Homeland Security, Science & Technology 

Directorate/Test & Evaluation and Standards Division 

Federal agencies are actively exploring the need for guidelines, standards, and best practices in M&S, and what 

considerations are important when acquiring M&S capabilities. Definitions of “model” and “simulation” are important 

to provide a common language base. The Institute of Electrical and Electronics Engineers’ (IEEE) definition of a 

model is an approximation, representation, or idealization of selected aspects of the structure, behavior, operation, or 

other characteristics of a real-world process, concept, or system. A simulation is a model that behaves or operates like 

a given system when provided a set of controlled inputs. Other federal agencies use other definitions of these terms, 

highlighting the need for standards. 

The importance of guidelines for M&S is multifaceted. Audits of M&S efforts by government agencies have indicated 

a need for improved management planning, coordination, and commitment. Guidelines for M&S would contribute to 

accountability for funds, confidence in M&S results, and the integration and interoperability of tools. Guidelines 

would also help to promulgate best practice, and facilitate private sector and commercial partnership in developing 

government tools, products, and services. Policy also holds the potential to advance the maturity of M&S as a field of 

technology. 

Considerations in acquiring M&S capabilities include economic, development, data, and evaluation factors. 

Guidelines and means for assessing each consideration would aid in the creation and adoption of M&S tools. 

Economic analyses question the relationship between results, cost, risk, and value (see Figure 8-1.) Development 

procedures include flow charts that incorporate steps for needs assessment, communication, design, peer-review, and 

application. The reliability and accessibility of data can be stumbling blocks to model development, and a process 

should be in place to vet data and identify its source. Evaluation procedures are critical to validate and verify the 

model. 

The results of an extensive review of existing federal guidelines for M&S describes pockets of expertise in M&S in 

various government organizations, as well as specific M&S tools. The U.S. Department of Defense (DoD), for 

example, has an acquisition M&S master plan to provide policy and guidance, enhance technical framework for M&S, 

improve M&S capabilities, improve M&S use, and shape the workforce. The U.S. Environmental Protection Agency 

(EPA) has produced guidance for an iterative approach to the development, evaluation, and application of 

environmental models. The U.S. Government Accountability Office (GAO) issued guidelines for model evaluation, 

which expands upon the verification and validation of models, with recommended procedures for each step. The 

National Aeronautics and Space Administration (NASA) issued standards for M&S that delineate uses of M&S for 

which standards are required or not required. The standards apply to basic research in critical applications. 

The U.S. Department of Homeland Security (DHS) is attempting to develop training, establish a community of 

interest, and develop policies, guidelines, and standards. It is also initiating strategic planning regarding M&S 

development, evaluation, and use. 
31

Source: Value Measuring Methodology: A How-to-Guide, Federal CIO Council, 

Best Practices Committee, 2004

Economics of Modeling & Simulation

Figure 8-1: Economics of Modeling & Simulation
32

9 SIMULATION’S POTENTIAL ROLE IN EMERGENCY 

MEDICAL CARE SYSTEMS AND MEDICAL EMERGENCY 

PREPAREDNESS 

David Marcozzi, MD, MHS-CL, FACEP 

Director, Emergency Care Coordination Center, Office of the Assistant Secretary for Preparedness and 

Response, Department of Health and Human Services 

A range of federal structures are responsible for emergency medical preparedness and response, beginning with the 

Assistant Secretary for Preparedness and Response (ASPR) within the U.S. Department of Health and Human 

Services (DHHS) (see Figure 9-1.) The mission of ASPR is to lead the nation in preventing, preparing for, and 

responding to the adverse health effects of public emergencies and disasters. The office serves as a coordination point 

for all processes, overseeing the spectrum of care from countermeasure development to administration. 

Three reports have been published by the Institute of Medicine regarding emergency care in the U.S.; these reports 

reviewed hospital-based emergency care, pediatric emergency care, and pre-hospital emergency care. The reports note 

that the nation’s emergency care has not kept pace with demand, and that there is a nationwide problem of emergency 

department (ED) overcrowding. In response to ED challenges, Congress established the Emergency Care 

Coordination Center (ECCC), which is mandated to promote and fund research in emergency medicine and trauma 

health care, among other tasks. The ECCC is still being developed. Its primary goal is to enhance operation efficiency 

and effectiveness of the delivery of emergency care. 

The use of M&S is key to the research component of this mission. The concepts of M&S apply to the input, 

throughput, and output aspects of emergency care. The first step in this process is to identify the type of data needed, 

where the data will come from, and what methods should be used to analyze the data. Currently, ASPR is reviewing 

data standards and existing methods of data collection and modeling. A strategic plan and approach is now in 

progress, with the goal of developing best practices. 

Daily emergency care and emergency preparedness are related, but require different approaches from the planning 

perspective. There are few existing data regarding emergency preparedness. Among the priorities of ASPR dictated by 

the Pandemic All Hazards Preparedness Act (PAHPA) are advanced research on countermeasures, and the 

development of preparedness priorities and evidence-based benchmarks and objective standards for measuring levels 

of preparedness. 

To address these research goals, ASPR is undertaking an emergency preparedness response simulation project. The 

concept is to create a simulation and analysis device for medical response along the lines of the SimCity application. 

The tool would have to account for federal, state, and local response capabilities. The main challenges include data 

collection, and the modeling of relationships, events, environments, and other features of emergency response. The 

goal is to use the tool for planning, training, and real-time decision support. Desired outcomes include improved 

medical response, the ability to direct and improve medical operations, the definition and refinement of metrics, 

identification of bottlenecks in response, and to perform a cost-benefit analysis. 
33

Basic 

First Aid 

Outpatient

Care

Hospital

Inpatient

Care

Nursing

Home 

Care

Pre-hospital 

Care

Fatalities

Management

Emergency 

Departments

Food /

Water 

Safety

Drug /

Blood 

Safety

Mental

Health

Health 

Surveillance

ICU/

Trauma

Critical 

Care

NDMS DMATs

Medical Reserve Corps

Volunteers

NDMS Hospitals

NDMS DMORT

USPHS APHT

USPHS MHT

USPHS RDF

Federal Emergency Preparedness

Basic 

First Aid 

Outpatient

Care

Hospital

Inpatient

Care

Nursing

Home 

Care

Pre-hospital 

Care

Fatalities

Management

Emergency 

Departments

Food /

Water 

Safety

Drug /

Blood 

Safety

Mental

Health

Health 

Surveillance

ICU/

Trauma

Critical 

Care

NDMS DMATs

Medical Reserve Corps

Volunteers

NDMS Hospitals

NDMS DMORT

USPHS APHT

USPHS MHT

USPHS RDF

Federal Emergency Preparedness

Figure 9-1: Federal Emergency Preparedness
34

10 SUSTAINABLE MODELING? SELECT FOR SUCCESS 

Terry P. Young, Professor 

Chair of Healthcare Systems, School of Information Systems, Computing and Mathematics, Brunel University, 

Uxbridge, UK 

Providing health care services requires health care systems, as well as drugs, technology, and infrastructure; 

developing drugs and technologies depends on basic science research. All of these aspects then inform policy. 

Defining where within this framework modeling and simulation (M&S) can be applied can be challenging. 

Each layer of this system has its own characteristics. For example, at the bottom of this schema – basic research – the 

impact of work on patients is often hard to define; intellectual property rights, however, are easily determined, and 

invention is favored over improvement. The higher regions – services and policy – have a clearer impact on patients, 

and improvement plays a key role. The question is where along this continuum the innovation interface is located, the 

interface line that bonds higher-level services (i.e., improvement) and technology (i.e., innovation). At the policy 

level, for example, the value concept is difficult to define, metrics are hard to assess, and improvement methodologies 

emphasize management and leadership. At the level of intervention development, the path to the marketplace is easily 

defined, metrics are easily quantified, but product value to service providers may be difficult to articulate (see Figure 

10-1.) 

The RIGHT program uses M&S to model policy- and service-level decision making. The question is how to mesh 

projects such as RIGHT, which works at higher levels, with M&S projects at the technology research and 

development level, where the goal is to coordinate efforts from research through to the delivery of care. Through the 

Multidisciplinary Assessment of Technology Centre for Healthcare (MATCH), several such tools have been 

developed for product development. An example is a value tool for early product assessment that helps to analyze the 

cost-benefit ratio of product development, and where the product fits in with other existing products. At other times, 

the team has applied Markov models, which have been used to evaluate the utility of procedures such as total knee 

replacement based on different health states. M&S methods at this level are concerned with linking technology to 

outcomes. 

At higher levels of policy and services, M&S methods need to link processes to metrics. A wide variety of methods 

have been explored for this purpose, creating a need for a classification of methods. An early goal of the RIGHT 

program was to create a means of selecting an appropriate M&S tool, given the characteristics of a particular servicerelated decision. The preliminary interface that was created allowed for input such as price, time, expertise, available 

data, and type of output. This tool is the first step toward creating a seamless set of M&S tools that bridge the gap 

between policy and basic science, especially across the innovation interface. The issues going forward with this quest 

are community engagement (such as involving the right stakeholders; field work; and knowledge transfer), the data 

conundrum (what and how to collect; analysis; taxonomy; and policy), method development (new methods; hybrid 

methods; and ‘method-of-methods’), and tool development (software engineering; and potential for new markets). 
35

 

innovation

interface

Devices

Devices

Drugs,

etc

Buildings

Buildings ITC

Materials

Materials

Genomics, Genomics,

Biochem

Biochem, etc

Services Services

Systems Systems

Policy

Drugs, technology Drugs, technology

& infrastructure & infrastructure

Basic science Basic science

High elasticity Low elasticity

IP hard to protect Lots of IP protection

Monopoly provider Thousands of suppliers

Patient impact Indirect: evaluation needed

People Physics, chemistry, biology

National Global Markets

Figure 10-1: RIGHT – On Latest Thinking
36

11 MODELING AND OPTIMIZING THE PUBLIC HEALTH INFRASTRUCTURE FOR 

EMERGENCY RESPONSE 

11.1 OPTIMIZING HEALTH CARE RESOURCES FOR EFFICIENT USAGE, AND 

INTEGRATION WITHIN an EMERGENCY RESPONSE CONTEXT FOR 

LARGE-SCALE POPULATION PROTECTION 

Eva K. Lee, PhD 

Director, Center for Operations Research in Medicine and HealthCare; Professor, Industrial & Systems 

Engineering, Georgia Institute of Technology; Co-Director, NSF I/UCRC Center for Health Organization 

Transformation; Co-Director, Biomedical Informatics Program, NCRR-Atlanta Clinical and Translational 

Science Institute, Emory – Morehouse – Georgia Institute of Technology 

Multi-level strategic planning of the public health infrastructure is an essential element of city readiness and 

emergency response for biodefense, radiological response, and infectious disease outbreaks. The first level involves 

optimizing the existing health services, assessing current surge capability and capacity, and optimizing patient flow, 

efficiency and clinic throughput. The second level involves the protection of the regional population through largescale dispensing of prophylaxis medication. The challenges to this process include the stockpiling and distribution of 

medication, locations of dispensing facilities, optimal facility staffing and resource allocation, as well as logistics, 

transportation, and dispensing modalities. Modeling, simulation, and large-scale optimization play an important role in 

addressing some of these challenges and in helping in the decision making process. Mass dispensing requires a 

smooth supply-demand operational infrastructure. It involves the flow of medication from stockpiles and managed 

inventory sites to dispensing sites for distribution to individuals, and the flow of population from their respective 

homes to checkpoints and to the dispensing locations. 

Researchers at Georgia Tech created a software enterprise system called RealOpt that allows for the study, training, and 

enhancement of emergency response and planning for biologic threats, infectious disease outbreaks, and radiological 

population monitoring (http://www2.isye.gatech.edu/realopt-regional/RealOptRegionalManual/Preface.html). The tool is a 

decision support system for population and medication flow planning and real-time resource allocation (see Figure 11-1.) It 

couples fast optimization algorithms and large-scale simulation into one single software decision-support system. The system 

accommodates both regional and federal strategic and operation planning and assessment. It assists local/state emergency 

managers in determining optimal setting up of regional points of dispensing (PODs) and its regional stockpile/distribution 

planning to prepare for emergency situations. Given a regional population, the system helps determine where and how many 

PODs are needed for optimal operations, the optimal assignment of individuals/households to various PODs, and the optimal 

staffing and allocation of resources at each POD. RealOpt has been used in dispensing exercises for anthrax and smallpox, as 

well as real vaccination events for flu, Hepatitis A, and H1N1. 

The computational challenges are two-fold: 1) To determine the network of PODs in a region, an optimization model 

with strategic planning considerations was designed. It addresses how to direct residents to PODs by minimizing 

average distance and travel time to the closest POD and how to minimize facility set-up costs. 2) For labor resource 

allocation, the problem involves multiple objectives such as minimizing labor costs, maximizing throughput, 

equalizing utilization at each POD, and minimizing wait time, while assigning staff with appropriate skills to serve in 

each station within the POD. The effectiveness of RealOpt was first validated in 2005 when it was used for planning 

and decision making for a large-scale anthrax drill. In this exercise, one county used RealOpt for planning, while 

seven other counties used other models. The county modeled with RealOpt processed the highest throughput of all 8 

counties; 50 % more individuals were processed compared to the second-place county. External evaluators determined 

that RealOpt produced the most efficient floor plan, the most cost-effective dispensing (smallest labor versus 

throughout ratio), and the smoothest operations. The exercise also revealed many areas that need attention during 

operations planning and design of dispensing centers. The tool is free and widely used in public health agencies; it can 

be run on any computer or personal digital assistant. 

 
37

Figure 11-1: Infrastructure of RealOpt – A Tool for Emergency Operational & Strategic Planning
38

11.2 EMERGENCY DEPARTMENT AND HOSPITAL THROUGHPUT 

MODELING 

Tanner Flynn 

Principal, FDI-Simulation 

David Ferrin, FHIMSS 

Principal, FDI-Simulation 

The basic elements to be considered in hospital throughput modeling for the emergency department (ED) and 

operating room (OR) include input (emergency admits, transfers, direct admits, post-operative admits), admissions 

and discharge times, business rules, and outcomes (optimizing numbers of beds, higher occupancy, lower surgery 

cancelations, etc.). Key performance indicators include volumes (of admissions, transfers, etc.), resources (beds, staff, 

hardware), cycle times (length of stay, boarding time, turn around time), and others (occupancy, bed utilization, etc.) 

(see Figure 11-2.) 

Best practices for patient throughput simulation are numerous, but must be individualized by institution. Sources of 

best practices include the Institute for Healthcare Improvement, Healthcare Advisory Board, Sg2, and the Robert 

Wood Johnson Foundation. 

Using key performance indicators and best practices, models can be constructed of ED, OR, or other facility 

throughput. In one example from a model of a hospital with 32 ED beds and 370 inpatient beds, the administrators 

wanted to know how many visits their ED could handle without increasing ED length of stay beyond 3 hours. The 

model estimated that the ED could handle approximately 40000 visits per year while maintaining a 3 hour length of 

stay. More visits led to a rapid increase in length of stay; 4 hours at 41000 visits, over 5 hours at 42000. The key 

message for surge capacity is that the difference between 40000 and 41000 visits is 3 patients per day; in other words, 

once capacity is reached, any further increase results in a dramatic change in length of stay. This is an example of how 

M&S can help managers understand how changes in different input parameters affect throughput, length of stay, and 

other outcomes. 

Other features that throughput modeling can evaluate include bed availability, discharge patient flow, inpatient 

discharge time of day, bedside registration, use of a discharge lounge, and staffing levels, among many others. Using 

M&S to understand current status is invaluable in the context of emergency preparedness, as the models can be used 

not only to assess features of hospital design and organization to maximize daily throughput and value proposition, 

but also to judge the effects of surge on hospital functions. 
39

Emergency Admits

Inter-Hosp Transfers

Internal Transfers

Direct Admits

Post-Surgical Admits

Bed Board Summary

# of Beds by:

- Specialty

- Geographic area

Outcomes:

Higher Occupancy

And Bed Utilization

Lower Surgery

Cancellations

Better understanding

Of what can improve

And how much

Incoming/Outgoing

-Expected+

patients

Discharge Times

Admit/Discharge Profiles

by Specialty

Include:

Business Rules

Patient Care Throughput

Emergency Admits

Inter-Hosp Transfers

Internal Transfers

Direct Admits

Post-Surgical Admits

Inter-Hosp Transfers

Internal Transfers

Direct Admits

Post-Surgical Admits

Bed Board Summary

# of Beds by:

- Specialty

- Geographic area

Outcomes:

Higher Occupancy

And Bed Utilization

Lower Surgery

Cancellations

Better understanding

Of what can improve

And how much

# of Beds by:

- Specialty

- Geographic area

Outcomes:

Higher Occupancy

And Bed Utilization

Lower Surgery

Cancellations

Better understanding

Of what can improve

And how much

Incoming/Outgoing

-Expected+

patients

Discharge Times

Admit/Discharge Profiles

by Specialty

Include:

Business Rules

Incoming/Outgoing

-Expected+

patients

Discharge Times

Admit/Discharge Profiles

by Specialty

Include:

Business Rules

Patient Care Throughput

Figure 11-2: Patient Care Throughput
40

12 EMERGENCY MANAGEMENT 

12.1 TOWARDS A SIMULATION AND GAMING FRAMEWORK FOR 

INCIDENT MANAGEMENT 

Sanjay Jain, PhD 

Assistant Professor, School of Business, George Washington University 

Potential applications of M&S in incident management include training, systems engineering, vulnerability/risk 

analysis, and operational planning and decision support. The challenge is that developments in M&S for this 

application area are ad-hoc and fragmented. For M&S to be effective, a coordinated approach is required. 

A vision for integrated M&S for incident management includes realistic role-playing game interfaces and technically 

correct simulations. The game interfaces include first responders, incident management, support personnel, civilian 

population, opposing forces, and live elements. The simulations model the major aspects of incident management 

including physical phenomena, the environment, social behaviors, organizations, and infrastructure systems. The 

overarching principals in this vision include modular simulation and a gaming reference architecture, computing and 

communications infrastructure, standard information models, databases and message formats, validated scenarios and 

sample data sets, conformance testing procedures, and systems. 

There is a need to identify and/or develop the standards needed to achieve data exchange, module interoperability, and 

software re-use. M&S needs in incident management include tools that can be rapidly configured to support different 

scenarios; validated tools and data sets; data-driven simulations; scenario data available in standard formats; 

interoperable tools to allow for rapid integration; and a communication and computing infrastructure. Currently, adhoc M&S tools are used to analyze multiple aspects of a selected incident type with different, overlapping scopes. 

The input data for these tools exist in proprietary formats. The resulting ‘custom developed’ models are generally 

stand alone and cannot be integrated. A framework is needed to help integrate M&S tools in incident management. 

The recommended framework includes three dimensions: incident, lifecycle/phase, and domain. Incidents include 

man-made and natural incidents. The lifecycle/phase dimension ranges from prevention and preparedness to response, 

recovery, and mitigation. Domains include civilian population, critical infrastructure, environment, government 

agencies, private sector, and others. Each cell of the framework represents a potential M&S application. For example, 

an application may be developed to model fire (incident) prevention (lifecycle/phase) in the private sector (domain). 

Each cell may require multiple models, or individual models could simulate one aspect of multiple cells. A software 

architecture is needed for the integration of standard component models that cover the solution space. 

A proposed system reference architecture concept was outlined that divided modules into gaming and simulation (see 

Figure 12-1.) Gaming subsystem modules include live elements, support institutions, response management, on-scene 

response, and civilian population. Simulation subsystem modules include social behavior simulators (crowd, traffic, 

epidemic, consumer and other behaviors), physical phenomena simulators (earthquake, explosion fire, plume disease 

and bioagents, and biotic agents), environmental simulators (weather, watershed, indoor climate, ecology), 

organizational simulators (fire, law enforcement, health care, government agencies, military, terrorists), and 

infrastructure system simulators (food supply, power distribution, water supply, transportation, communications, 

computers and networks). 

Health care system simulations, by way of example, are an integral part of integrated incident management 

simulations. Health Care simulations include social behaviors (such as might influence epidemic spread), physical 

phenomena (such as spread of a bio-agent), and organizational simulations (such as response to anthrax attack). 

Gaming examples include emergency vehicle driver training and on-scene response and triage by emergency medical 

personnel.
41

Simulation Communications Integration Infrastructure

Organizational

Simulators

Social 

Behavior 

Simulators

Environmental 

Simulators

Physical 

Phenomena 

Simulators

Simulation 

Federation 

Management

Infrastructure

System

Simulators

Gaming Communications Integration Infrastructure

Live 

Elements MMOG 

Management

Support 

Institutions

Response

Management

On Scene 

Response

Civilian 

Population 

and 

Opposing 

Forces

MMOG

Data 

Servers

Federation 

Data 

Servers

System Reference Architecture Concept

Simulation Communications Integration Infrastructure

Organizational

Simulators

Social 

Behavior 

Simulators

Environmental 

Simulators

Physical 

Phenomena 

Simulators

Simulation 

Federation 

Management

Infrastructure

System

Simulators

Gaming Communications Integration Infrastructure

Live 

Elements MMOG 

Management

Support 

Institutions

Response

Management

On Scene 

Response

Civilian 

Population 

and 

Opposing 

Forces

MMOG

Data 

Servers

Federation 

Data 

Servers

System Reference Architecture Concept

Figure 12-1: System Reference Architecture Concept
42

12.2 Walking on Broken Glass – A Case for Developing Standards 

in Emergency Preparedness 

Joseph L. Cappiello 

President & CEO, Simulation Education Services 

From the perspective of the hospital, organizations prepare for disasters for several reasons, including requirements 

(federal, state, insurance, etc.), geographical realities, history of past situations, public expectation, and moral 

imperative. Influences over preparation are equally diverse, including accreditation and regulatory requirements, 

financial limitations, workforce shortages, capacity constraints, operational disruption, corporate influence, and lack 

of standardized performance metrics, among others. Factors that impact preparedness range from the infrequency of 

events, to a lack of taxonomy and baseline data. Further, competing priorities, competition between hospitals, and a 

heavy focus on critical infrastructure also impacts preparedness. 

The emphasis is typically on response rather than resiliency. Resiliency encompasses the continuity of operations 

planning. Unlike preparedness, resiliency can be easily and concretely defined. Definitions of resiliency describe it as 

the ability to rebound from stress or catastrophe. Resiliency as a concept has certain advantages. It creates a language 

and goals that “preparedness” cannot, broadens the dialogue, includes the continuity of operations planning, creates 

public-private partnerships, and lends itself to effective M&S scenarios. Resiliency describes a cycle from preparation 

to response, recovery, and mitigation. Elements of resiliency include robustness (continuing to function during 

disruption), resourcefulness (managing response to disruption), rapid recovery (get back to normal), and reflection 

(absorb new lessons). 

The major issues from past disasters include failure of communications, inadequate utility plans and strategies, lacking 

incident command systems, and minimal involvement with the community’s Emergency Operations Center. 

Furthermore, hospital planning is often dictated by the most recent significant disaster. Finally, few organizations 

could withstand events that last for days and separate them from community support. During a disaster, hospitals 

experience increased admissions, decreased discharges, and ever-increasing pressure on limited resources. Outpatients 

and chronically ill patients seek support and medications at hospitals and citizens even seek non-health care services 

and shelter. Significant disasters are sustained, affect multiple communities simultaneously, overwhelm federal 

response, impact pubic services, and threaten the entire health care infrastructure. 

There are six critical components for hospital resiliency: communications, resources, safety and security, staff 

responsibilities, utilities, and clinical activities. Models of hospital preparedness should incorporate these features. The 

“96 hour rule,” which is really a principle to guide planning, takes into account the fact that federal response to a large 

scale disaster may require 72 or more hours. Disaster response strategies derived by this rule include conservation of 

resources; curtailment of services; consolidation of patients; supplies and staff; staged or limited evacuation; and full 

evacuation. Modeling could help facilities determine what strategy to pursue; for example, what point should they 

elect to shelter in place versus initiate a full evacuation (see Figure 12-2.) 
43

Figure 12-2: To Stay or Leave? 

Low

Stress High 

Stress

High 

- Supply lines intact 

- Local evacuation 

- Community support

- Sustainable for 

unlimited time 

- Evacuation not 

needed 

- Supply lines cut 

- Distant evacuation 

- Improvisation a 

must 

- Clock is ticking

II IV 

I III 

- Rapid deterioration 

- Only distant evacuation 

possible 

- No local support 

 available

 HCO Stress 

Community Stress 

To Stay or To Leave?
44

12.3 Leveraging Ubiquitous Technologies for Real-Time Disaster 

Simulation 

Ryan Scott Bardsley 

Senior Systems Manager, Massachusetts General Hospital/ Center for Integration of Medicine and Innovative 

Technology (CIMIT) 

CIMIT’s applied research in simulation ranges from fundamental technologies for medical simulation to intelligent 

tutoring systems. CIMIT has focused on integrating two M&S projects: a real-time incident preparedness (RIPS) 

simulation effort and a next-generation trauma mannequin called COMETS or Combat Medic Training System 

(http://www.cimit.org/about-stories-simulation.html.) 

RIPS is designed to facilitate storytelling during tabletop training exercises, the most commonly used form of disaster 

response training. RIPS supports both rural and urban areas by automatically making the content relevant by pulling 

locale-specific content from web sources such as Google Maps. This architecture allows content to be freely shared 

and modified by users, a model successfully employed by the gaming industry. Certain areas of the narrative, such as 

place names, street names, and weather patterns, are customized to match those of the area where the simulation is 

being conducted. RIPS can therefore take any scenario and tailor it to provide a relevant training environment without 

the need to end user customization (see Figure 12-3.) Current RIPS scenarios include a slow-onset flood, conventional 

explosive device, release of a chemical agent, a smallpox outbreak, radiological spill in transit, response to suspicious 

packages, and HAZMAT (hazardous material) level B training. 

COMETS is a multi-year research effort to develop a fully-autonomous and ruggedized casualty simulation for 

training of combat medics in battlefield hemorrhage control and tactical combat casualty care (TC3). It will initially 

take the form of a 183 cm (6 ft) tall, 75 kg (165 lb) male soldier that bleeds, screams, and writhes in pain. The 

mannequin is powered by a hybrid architecture of pre-computed physiologic models and real-time detection and 

response to medic intervention. The units will be completely autonomous, capable of operation outside of the line-ofsight of an instructor; once the program is initiated, the mannequin will “die” unless treated appropriately. This 

enables a single COMETS mannequin to be used from point-of-injury, through transport, to definitive care. While 

advanced technologically, the system is designed from the outset to be simple and intuitive to operate. All data are 

stored onboard to facilitate after-action review by an instructor allowing COMETS to provide the Army with a new 

tool for casualty data-collection and medic performance assessment. 

The Comprehensive Emergency and Disaster Response Simulation (CEDRS) is an effort to integrate these two 

simulation approaches. CEDRS will integrate in-field data from medic intervention via the self-contained mannequin 

simulators with the RIPS virtual incident command simulation. The program will use a gaming approach to encourage 

suspension-of-disbelief, increase effectiveness of team training, and better simulate the complexities of actual disaster 

response. 
45

 

Figure 12-3: Accident Simulation
46

13 HEALTH CARE SYSTEMS 

13.1 Trends in ED Design – Architectural Planning and Simulation 

Modeling Integration 

13.1.1 From Feasibility Studies to Programming to Post-Occupancy – 

Patient Safety Perspective (Patient Safety and the Need for Simulation 

Modeling) 

Kenneth N. Dickerman, ACHA, AIA, FHFI 

Patient Safety Design Consultants, LLC 

This is part one of a two-part presentation on the use of modeling and simulation to improve patient care in the 

physical space. This presentation features patient safety in the Emergency Department (ED). Patient safety is a 

significant problem in medical care. Estimates of deaths due to injury and hospital acquired infections range from 

100000 to 250000 per year. Indeed, medical errors and accidents are one of the leading causes of death in the U.S.; a 

trip to the hospital is more risky than flying on a scheduled airline, working in a nuclear power plant, or serving on an 

aircraft carrier. Unfortunately, ED design has not kept up with the need to improve physical environments to enhance 

safety. The emergency department is a major patient “portal.” Patient safety, already tenuous, is put at the knife’s edge 

by challenges such as changing patient demographics (e.g., obesity), financial pressures, and the need to cope with 

major man-made or natural disasters. 

This presentation discusses some important background material relating to safety and to concepts used to create 

better design, including standardization, forcing functions, natural mapping, visibility, and “knowledge in the world.” 

These concepts have been developed by environmental psychology researchers such as Donald Norman and apply to 

building environments as well as other systems. Charles Perrow has also contributed by describing how the 

complexity and connectivity of systems impacts the severity of accident events. Health care is a tightly connected, 

highly complex system. The “Swiss Cheese Model,” crafted by safety researcher James Reason, shows how safety is a 

systems problem in which many small failures, some due to latent defects in various system components, contribute to 

the chain of events that leads to accidents (see Figure 13-1.) 

Simulation can, and should, play an important part in the process of health care building design. In fact, no part of the 

hospital has a greater need for better design than the ED. Unfortunately, a defective, incomplete design process has the 

same results in the quality of the building environment as poor systems design does for medical care. Inadequate 

environments contribute to errors, usually in small ways, but sometimes more dramatically. For example, hospitalacquired infections, a portion of the safety problem, sometimes result from air distribution and filtration problems. 

Falls frequently have an environmental component, and patient handling, which can lead to caregiver injuries, also has 

building design solutions. 

Is it possible to model safety? There are challenges in following and mapping aberrant processes, including the lack of 

useful statistics, caregivers’ reluctance to cooperate, and potential legal concerns. However, these problems are not 

insurmountable and pale in comparison to the potential benefits of better ED design derived from and supported by 

simulation modeling.
47

Figure 13-1: Swiss Cheese Model 

 
48

13.1.2 FROM FEASIBILITY STUDIES TO PROGRAMMING TO POSTOCCUPANCY – BUILDING DESIGN AND TECHNOLOGY PERSPECTIVE 

Mark Sullivan, AIA, NCARB 

President, Mark Sullivan Architects 

M&S can be used to inform the architectural approach to health care design. Modeling in health care design is an 

evidence-based approach that forces a closer analysis of flow process and space utilization. The steps to building a 

simulation model are analogous to the architectural design process. The purpose must be defined, data collected, 

precision of the model determined, and the model built. Then it must be verified, tested, and validated. The advantages 

of M&S in the design process are numerous. M&S allows for better assessment of operations and productivity 

opportunities, evaluation of staffing needs and space utilization, and examination of surge capacity, among other 

advantages. 

Modeling is not new to architecture. Architects have long used 2D floor plans, scale models, and 3D virtual models 

for education and presentation. Newer technologies and applications now allow for the study of building design in the 

context of the surrounding environment using modeling and animation. By way of example, Mr. Sullivan presented a 

3D animation that modeled the events of September 11, 2001, when an airliner struck the North Tower of the World 

Trade Center (Refer to the top left graphic of Figure 13-2.) The animation was created by computer scientists at 

Purdue University who developed an application to link computer simulation and visualization systems using detailed 

finite element analysis (FEA) models of the top 20 floors of the building. The goal of this work was to translate 

collected data into visual events in a scientifically accurate manner. The translator accomplishes this goal by 

discarding simulation data that have little visual relevance and enhancing details with high visual relevance. The result 

was a powerful, scalable, and generalizable tool for visualizing FEA simulations with physical and visual fidelity. 

Similarly, evidence-based architectural models can be used to simulate the impact of new hospital design on work 

processes, patient flow, and the surrounding environment. For ED design, architectural models can be combined with 

M&S tools such as patient flow process maps to evaluate the movement of patients or staff through the proposed unit. 

Such models, based on real-world data, can also help to predict walking distances for staff (Refer to the top right 

graphic of Figure 13-2,) and the revenue lost due to when rooms are closed for procedures (Refer to the bottom 

graphic of Figure 13-2.) Codes, regulations, and guidelines can be incorporated into models to prevent later conflicts 

or violations. 

Mr. Sullivan posed some important questions regarding M&S from an architect’s perspective: Why is data collection 

so complicated and time consuming; why are M&S models often difficult to understand; why do many clients lack 

confidence in M&S; why are models not more interactive for client users; and why are simulation models not 

interconnected to the 3D model of the architectural environment? These questions apply to all components of the 

architectural process, programming, design and construction and should be addressed in feasibility studies on through 

post-occupancy evaluations. Each of these questions represents an important area for future research and innovation. 
49

 

 

Scientifically accurate model that 

renders 3D animation 

Floor Plan used for calculating 

distances traveled by Staff 

Model for Projecting Costs and Feasibility of 

Current Cardiovascular Suite Practice during 

Renovation 

Figure 13-2: Trends in ED Design – Building Design and Technology 
50

13.2 Best Practices for Semantic Discovery and Leverage of 

Enterprise Data in Simulations 

Alan McCutchen 

Vice President, Products, Modus Operandi (MO), Inc 

High quality data is extremely important for successful M&S applications in health care. The completeness, relevance, 

and accuracy of data directly impact the quality of simulation results and decisions made. To provide simulators and 

decision makers with the highest quality data requires strong data integration and correlation. The use of semantic 

technology can improve data and event integration, and the interoperability of simulations. 

Issues that can limit the ability to leverage data are numerous, including lack of interoperability of data, inability to 

locate or retrieve data, trouble making sense of data, and others. Challenges to users reflect the difficulty in integrating 

data from complex systems. Barriers to integration and mediation include human consensus barriers, enablement 

barriers, and integration and sharing barriers. Human consensus barriers revolve around the lack of mission consensus 

and shared vocabulary, strong information architecture, and consistent representation. Often there is limited political 

‘mindshare’. Lack of consistent knowledge representations, inadequate technology to implement in a consistent 

manner and limited technology mindshare are typical of enablement barriers. Integration and sharing barriers are often 

a result of privacy constraints and concerns as well as the lack of unified access. 

Semantic data services were designed to improve integration, create vocabulary-driven data discovery and access, 

enhance the mediation and interoperability of simulators, and align business processes and workflow. The impacts of 

semantic technology include smarter data (discoverable, relevant), smarter infrastructure (rule-, data-, and eventdriven software), and improved interoperability (between simulators and communities of interest). Connecting 

simulators with semantic data services provides a better data and event integration framework (see Figure 13-3.) 

Key software architectural considerations include flexible data source adapters, multi-channel messaging, best-ofbreed infrastructure component support, enterprise data access, and Service Oriented Architecture (SOA) interfaces. 

Data source adapters accelerate integration of legacy systems while multi-channel messaging facilitates event-driven 

data and application integration. Best-of-Breed component support enables optimal solutions such as Declarative 

/Pluggable entity extractors (Apache UIMA (Unstructured Information Management Architecture) compatible), 

reasoners (Jena compatible), analytical algorithms and correlation agents, and finally pluggable security mechanisms 

and policies. SOA web service endpoints facilitate composable integration with Enterprise Service Buses and the 

broader enterprise ecosystem. 

Best practices for semantic data services fall into four key dimensions: human consensus, semantic knowledge 

representations, enablement infrastructure, and simulation integration. The first dimension, human consensus, 

establishes management sponsorship and clear success criteria; this requires management buy-in and authority. The 

simulators and work products to be integrated should be identified as early in the project as possible. And there must 

be a focus on alignment of tasks with project success criteria. The second dimension, semantic knowledge 

representations, focuses on capturing subject matter expertise in a form that can be easily processed. Ontology and 

belief networks must be identified and refined. A minimum set of cross-ontology relationships should be specified. 

The third dimension, enablement infrastructure, aims for runtime functionality that is reusable, interoperable, and 

compliant with key standards. As part of the enablement infrastructure, it is important also to integrate and comply 

with security policies and mechanisms. We’ve seen best results where the process is iterative from pilot to production, 

keeping success criteria in mind throughout. The last dimension, simulation integration, builds on the previous three 

best practices. The simulators that need to leverage the data delivery infrastructure must be integrated, preferably 

using standard web services interfaces. The performance and accuracy of the other dimensions should also be 

validated, and the underlying ontology, mediation, and data mappings must be sustained. Enterprise business 

processes may also be used to orchestrate and monitor the process.
51

Event-driven Semantic Processing and Propagation of Simulation Data

Provides a data and event integration framework

Supports vocabulary-driven data discovery & access

Acts as a workflow propagation engine

Enables interoperability and mediation

Connecting Simulators with 

Semantic Data Services

Event-driven Semantic Processing and Propagation of Simulation Data

Provides a data and event integration framework

Supports vocabulary-driven data discovery & access

Acts as a workflow propagation engine

Enables interoperability and mediation

Connecting Simulators with 

Semantic Data Services

Figure 13-3: Connecting Simulators with Semantic Data Services 
52

13.3 Back to the Future: The Ongoing Need to Promote Simulation 

Modeling to Health Care Decision Makers 

Dave Eitel, MD, MBA 

The application of M&S tools to the modeling of emergency department (ED) capacity is an example of how these 

techniques can be used to improve health care delivery. There is a tremendous need to improve the effectiveness of 

patient handling in health care. One example of improved customer handling is the altered check-in processes at some 

airlines, which drastically reduced waiting times. This example illustrates the potential of re-thinking work processes 

in the ED. Indeed, “no wait” EDs do exist. Both improved airline check-in and no-wait EDs share in common the use 

of queuing. The ED represents a queuing (waiting in line) system, which consists of arriving entities – patients – and 

one or more “servers” providing service. 

Queuing and lean engineering are about eliminating the waste of waiting. The core principles of queuing include 

matching demand (prediction of service load) and capacity (the ability to deliver service over a particular time period). 

Demand for services in the ED is random, or “stochastic,” but it is not unpredictable. For example, the patterns of 

physician staffing and patient arrivals are distinct on weekdays and weekends. Distinct patterns can also be 

demonstrated for individual days of the week: Sundays look like Sundays, Tuesdays like Tuesdays. 

Capacity is a major issue for many hospitals. More specifically, the problem is hospital service capacity. Variation in 

arrivals and/or processes has a profound effect on service capacity. The interdependency of the many features of the 

hospital or ED requires application of M&S tools to describe the impact of variability on service capacity (see Figure 

13-4.) Answering questions such as, “How long will patients spend in the ED for their visit?” requires predictive 

analysis: how many beds, physicians, nurses, and other resources will be needed and when will they be needed to 

handle any particular service load (demand) with current system design, physical plant, and staffing plans (capacity). 

The most important concept in queuing theory is the bottleneck, a stage or number of stages in a system that cannot 

process service quickly enough to prevent backlogs. Improving system performance requires identifying and 

minimizing the performance of bottlenecks. 

Process modeling is used to model queuing systems (service systems). Queuing systems consist of entities being 

processed through a series of service stages, with the opportunity for queues to form between each stage when there is 

insufficient processing capacity at server units. Process modeling helps health care managers understand and manage 

the variation and interdependencies of their world. There are two forms of process modeling. The first is dynamic 

process modeling (Discrete Event Simulation), which handles the need to describe variation over time. This type of 

model is complex, work intensive, and time consuming, but it is powerful and can model anything. The second option 

is static process modeling, which allows for capacity and resource planning at a given service load. 

Simulation provides the ability to model events that occur over time, as well as all variability associated with these 

events. Simulation permits decision makers to visualize the operation through animation of a new or existing system 

under a variety of conditions, and allows users to draw inferences about a new system’s behavior without actually 

building it, or an existing system’s behavior without disturbing it. For example, simulation can be used effectively to 

test major changes to patient flow processes. 

The advantages of simulation tools include the ability to test major changes in patient flow processes. They allow for 

the evaluation of “what-if” scenarios before investing capital and without disrupting hospital operations. Simulation 

can contribute to an understanding of how various system components interact and affect overall performance, allows 

for real-world variability to be included in system analysis, can represent complex real-world behavior that cannot be 

captured analytically, captures logic and policies that do not exist or cannot otherwise be tested, and allows for 

analysis over time. 
53

Figure 13-4: Impact Variability 
54

14 PREPARING FOR WHEN THE RUBBER MEETS THE ROAD: 

ALIGNING SIMULATION AND TECHNOLOGY FOR 

DISTRIBUTED HIGH-ACUITY PATIENT CARE 

Julian M. Goldman, MD 

Director, CIMIT Program on Interoperability and the Medical Device Plug-n-Play Program, Massachusetts 

General Hospital, Harvard Medical School

Emergency health care management use cases for M&S can be used to support effective planning and development. 

Applications of M&S include patient flow within or between institutions. One need to support M&S applications is 

the real-time transfer of data. High-acuity distributed systems do exist, but not yet in health care. Examples include air 

control, satellite tracking, and others. An essential requirement for such systems is interoperability, which must extend 

all the way to medical devices and the patient. Medical devices are key data sources in health care, and can be better 

utilized to deliver care. Many improvements in patient safety and health care efficiency require systems solutions that 

cannot be implemented due to lack of interoperability of medical devices and systems, especially in high-acuity 

settings. The ability to integrate the clinical environment is an essential step toward creating error-resistant systems. 

At CIMIT (Center for Integration of Medicine and Innovative Technology), researchers have developed a prototype 

“Operating Room (OR) of the Future,” which is designed to study workflow and process. One conclusion from this 

prototype was that comprehensive integration of data from clinical and environmental systems can prevent errors and 

inefficiencies (see Figure 14-1.) Certain clinical scenarios demonstrate the potential benefits of interconnected 

medical devices. During cardio-pulmonary bypass, for example, the patient is switched from the anesthesia machine 

ventilator to the bypass machine, and then back again. There have been cases where the team forgot to turn on the 

ventilator after the patient was transitioned off the bypass machine. A smart system would provide a warning if the 

ventilator was off and bypass pump flow was zero. The use of patient-controlled analgesia (PCA) – pumps that deliver 

opioid – is another example. The problem with these devices is that patients or family members may press the delivery 

button by accident, potentially leading to overmedication and adverse effects such as respiratory depression. PCA 

devices could be linked to patient monitors that would detect respiratory depression and limit opioid delivery. 

Two capabilities of medical device interoperability are required to mitigate these hazards. One is bi-directional 

medical device data communication. The second is medical device control capability to permit the integration of 

medical devices into networks that produce “error resistant” systems. The CIMIT Program on Interoperability and the 

Medical Device Plug-n-Play was established to lead the adoption of open standards to support medical device 

interoperability; to define a regulatory pathway in partnership with the U.S. Food and Drug Administration (FDA) and 

other regulators; to elicit clinical requirements for the proposed interoperable solutions to maintain focus on patient 

safety; and to use the vendor-neutral laboratory to evaluate standards and solutions, and model clinical use cases.

Standards for the Integrated Clinical Environment (ICE) have been drafted by the Program. The standard is for 

management of a network of medical devices in support of a single patient in the ICE. The standards establish general 

principles for design, verification, and validation of a model-based integration system that enables the creation of an 

Integrated Clinical Environment. 
55

Movement of Patients and Data 

Within an Institution

Figure 14-1: Movement of Patients and Data within an Institution 
56

15 BREAKOUT SESSIONS/CHARGE TO THE GROUP: CASE 

STUDY GROUP REPORTS AND RECOMMENDATIONS 

Workshop participants broke into six groups to discuss specific features of M&S in emergency management and 

health care systems, and to suggest a starting place for the development of standards and guidelines. The six topics 

were determined by the workshop organizing committee to fill the most prominent needs for standards and guidelines. 

These six topics are: 

 public health data requirements for modeling and simulation 

 data requirements for modeling and simulation in emergency management and health care systems 

 facilities-based modeling and simulation planning for disaster preparedness 

 specifications for modeling, simulation, and gaming 

 modeling and simulation needs assessment for health care systems – clinical needs 

 modeling and simulation needs assessment for health care systems – engineering needs 

The results of these work groups, summarized below, represent a starting point, a foundation for continuing discussion 

and research into the development of standards and guidelines for M&S in emergency management and health care 

systems. 
57

15.1 PUBLIC HEALTH DATA REQUIREMENTS FOR MODELING AND 

SIMULATION 

Group 1 was asked to consider what data would be needed for M&S by public health departments when developing 

emergency response plans. Public health departments are charged with detecting changes in the need/demand for 

health care services, and making sure that need/demand is met with appropriate response. M&S can provide direction 

for designing systems to determine need/demand and response capacity, to examine what-if scenarios and to add 

predictive capabilities. (Planners should distinguish between objective need and subjective demand.) 

In terms of charting the ability of public health systems to respond to a surge, necessary inputs include detailed 

demographics of the population and morbidity patterns by time and place. These data are generally available. On the 

response side of the public health equation – focusing on pre-hospital and hospital care – data sets exist but vary 

widely from state to state. Some states have county level data of hospital admissions which include primary and 

secondary diagnoses. However, the data generally do not describe the specialty services patients received. To 

accommodate M&S requirements, tags should be added to patient data that include information on specialty services 

and admission and discharge times. 

On the capacity side, data exist describing the number of hospitals in an area and whether they have emergency 

departments. What services are available within a hospital and how many beds each facility has is generally not 

known. For M&S, public health departments need accurate, timely information on number and types of available 

beds. In terms of workforce, the number of physicians, nurses and other health care providers is unknown too in many 

cases. Physicians who do not practice fulltime should be identified for modeling purposes, perhaps through licensure 

registries. Each state should have a center where these data are housed, and staff who oversees the data and 

accommodates data retrieval. 

While the list below shows M&S data requirements from a public health perspective, data needs may vary depending 

on the specific nature of the modeled emergency. Also, it should be recognized that M&S dealing with health 

emergencies typically involves collaboration of multiple public and private agencies. 

Demographics

 Current age and sex distribution 

 Population density across target area 

Health patterns

 Disease specific morbidity rates 

 Health care utilization statistics 

Emergency event

 Characteristics of targeted health threat 

 Presumptive community reaction 

Response capacity

 Characteristics of health care facilities 

 Size and capabilities of health care workforce 
58

15.2 DATA REQUIREMENTS FOR MODELING AND SIMULATION IN 

EMERGENCY MANAGEMENT AND HEALTH CARE SYSTEMS 

The task of Group 2 was to create an initial set of data requirements for modeling and simulating emergency 

management and health care systems. The group discussed the topic in relation to a proposed scenario: a hospital 

nurse receives a pamphlet in the mail regarding M&S, and (s)he becomes interested in creating a simulation for 

handling an emergency at their hospital or unit; what data does the hospital need to give the engineers to create and 

run a simulation? 

The goals of M&S identified by the group for this scenario include capacity analysis, and determining the hospital’s 

needs from outside entities. Data requirements were divided into inputs and outputs. The inputs required from the 

hospital include data from: 

 Admissions 

 Discharge 

 Insurance 

 Social Services Office 

 Management 

 Operations manager 

Defining the roles of personnel was also considered important, including: 

 Nurses 

 Doctors 

 Emergency managers 

 Security personnel 

 Volunteers 

 Technicians 

 Administrators 

 Transportation personnel 

Existing data should be identified, and the format of existing data is critically important to the creation of M&S tools. 

Data types must be defined. These include data from admissions, patient treatment, and management. 

Admissions office: 

 Patient types 

 Number of patients 

 Time of arrive – should be further normalized for model 

Patient treatment: 

 Time patient leaves 

 Total duration of day (waiting time, examination time, treatment time) 

 Location of patient 

 Staff responsible 

Management: 

 Number of hospital beds (Emergency Department (ED), Intensive-care Unit (ICU), general) 

 Hospital layout 

 Capacity of waiting area 

 Capabilities and services 
59

 Equipment (medical, such as CT, X-ray, MRI; transportation, such as ambulance, wheel chair) 

 Blood supply (type and amount available) 

 Operations (call list, on-duty personnel, incoming shifts/personnel, shift schedule) 

 Alternative facility resources 

The output data requirements were divided into the categories of throughput, utilization rate, patient acceptance rate, 

and bottlenecks. 

Throughput: 

 Patients per time unit 

 Total patients treated 

 Patients successfully discharged 

Utilization rate: 

 Staff 

 Beds 

 Equipment 

Bottlenecks also include the exhaustion of resources. For example, what happens if the hospital runs out of blood 

during an emergency response? 
60

15.3 FACILITIES-BASED MODELING AND SIMULATION PLANNING 

FOR DISASTER PREPAREDNESS 

Group 3 discussed an approach to developing a plan to deal with a major disaster from the perspective of facilities and 

design. Each of the concepts they developed could be evaluated with M&S to test different scenarios, and the impact 

of choices. For example, one could model the impact of resource utilization on the surrounding area. The features of 

developing an emergency preparedness plan for facilities include: 

 Defining goals of the plan 



 Reaching agreement among participants on a glossary of terms 



 Isolating specific types of disasters that the plan would consider 

o Different types of disaster have highly specific characteristics 

o Need specialist on team for each type of disaster to be included 



 Identify decision makers up front 

o For creating the plan, for adopting the plan, and for implementing the plan 



 Identify major decision points 

o On a timeline, schedule or budget 



 Identify data required, depending on the complexity and scope of problem 



 Identify codes, and agencies that monitor codes that may impact elements of the plan 

o For example, repurposing of spaces may be necessary, and this must be done in compliance with 

codes and regulations 



 Identify resources 

o Internal resources in terms of staff and equipment 

o Dual-purpose equipment 

o In-house reserve resources over which the facility has control 

o Resources and collaborators outside facility 



 Create a database of all potential care space within a certain zone– facility, city, region, state 

o E.g., Outpatient surgical clinics could be identified and incorporated into response plan to serve 

trauma centers 



 Determine how all collaborators will work together and communicate 



 Within a specific facility, identify fall back positions 

o For example, what other space within the facility can be repurposed if ED is overwhelmed 

o Parking garage, ambulatory care spaces, and other clinical spaces or facilities which are not utilized 

on a 24/7 basis. 
61

15.4 SPECIFICATIONS FOR MODELING, SIMULATION, AND 

GAMING 

The task of Group 4 was to create an initial set of specifications for modeling, simulation and gaming in the context of 

emergency management and health care systems. In other words, what should expectations be of the M&S process? 

The group divided specifications into user and developer categories. These categories encompass both needs and 

deliverables: what can users expect to get, and what do they need to provide; what do developers expect to get, and 

what do they need to provide. 

User responsibilities and requirements include: 

 Requirements: 

o Problem and intended use for model 

 Is it intended for decision support, optimization, etc. 

o Budget and schedule 

o Who will use the model 

o Level of user interaction: live, virtual 

o Run time 

o Extensibility/lifecycle 

 Data 

o Availability 

o Accessibility 

 Management buy-in (critical) 

o Physicians 

o Nurses 

o Administrators 

o Subject matter experts commitments 

 Testers 

 Continuing user group 

 Analysis output 

o Form/field 

Developer responsibilities and requirements include: 

 Scope of work 

o Milestones 

 Conceptual model 

 Prototype 

 First data sets 

 Other key events 

 Articulate modeling approach 

o Supports dialogue with customer 

o Alternative approaches, pros and cons 

 Conceptual model: set of materials to describe the model 

o Set up 

o Algorithms 

o Fidelity 

o Interaction of elements 

 Outputs to generate 

 Risk assessment 

 Data requirements 

o What’s available, what’s achievable 

 Validation 

 Deliverables: 
62

o User interface 

o One or more data sets 

o Training 

o Licensing 

o Support plan 

o Assumptions, extrapolations 

Expectations: 

 The rationale underlying these specifications is that many M&S projects fail due to differences between 

expectations of the client and what is achievable by a model. 

 Specifying expectations allows M&S to achieve small successes throughout the process. 

 Other key considerations 

o The challenge of attempting to create a model that works in real time or reacts quickly due to 

complex considerations, expectations of “real-time” responsiveness should be discussed 

o Major underlying factors always include the visualization of output and the availability of data. 
63

15.5 MODELING AND SIMULATION NEEDS ASSESSMENT FOR 

HEALTH CARE SYSTEMS – CLINICAL NEEDS 

Group 5 considered the question of what clinicians want from M&S teams. 

The group settled on three main categories of goals: 

 imparting cultural change 

 multi-patient simulation 

 “med-arch-sim,” or medical-architecture-simulation. 

Changing current medical culture will be key driver of improving patient safety and the efficiency of care delivery. 

 One way to approach this problem is through multiple patient simulations to help clinicians shift from tactical 

thinking (their individual patient) to strategic thinking (multiple patients). 

 Cognitive task analysis will aid the process of redesigning the hospital environment and organization. 

 “Med-arch-sim” is a term the group created to describe an ultra-high quality model of future state(s) to show the next 

generation of clinicians the possibilities of a new environment. 

There are three domains that emerged during the group discussion: 

 facilities (or architecture) 

 simulation 

 medical (or clinical) 

Each domain has its own tools, but no tools exist to bring all three domains and correlating needs together. 

 Architecture Tools 

o 2D Plans 

o 3D Renderings 

 Simulation Tools 

o Conceptual diagrams and models 

o Mathematical models 

o Dynamic models 

o Programming paradigms 

o Analysis techniques 

 Medical Tools 

o Patients 

o Medical simulations 

o Virtual reality 

o Tools for training 

Value of multi-domain tool: 

 architects could leverage both quantitative M&S and clinical aspects of design 

 clinicians could draw on facilities and simulation data 

 the simulation modelers could bring both architectural and clinical aspects to bear on their work. 

The need for such a tool represents an opportunity for future development. The group issued a high-level charge to 

M&S for the development of a “med-arch-sim” real-time animation model to bring together these different cultures 

and environments. 

The group determined that M&S tools should support multiple clinical goals: 
64

 Team training 

 Support task and skill training – from a systems perspective, modeling the interaction between tasks and 

devices 

 Enable analysis of clinical pathways to support quantitative analysis 

 Assess the physical environment in relation to delivery of care 

 Create tools to assess the potential patient safety and error-resistance that could be achieved with systems 

integration, safety interlocks, and closed workflow loops 

 Tools should be useful, useable, and used by the appropriate trained clinicians. 
65

15.6 MODELING AND SIMULATION NEEDS ASSESSMENT FOR 

HEALTH CARE SYSTEMS – ENGINEERING NEEDS 

Group 6 considered the M&S needs assessment from an engineering perspective. 

The participants considered this task in a phased approach: 

 Short term (less expensive) 

 Medium term (intermediate expensive) 

 Long term (expensive). 

Complexity increases from short- to long-term in relation to model development. 

The terms and use of M&S applications for health care systems: 

 Short Term 

o Complexity: Low; small problems with isolated scope 

o Analysis: Parametric 

o Design: Conceptual 

o Training: Familiarization 

o Operations: None 

 Medium Term 

o Complexity: Intermediate; multiple components of system and mutual dependencies; semantic 

interoperability 

o Analysis: Quantitative analysis 

o Design: Evaluation of alternatives 

o Training: Task training and process refinement 

o Operations: Limited 

 Long Term 

o Complexity: High; distributive enterprise dynamic level representations 

o Analysis: Predictive credibility 

o Design: Detailed system design (level of floor, instrumentation 

o Training: Team and distributed collaborator training 

o Operations: Provide real-time operations support (suite of M&S tools, different levels of 

representation 

The enterprise business practice view 

The group’s intention was to provide for any given activity the expected processes and deliverables. The group did not 

consider the table completed or thorough; it represents a framework for considering an analysis of M&S needs, and a 

starting place in this process. 

 Task (activity): Deduce stakeholder needs 

o How (processes): Collaborate with stakeholder 

o Results (deliverables): Initial systematic M&S needs elicitation process and result 

 Task (activity): Document conceptual mission space 

o How (processes): Semantically correct models, interoperability; challenging to accomplish 

o Results (deliverables): Practices, strong cultural commitment 

 Task (activity): Suggest enterprise best practice for investment, development, qualification, and use 

o How (processes): Research existing standards, etc. 

o Results (deliverables): Best practices for enterprise system 
66

16 CONCLUSION 

Mr. Charles McLean concluded the workshop with thanks to the participants for their contributions and 

recommendations. NIST will endeavor to follow-up on the next steps recommended by workshop breakout groups 

(listed earlier in the description of the breakout sessions). 

This workshop provided a strong foundation for collaborative follow-on efforts among government agencies, the 

response community, health care industry, and academia to: 

 identify information sources, simulation systems, and data requirements 

 develop a health care simulation framework 

 develop standards for interoperability and integration 

 develop and demonstrate distributed simulations using commercial simulation software and the 

simulation framework 

The workshop brought together a number of people from government agencies, academia, and industry. Based on the 

discussions and feedback received from the attendees, the workshop succeeded in promoting the concept of the 

simulation framework. 

It is too early to determine the strength of collaborative relationships established at the workshop, but the workshop 

did succeed in creating a network of people working in the area of modeling and simulation for emergency response 

health care applications. Some follow-up meetings have already taken place and more are being arranged. It is hoped 

that tangible collaborations will be established in the future to address the critical steps toward building a simulation 

framework for emergency response and developing necessary simulation standards for interoperability and integration 

of emergency response requirements. 

As mentioned in the Preface of this document, this “Modeling and Simulation for Emergency Management and Health 

Care Systems” workshop is the first technical interest group (TIG) meeting on health care. With the strong interest 

expressed by the participants of this workshop, NIST is planning the next TIG workshop on this topic in 2010. 
67

APPENDIX A. MEDICAL AND HEALTH CARE ORGANIZATIONS 

The following is a partial list of medical and health care organizations that support different levels of emergency 

response responding to unanticipated events that may result in injury and/or loss of human lives. 

Entity Type: 

 Multinational bodies: 

o World Association for Disaster and Emergency Medicine 

o World Health Organization (WHO) 

 Governmental agencies or departments: 

o Centers for Disease Control and Prevention (CDC) 

 Bioterrorism Preparedness and Response 

 Emergency Preparedness and Response Branch 

 Environmental Health Services Emergency Planning Site 

o National Institutes of Health (NIH) 

 National Institute of Allergies and Infectious Diseases 

o U.S. Department of Health and Human services 

 Office of the Assistant Secretary for Preparedness and Response Emergency Operations 

 Agency for Health Care Research and Quality (AHRQ) 

 Health Insurance Portability and Accountability Act (HIPAA) 

 Healthfinder.gov 

 Administration for Children and Families 

 National Disaster Medical System 

o U.S. Food and Drug Administration (FDA) 

o U.S. Public Health Services (PHS) 

o State Departments of Public Health (http://www.phppo.cdc.gov/phtn/sites.asp)

 Military services: 

o Defense Threat Reduction Agency 

o Military Vaccine Agency 

o Modeling and Simulation Information Analysis Center 

o U.S. Department of Veterans Affairs 

 Emergency Management Strategic Health Care Group 

 Private sector organizations: 

o Academic Center for Public Health Preparedness 

o American Academy of Emergency Medicine 

o American Academy of Pediatrics, Bioterrorism, Terrorism Resources 

o American College of Contingency Planners 

o American Hospital Association 

 Association for Healthcare Resources & Materials Management 

o American Medical Association 

 Center for Public Health Preparedness and Disaster Response 

o American Red Cross 

o American Society of Professional Emergency Planners 

o Center for Public Safety Excellence (CPSE) 

 Commission on Fire Accreditation International 

 Commission on Professional Credentialing 

o Center for Integration of Medicine and Innovative Technology (CIMIT) 

o Greater New York Hospital Association 

o Louisiana Hospital Association 

o Massachusetts Hospital Association 

o National Association of County and City Health Officials 

o National Associations of State EMS Directors 
68

o National Bioterrorism Civilian Medical Response Center 

o National Nurse Emergency Preparedness Initiative 

o Nevada Hospital Association 

o The RAND Center for Domestic and International Health Security 

o Yale New Haven Health Center for Emergency Preparedness & Disaster Response 

o University of Pittsburgh Medical Center, Center for Biosecurity 

o Selected healthcare facilities and systems: 

 Caritas Christi Healthcare System 

 Catholic Health Initiative 

 Johns Hopkins Hospital and Healthcare System 

 Intermountain Healthcare 

 Kaiser Permanente 

Public Regulatory: 

o The Joint Commission Resources 

o State and local Departments of Health 

Public Advisory: 

o Healthcare Advisory Board 

o Institute for Healthcare Care Improvement (IHI) 

o Institute of Medicine (IOM) 

o Robert Wood Johnson Foundation 

o The Leapfrog Group 
69

APPENDIX B. RELEVANT HEALTH CARE STANDARDS AND EFFORTS 

The following table is a partial list of established standards and specifications that may be relevant to modeling and 

simulation for emergency management with focus on health care. The name, information category, description, 

standardization status, and responsible organization of each specification or standard are included in the table.

Standard/Specification 

Name 

Information 

Category 

Description Standardization Responsible Organization 

The International 

Classification of 

Diseases 

Operational 

guidelines 

An official system of 

assigning codes to 

diagnoses and 

procedures 

associated with 

hospital utilization. 

Standard: 

Ninth Revision, 

Clinical 

Modification 

(ICD-9-CM) 

The National Center for 

Health Statistics (NCHS), 

and Centers for Medicare 

and Medicaid Services. 

Digital imaging and 

communications in 

medicine (DICOM) 

General purpose 

integration 

interfaces 

A specification for 

exchange of 

radiology images 

and other medical 

information between 

computers. It 

enables digital 

communication 

between diagnostic 

and therapeutic 

equipment and 

systems from 

various 

manufacturers. 

Industry 

specification: 

DICOM 3.0 

American College of 

Radiology (ACR), and 

National Electrical 

Manufacturers Association 

(NEMA) 

Health Level Seven 

(HL7) Standards 

Domainspecific 

integration 

interfaces 

A specification for 

the exchange, 

management, and 

integration of 

electronic healthcare 

information. 

Standards: 

ANSI/HL7 V2 

XML-2003; 

ANSI/HL7 V2.5-

2003; ANSI/HL7 

V3 DSR, R1-

2005, ..... 

ANSI/HL7 V3 

PM, R1-2005; 

ANSI/HL7 V3 

CR, R3-2005; 

ANSI/HL7 V3 

MFRI, R1-2006 

Health Level Seven (HL7), 

and American National 

Standards Institute (ANSI) 
70

Standard/Specification 

Name 

Information 

Category 

Description Standardization Responsible Organization 

Introductory resource 

guide for implementing 

the Health Insurance 

Portability and 

Accountability Act 

(HIPAA) Security Rule 

Operational 

guidelines 

Standards for the 

security of electronic 

health care 

information. It 

specifies a series of 

administrative, 

technical, and 

physical security 

procedures for 

covered entities to 

use to assure the 

confidentiality of 

electronic protected 

health information. 

Standard: 

SP 800-66 REV 1 

National Institute of 

Standards and Technology 

(NIST) 

The Americans with 

Disabilities Act (ADA) 

Standards for 

Accessible Design 

Operational 

guidelines 

Guidelines for 

accessibility to 

places of public 

accommodation and 

commercial facilities 

by individuals with 

disabilities. These 

guidelines are to be 

applied during the 

design, construction, 

and alteration of 

such buildings and 

facilities. 

Standards: 

ANSI A117.1-

1980, and 

ADA Standards 

for Accessible 

Design 

U.S. Department of Justice, 

and American National 

Standards Institute (ANSI) 

Interim Life Safety 

Measures (ILSM) 

Operational 

guidelines 

Guidelines for 

Design and 

Construction of 

Hospital and Health 

Care Facilities to 

minimize the 

possibility of injury 

or damage due to 

fire, smoke & fumes, 

or other threat. 

Standard: 

 ILSM 

The Joint Commission 

Resources 
71

Standard/Specification 

Name 

Information 

Category 

Description Standardization Responsible Organization 

Infection Control Risk 

Assessment (ICRA) 

Operational 

guidelines 

Guidance with 

general information 

on risks and possible 

mitigation strategies 

for remote use of and 

access to (EPHI) 

Electronic Protected 

Health Information. 

Standard: 

ICRA 

Department of Health and 

Human Services (DHHS) 

Emergency management 

drills 

Operational 

guidelines 

A plan that describes 

a hospital's approach 

to conduct drills to 

test emergency 

management. 

Standard: 

EC.4.20 

The Joint Commission 

Resources 

Emergency management 

planning 

Operational 

guidelines 

A plan that describes 

a hospital's approach 

to emergencies in the 

hospital or in its 

community. 

Standard: 

EC.4.10 

The Joint Commission 

Resources 

Emergency electrical 

power source 

Operational 

guidelines 

A plan that describes 

the requirements of a 

hospital's emergency 

power system. 

Standard: 

EC.7.20 

The Joint Commission 

Resources 

Maintenance, testing, 

and inspection 

requirements for 

hospital emergency 

power system 

Operational 

guidelines 

A plan that identifies 

how a hospital 

maintains, tests, and 

inspects its 

emergency power 

system. 

Standard: 

EC.7.40 

The Joint Commission 

Resources 

Business 

continuity/disaster 

recovery plan 

Operational 

guidelines 

A plan that describes 

a hospital's continuity 

of information when 

information systems 

are interrupted. 

Standard: 

IM.2.30 

The Joint Commission 

Resources 
72

Standard/Specification 

Name 

Information 

Category 

Description Standardization Responsible Organization 

Patient flow 

management 

Operational 

guidelines 

Plans that identify 

and mitigate 

impediments to 

efficient patient flow 

throughout the 

hospital. 

Standard: 

LD.3.15 

The Joint Commission 

Resources 

Infection control Operational 

guidelines 

A plan that manages 

an ongoing influx of 

potentially infectious 

patients over an 

extended period. 

Standard: 

IC.6.10 

The Joint Commission 

Resources 

Disaster privileges Operational 

guidelines 

A plan that identifies 

the option to grant 

disaster privileges. 

Standard: 

MS.4.110 

The Joint Commission 

Resources 

Drawing Interchange 

File (DXF) Formats 

General 

purpose 

integration 

interfaces 

A specification for 

exchange of 

radiology images and 

other medical 

information between 

computers. 

Industry 

specification: 

AutoCAD, 

v.u.22.1.01 

Autodesk, Inc. 

WMD, Emergency 

Management and 

Medical Websites 

(http://www.ynhhs.org/ 

emergency/US_DHHS 

_web_sites.pdf)

Reference 

resource 

A comprehensive list 

of internet sites of 

use for emergency 

planning and in 

particular Weapons 

of Mass Destruction 

(WMD) and medical 

emergency planning. 

Reference 

resource: 

Yale New 

Haven Health 

System 

Yale New Haven Center for 

Emergency Preparedness 

and Disaster Response 

Standard Guide for 

Hospital Preparedness 

and Response 

Operational 

guidelines 

A guide intended to 

assist the leaders of 

hospitals in the 

design, planning, and 

response to be 

undertaken by 

hospitals and health 

care organizations to 

an event that 

necessitates the 

activation of an 

emergency operations 

plan. 

Standard: 

ASTM E2413 – 

04 (2009) 

ASTM International 
73

APPENDIX C. RELEVANT ARCHITECTURE STANDARDS AND EFFORTS 

The following table is a partial list of established standards and specifications that may be relevant to modeling and 

simulation for architectural design for health care facilities. The name, information category, description, 

standardization status, and responsible organization of each specification or standard are included in the table. 

Standard/Specification 

Name 

Information 

Category 

Description Standardization Responsible Organization 

The Industry 

Foundation Classes 

(IFC) 

Domainspecific 

Integration 

Interfaces 

A product data model to 

facilitate interoperability 

in the building industry. 

Its data requirements 

include disciplines 

involved, life-cycle 

stages, level of detail 

required, and software 

application used. 

Industry 

specification: 

The IFC2x, 

Edition 3 

The International Alliance 

for Interoperability (IAI) 

The CIMSteel 

Integration Standards 

Release 2 (CIS/2) 

Domainspecific 

Integration 

Interfaces 

A product model and 

electronic data exchange 

file format for all parties 

of the steel supply chain 

involved in the 

construction of steel 

framed structures. 

Industry 

specifications: 

SCIP265:2003; 

SCIP266:2003; 

SCIP268:2003; 

SCI-P269:2003 

The Steel Construction 

Institute (SCI) 

National Building 

Information Model 

(NBIM) Standards 

Domainspecific 

Integration 

Interfaces 

A specification that 

supports business 

processes and 

information exchanges 

of information employed 

during all phases of the 

facility lifecycle. 

Nomenclature specific to 

North American 

business practices is 

used. 

Industry 

specifications: 

SCIP265:2003; 

SCIP266:2003; 

SCIP268:2003; 

SCI-P269:2003 

National Institute for 

Building Sciences (NIBS) 
74

Standard/Specification 

Name 

Information 

Category 

Description Standardization Responsible 

Organization 

International Building 

Code, 2003 

Operational 

guidelines 

A specification on 

structural and fire- and 

life-safety provisions 

covering seismic, wind, 

accessibility, egress, 

occupancy, roofs, etc. 

Industry 

specification: 

ICC IBC-2003 

International Code 

Council 

CityGML Domainspecific 

Integration 

Interfaces 

A specification for 

exchange and storage of 

Virtual 3D city models. 

Industry 

specification 

The Open Geospatial 

Consortium, Inc (OGC) 

The Americans with 

Disabilities Act (ADA) 

Standards for 

Accessible Design 

Operational 

guidelines 

Guidelines for 

accessibility to places of 

public accommodation 

and commercial 

facilities by individuals 

with disabilities. These 

guidelines are to be 

applied during the 

design, construction, 

and alteration of such 

buildings and facilities. 

Standards: 

ANSI A117.1-

1980, and 

ADA Standards 

for Accessible 

Design 

U.S. Department of 

Justice, and 

American National 

Standards Institute 

(ANSI) 

Interim Life Safety 

Measures (ILSM) 

Operational 

guidelines 

Guidelines for Design 

and Construction of 

Hospital and Health 

Care Facilities to 

minimize the possibility 

of injury or damage due 

to fire, smoke & fumes, 

or other threat. 

Standard: 

ILSM 

The Joint Commission 

Resources 

Emergency electrical 

power source 

Operational 

guidelines 

A plan that describes the 

requirements of a 

hospital's emergency 

power system. 

Standard: 

EC.7.20 

The Joint Commission 

Resources 
75

Standard/Specification 

Name 

Information 

Category 

Description Standardization Responsible 

Organization 

Maintenance, testing, 

and inspection 

requirements for 

hospital emergency 

power system 

Operational 

guidelines 

A plan that identifies 

how a hospital 

maintains, tests, and 

inspects its 

emergency power 

system. 

Standard: 

EC.7.40 

The Joint Commission 

Resources 

Governmental Unit 

Boundary Exchange 

Standard 

Domainspecific 

Integration 

Interfaces 

A specification for 

establish the content 

requirements for the 

collection and 

interchange of 

Government unit and 

legal entity boundary 

data. 

Government 

Specification: 

Governmental Unit 

Boundary Data 

Exchange Standard 

(January/2003) 

Federal Geographic 

Data Committee 

(FGDC) 

Drawing Interchange 

File (DXF) Formats 

General 

purpose 

integration 

interfaces 

A specification for 

exchange of radiology 

images and other 

medical information 

between computers. 

Industry 

specification: 

AutoCAD, 

v.u.22.1.01 

Autodesk, Inc. 

ESRI Shapefile 

Technical Description 

Document 

formats 

A geospatial vector 

data format for 

geographic 

information systems 

software. 

Industry 

specification 

Environmental 

Systems Research 

Institute (ESRI) 

Spatial Data Transfer 

Standard (SDTS) 

Document 

formats 

A standard for the 

exchange of spatial 

data between different 

computing platforms. 

Standard: 

FIPS 173 

National Institute of 

Standards and 

Technology 

(NIST), and U.S. 

Geological Survey 

(USGS) 

Standard for a United 

States National Grid 

(USNG) 

General 

purpose 

Integration 

Interfaces 

A system of 

geographic grid 

references for 

developing locationbased services within 

U.S. 

Government 

specification 

Federal Geographic 

Data Committee 

(FGDC) 

Green Guide for Health 

Care (or Green Guide)

Operational 

guidelines 

The Green Guide for 

Health Care is a best 

practices guide for 

healthy and 

sustainable building 

design, construction, 

and operations for the 

healthcare industry. 

Green Guide for 

Health care, Version 

2.2 

gghc.org 
76

Standard/Specification 

Name 

Information 

Category 

Description Standardization Responsible 

Organization 

GeoTIFF Document 

formats 

A metadata format, 

which provides 

geographic information 

to associate with the 

image data. 

Industry 

specification 

GeoTIFF community 

(over 160 different 

remote sensing, GIS, 

cartographic, and 

surveying related 

organizations.) 

OpenGIS Specifications General 

purpose 

Integration 

Interfaces 

Technical documents 

that detail interfaces 

and encodings to enable 

geoprocessing 

technologies to 

interoperate. 

Industry 

specification 

The Open Geospatial 

Consortium, Inc (OGC) 

Vector Product Format 

(VPF) 

General 

purpose 

Integration 

Interfaces 

A format, structure, and 

organization for large 

geographic databases 

that are based on a 

georelational data 

model and are intended 

for direct use. 

Military standard: 

MIL-STD-2407 

National GeospatialIntelligence Agency 

Content Standard for 

Digital Geospatial 

Metadata (CSDGM) 

General 

purpose 

Integration 

Interfaces 

A specification that 

provides a common set 

of terminology and 

definitions for the 

documentation of 

digital geospatial data. 

Standard: 

FGDC-STD-001-

1998 

Federal Geographic Data 

Committee (FGDC) 

Computer Graphics 

Metafile (CGM) 

Document 

formats 

A specification for 

storage and exchange of 

virtual two-dimensional 

static pictures. 

Standard: 

ANSI/ISO 8632-

1992 

International 

Standardization 

Organization (ISO), and 

American National 

Standards Institute 

(ANSI) 
77

Standard/Specification 

Name 

Information 

Category 

Description Standardization Responsible 

Organization 

Extensible Markup 

Language (XML) 

Document 

formats 

A simple, flexible text 

format derived from 

SGML for exchange of a 

wide variety of data on the 

Web and elsewhere. 

Industry 

specification: 

XML 1.0 and 

XML 1.1 

World Wide Web 

Consortium (W3C) 

Flexible Image 

Transport System 

(FITS) 

Document 

formats 

A data format designed to 

provide a means for 

convenient exchange of 

astronomical data between 

installations whose 

standard internal formats 

and hardware differ. It is 

used for the transport, 

analysis, and archival 

storage of scientific data 

sets. 

Government 

specification 

National Aeronautics 

and Space 

Administration 

(NASA) 

Graphics Interchange 

Format (GIF) 

Document 

formats 

A data stream-oriented 

format for the storage and 

transmission of rasterbased graphics 

information. 

Industry 

specification: 

GIF89a 

CompuServe, 

Incorporated 

Hierarchical Data 

Format (HDF) 

Document 

formats 

A set of file formats 

designed to facilitate 

sharing of scientific data. 

HDF4 - a file format that 

supports raster image, 

array, palette, group, 

annotation, and 

multidimensional 

table.HDF5 - a file format 

and library, includes 2 

primary objects, dataset (a 

multi-dimensional array of 

records ) and group (a 

structure for grouping 

objects.) 

Industry 

specification: 

GIF89a 

National Center for 

Supercomputing 

Applications (NCSA), 

and The HDF Group 

(THG) 
78

Standard/Specification 

Name 

Information 

Category 

Description Standardization Responsible 

Organization 

Joint Photographic 

Experts Group (JPEG) 

Document 

formats 

An image compression 

format used to transfer 

color photographs and 

images over computer 

networks. 

Standard: 

ISO/IEC IS 

10918-1 | ITU-T 

Recommendation 

T.81 

International 

Organization for 

Standardization (ISO), 

and Joint Photographic 

Experts Group (JPEG) 

Portable Network 

Graphics (PNG) 

Document 

formats 

An extensible file format 

for the lossless, portable, 

well-compressed storage of 

raster images. It supports 

online viewing 

applications. 

Standard: 

ISO/IEC 

15948:2003 

International 

Organization for 

Standardization (ISO), 

and World Wide Web 

Consortium (W3C) 

Tagged Image File 

Format (TIFF) 

Document 

formats 

A public domain tag-based 

file format for storing and 

interchanging raster 

images. 

Industry 

specification: 

TIFF6.0 

Adobe Systems 

The Collaborative 

Design Activity 

(COLLADA) 

Document 

formats 

A public domain, Digital 

Asset schema for 

interactive 3D applications 

that contain features 

including programmable 

shader effects and physics 

simulation. 

Standard: 

ISO/IEC 

15948:2003 

International 

Standardization 

Organization (ISO), 

and The Khronos 

Group 

Truevision TGA File 

Format or Truevision 

Advanced Raster 

Graphics Adapter 

(TARGA) File Format 

Document 

formats 

A format for defining raster 

or bitmap images. 

Industry 

specification: 

TGA, Version 

2.0 

Truevision (now 

Pinnacle Systems) 
79

Standard/Specification 

Name 

Information 

Category 

Description Standardization Responsible 

Organization 

Web Feature Service 

(WFS) 

General 

purpose 

Integration 

Interfaces 

An interface service for 

data access and 

manipulation operations 

on geographic features, 

using HTTP as the 

distributed computing 

platform. 

Industry 

specification: 

OGC 04-094, 

Version 1.1.0 

The Open Geospatial 

Consortium, Inc 

(OGC) 

Web Map Service 

(WMS) 

General 

purpose 

Integration 

Interfaces 

An interface service in 

support of the creation 

and display of registered 

and superimposed maplike views of 

information that come 

simultaneously from 

multiple remote and 

heterogeneous sources. 

Industry 

specification: 

OGC 03-109r1, 

Version 1.3.0; 

ISO/DIS 19128 

The Open Geospatial 

Consortium, Inc 

(OGC) 

X3D General 

purpose 

Integration 

Interfaces 

An is an open standard 

for 3D content delivery. 

It provides a system for 

the storage, retrieval and 

playback of real time 

graphics content 

embedded in 

applications, all within 

an open architecture to 

support a wide array of 

domains and user 

scenarios. 

Industry 

specification: 

ISO/IEC 19774 

(Humanoid 

Animation); 

ISO/IEC 19775 

(the abstract 

specification); 

ISO/IEC 19776 

(X3D XML and 

VRML encodings) 

International 

Standardization 

Organization (ISO), 

and The Web3D 

Consortium 

Guidelines for Design 

and Construction of 

Health Care Facilities 

Operational 

guidelines 

A specification 

referenced by architects, 

engineers, and health 

care professionals who 

are planning new or 

renovated health care 

facility construction. 

Guidelines for 

Design and 

Construction of 

Health Care 

Facilities, The 

2006 Edition 

American Institute of 

Architects (AIA) 

Massachusetts 

Department of Public 

Health 

Operational 

guidelines 

Mirrors AIA Guidelines 

for Design and 

Construction of Health 

Care Facilities 

Based on AIA 

Guidelines 

Massachusetts 

Department of Public 

Health 
80

Life Safety Code 

(NFPA 101) 

Operational 

guidelines 

A specification provides 

the rules for sprinklers, 

alarms, egress, 

emergency lighting, 

smoke barriers, special 

hazard protection--the 

complete range of 

construction, protection, 

and occupancy features 

that impact lives every 

day. 

The 2009 NFPA 

101: Life Safety 

Code 

National Fire 

Protection Association 
81

APPENDIX D. ACRONYMS AND ABBREVIATIONS 

ADA Americans with Disabilities Act 

AHRQ Agency for Health Care Research and Quality 

AIA American Institute of Architects 

ANSI American National Standards Institute 

ASPR Assistant Secretary for Preparedness and Response 

BBN Bayesian Belief Network 

CDC Centers for Disease Control and Prevention 

CEDRS Comprehensive Emergency and Disaster Response Simulation 

CGM Computer Graphics Metafile 

CIMIT Center for Integration of Medicine and Innovative Technology 

CIS The CIMSteel Integration Standards 

CMSD Core Manufacturing Simulation Data 

COLLADA The Collaborative Design Activity 

COMET Combat Medic Training System 

CSDGM Content Standard for Digital Geospatial Metadata 

DHHS Department of Health Human Services 

DHS U.S. Department of Homeland Security 

DICOM Digital Imaging and Communications in Medicine 

DISTEX Distant Exercises 

DoD U.S. Department of Defense 

DXF Drawing Interchange File Format 

ECCC Emergency Care Coordination Center 

ED Emergency Department 

EPA U.S. Environmental Protection Agency 

ESRI Environmental Systems Research Institute 

FDA U.S. Food and Drug Administration 

FEA Finite Element Analysis 

FGDC Federal Geographic Data Committee 

FITS Flexible Image Transport System 

GAO U.S. Government Accountability Office 

GGHC Green Guide for Health Care 

GIF Graphics Interchange Format 

GRRRS Goals, Roles, Relationships, Resources, Structure 

HAZMAT Hazardous Material 

HCO Health Care Organization 

HDF Hierarchical Data Format 

HEAT Health Care Efficacy Architectural Analysis Tool 

HIPAA Health Insurance Portability and Accountability Act 

HL7 Health Level Seven 

HUREVAC Hurricane Evacuation 

IAI The International Alliance for Interoperability 

IC Infection Control 

ICE Integrated Clinical Environment 

ICD The International Classification of Disease 

ICRA Infection Control Risk Assessment 

ICU Intensive-Care Unit 

IEEE Institute of Electrical and Electronics Engineers 

IFC The Industry Foundation Classes 

IHI Institute for Health Improvement 

ILSM Interim Life Safety Measures 

IOM Institute of Medicine 
82

ISO International Standardization Organization 

IT Information Technology 

JPEG Joint Photographic Experts Group 

MATCH Multidisciplinary Assessment of Technology Center for Healthcare 

M&S Modeling and Simulation 

NASA National Aeronautics and Space Administration 

NBIM National Building Information Model 

MMOG Massively Multiplayer Online game 

NCHS The National Center for Health Statistics 

NCSA National Center for Supercomputing Applications 

NFPA National Fire Protection Association 

NIBS National Institute of Building Sciences 

NIH National Institutes of Health 

NIST National Institute of Standards and Technology 

OGC The Open Geospatial Consortium, Inc. 

OR Operating Room 

PAHPA Pandemic All Hazards Preparedness Act 

PCA Patient-Controlled Analgesia 

PDA Personal Digital Assistant 

PNG Portable Network Graphics 

POD Point of Dispensing 

RFID Radio Frequency Identification 

RIPS Real-time Incident Preparedness 

SCI The Steel Construction Institute 

SDTS Spatial Data Transfer Standard 

SOA Service Oriented Architecture 

SoS System of Systems 

TARGA Truevision Advanced Raster Graphics Adapter File Format 

TC3 Tactical Combat Casualty Care 

THG The HDF Group 

TIFF Tagged Image File Format 

TIG Technical Interest Group 

UIMA Unstructured Information Management Architecture 

USGS U.S. Geological Survey 

USNG Standard for a United States National Grid 

VPF Vector Product Format 

WFS Web Feature Service 

WHO World Health Organization 

WMS Web Map Service 

W3C World Wide Web Consortium 

XML Extensible markup Language 
83

Disclaimer 

No approval or endorsement of any commercial software by the National Institute of Standards and Technology is 

intended or implied. Certain commercial software and related materials are identified in this report to facilitate 

understanding. Such identification does not imply recommendation or endorsement by the National Institute of 

Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best 

available for the purpose. 

The work described was funded by the United States Government and is not subject to copyright.