# Presentation: 560415

## Use of Clinical Decision Support Expert Rules within Clinician Workflows:Improving Quality of Care for Vulnerable Populations Through HIT – AHRQ 2008 Conference

**Jane M. Brokel, PhD, RN**
**Assistant Professor, University of Iowa**
**Informatics Consultant – Trinity Health**

**Notes:**
*Jane Brokel  title with abstract. 
Clinician decision support (CDS) interventions integrated within clinical workflows supports clinician use of Electronic health care record (EHR) EHR in rural community hospital and clinics

## Inpatient Setting

- Outline - Background

- Rural Iowa Redesign of Care Delivery with EHR Functions  and Collaborative EHR- Implementation to Bridge the Continuum of Care in Rural Iowa
-  Provide challenges and success of implementing evidence-based practices with clinical decision support tools.
- Share examples.
- Provide a model to approach Clinical Decision Support aligned with an implementers guide.

- Copyright 2007 Trinity Health – Novi, Michigan

**Notes:**
*Inpatient setting. Collaborative EHR10 &  Rural Iowa Redesign of Care Delivery with EHR Functions>       Implementation to Bridge the Continuum of Care in Rural Iowa.>       Jane will focus on the challenges and success of implementing>       evidence-base care guidelines with clinical decision support>       tools for a few conditions; she will share examples on falls and/or health>       condition.

Illustration includes map of the 48 contiguous states with California, Idaho, Iowa, MI, Indiana, Ohio, Maryland and Delaware highlighted as part of the health system.  A sketch of Iowa with several counties highlighted is featured as the study site.

## Clinical Decision Support Definition

- “**Providing clinicians or patients with clinical knowledge** and patient-related information, intelligently filtered or presented at appropriate times, **to enhance patient care**.”  Osteroff et al, 2006
*Many interventions that are already being done on a daily basis in healthcare organizations... *
_*Rules are one of the interventions*_

**Notes:**
In 2006, the American Medical Informatics Association  developed a framework to begin the work of coordinating structures and processes to facilitate the use of clinical decision support.  This was led by Dr. Jerome Osteroff and many others.  I’ll focus on one type of clinical decision support intervention that was used in the AHRQ Research Studies in Rural Iowa. 

## Methods: Formative Evaluation of Implementation

| Findings:   Clinical Decision Support Rules for Use |  |  |  |  |
| --- | --- | --- | --- | --- |
| Use Location  | System Use | Highly Recommend | Optional Use | Pilot Use |
| Hospital  | 22 | 16 | 12 | 4 |
| Clinics  | 17 | 0 | 0 | 0 |

- _Descriptive Findings:_
**Rules to Prevent Error  4**
**Rules to Promote Safety 6**
**Rules to Use Evidence-based Practices - 17 Clinics and 17 Hospital **
**Rules to Promote Interdisciplinary Communication – 25**
**Rules for other reasons - 3 **

- Copyright 2007 Trinity Health – Novi, Michigan

**Notes:**
Key Findings – The overall study evaluated the implementation of an EHR in a rural referral setting that would be subsequently used also in critical access hospitals that are implementing a similar EHR. 
Successes: 
Setting Priorities for appropriate use of clinical decision support. – Regulatory requirements; Market-driven; Critically Appraised Literature
Reusable clinical decision support rules - specifications from Urban to Rural referral to Critical Access.  (In 2005 – 41% of the CDS rules used were used also by all urban hospitals and replicated for rural hospital) 
When a Rule is designed should it be used in more sites.  In 2008 nearly 50% of over 220 rules are used across the system.  
Not all requests were processed into clinical decision support rules.  About 4-7% of requests can not be developed into rules due to technical capacity, lack of evidence to support the design, lack of data available for use, or impact to a clinician’s workflow.   A rule may have positive benefits to one discipline and negative impacts to another discipline. 

## Clinical Decision Support in Workflow Process

| Clinical Workflow | # Rules | # To Prevent Error | # To Promote Safety | # To Use Evidence-based Practice | # To Promote
Communication |
| --- | --- | --- | --- | --- | --- |
| Medication
Pharmacy  | 5 | 1 | 2 | 0 | 2 |
| Medical Staff/PCP | 21 | 0 | 0 | 17 | 4 |
| Maternal-Child | 6 | 0 | 0 | 5 | 1 |
| Pediatric | 2 | 0 | 0 | 2 | 0 |
| Nursing Care | 15 | 2 | 1 | 6 | 6 |
| Behavioral Health | 4 | 0 | 1 | 2 | 1 |
| Nutrition Service | 7 | 0 | 0 | 0 | 7 |
| Infection Control | 2 | 0 | 0 | 2 | 0 |
| Diagnostic Radiology | 3 | 1 | 2 | 0 | 0 |
| Rehabilitation | 1 | 0 | 0 | 0 | 1 |
| Social Worker | 2 | 0 | 0 | 0 | 2 |
| Spiritual Care | 1 | 0 | 0 | 0 | 1 |

- Copyright 2007 Trinity Health – Novi, Michigan

**Notes:**
The initial 2005 implementation of the EHR included 54 clinical decision support rules in 13 clinical workflow processes to support goals and objectives with clinician workflows.  In 2007 the clinics added 17 rules and today there are 95 rules.

Challenges: 
In 2004 preparations the rules were one of many applications offered to clinicians with the EHR. One of the key learning’s was to integrate each clinical decision support rule within the workflow of the patient and clinician.   This change in 2005 supported use of rules with less confusion on what rules to use.  Each rule has a purpose to support the decisions and actions. 
Requesting evidence to support requests for the design.  The rural hospital piloted 4 new rules and 4 requests were rejected. 

## Fall Risk – Clinical Decision Support Rule

**Triggering event**

**Triggered action response**

- Copyright 2007 Trinity Health – Novi, Michigan

**Data input **

**Evaluation of conditional limits**

**Inclusions**

**Exclusions**

**Evidence**

## Fall Risk – Clinical Decision Support Rule

**Clinician **
**Performed Response**

**Patient Outcome: **
**Safe mobility; no injuries**

- Copyright 2007 Trinity Health – Novi, Michigan

**Organizational Outcome:  costs to reimbursement margin; staffing impacts**

**Society Outcome: Safety**
**    ****Reduction of costs**

**Fall Prevention Intervention & **
**Teaching Patients/Family**

**Notes:**
Example of a rules operational aspects: Continued with the steps after the triggered action response. 
Left –side shows care set of nursing interventions ordered for high risk patients. 

Far right-hand side shows illustration of three people walking to illustrate a good patient outcome, safe mobility  

## Copyright 2007 - Brrokel

**Notes:**
Since nurses are the largest group of healthcare professional disciplines interacting with the EHR, the model highlights the decision support for the nurse clinician’s interaction and use of patient data with Clinical Decision Support Rules. Nurses document large amount of data within the EHR, these data are used to trigger events to evaluate the conditional limits of data to produce actionable responses. The schematic model illustrates how data are entered, uploaded from devices, or transmitted into the EHR from other applications within the framework of information and data use. The arrows depict data use for required elements in CDSR operation. The model represents the rule’s  application use of data in the “Trigger,” the “Conditional Limits, and the “Action Response.” the arrows flow into and out from the Clinician Workflow Process at the point of “Action Response” within the CDS rule.  The Action Response includes data, information, or actions presented to the clinician that support a person’s decision in the “Clinician Performed Response” to perform interventions or assessments. What is important to note that state laws, professional practice standards, organizational policy, and legal interpretation avow practice that also guide the rule’s design for clinician decisions and actions. 
Inherent in this design is the unequivocal need for translation of research evidence into practice14 to support valid and reliable use of information within each rule, thus the evidence arrows point to the Information Science and Computer Science, as well as professional standards that direct the Clinician Workflow. The clinician’s actions with the data used in the CDS rule displays in clinician workflow processes (e.g. nursing process). Each discipline interacts with CDS rule in an initiator role that triggers the events, or in a receiver role to decide the action response during the patient care workflow. Due to the dynamic nature of evidence, workflow processes and policy changes, the model includes “Change Control Procedures” which serve to identify the processes to initiate, manage, evaluate and upgrade the CDS rules safely over time. 

## Fall Risk Data Model

- _Patient Data_
- Admission into MO Facility

- _Trigger - Documented Data Elements Used: _ Assessment limits (eforms)
- Medication Review:  Patient considered High Risk for Falls (Yes)  (Boolean field DTA)
-    Finds Meds– antihypertensive agents, ACE inhibitors, diuretics, trycyclic antidepresants, antianxiety agents, opiates, hypnotics or tranquilizers.	OR
- Morse Fall Risk (eform: calculated field) x > 44 (DTA) or Morse Fall Risk 0 <  x < 44 (DTA) OR
- Date & time of Fall eform SNOMED-CT-  Fall event 1912002
- Admission Profile -  Social section - alcohol use; Lives alone;  Lower Limb Prothesis;  Currently uses an assistive device; Gender - Female (elder) or Male (toddlers)

- _Action Response_
-  CDS message At Risk for Falls and actions taken
-  CDS actions: 1) Order _Activate Fall Prevention (TH) Orderset_
- 2) Add Problem “At Risk for Falls” if not pre-existing Problem List

- _Performed Response_
- Order the Fall Prevention Order set  (required, pre-selected or optional orders) – Existing orders found
- Documented Nursing Interventions (where are NIC data entered)  Fall Prevention (form), Dementia management (form), Surveillance: Safety (form)
- Patient Education (from) completed

- _Patient Outcome_
-  No Falls (2.3 to 7 falls per 1000 patient days )
-  Fall Prevention Behaviors demonstrated such as use assistive devices,

- _Primary Responding Clinician _
- Role  RN, (other disciplines charting falls – PT, OT)
- Relationship ( Encounter)
- _Secondary Responding Clinician_
-   Specialties (NA)

- _Conditional_ _Limits  - Inclusions_
- IC:Age > 17 for adults (or Age < 2 – children)
- IC:Encounter type  - Inpatient types HOSPICEINPATIENTP2; INPATIENT; IPMAJORSURGERY; LABORDELIVERYP2; LONGTERMCAREP2; OBSERVATIONP2; PSYCHIATRICINPATIENTP2; REHABINPATIENTP2; RESIDENTIALINPATIENTP2; SKILLEDNURSINGFACILP2

- _Conditional_ _Limits  - Exclusions_
- Problem List: Nursing diagnosis– At Risk for Falls r/t  history of falls (SNOMED CT code 129839007
- Date & time of Fall form SNOMED-CT-  Fall event 1912002
- Medication Review:  Patient considered High Risk for Falls (Yes)

- Future Options

**Notes:**
The use of a Data model identifies Primary  use of data for designing the rules as well as secondary use of data to evaluate the rules.

Challenges:  Evaluation of transactional data was available only through the production clinical data repository but was not extracted into the Extended data warehouse.   Steps to ensure the triggered action response can be evaluated outside of production domain is key 

## Risk for Impaired Skin Integrity - Rule

**Automation – Patient’s Problem List  - Risk for **
**Impaired Skin Integrity**

**Clinician Performed Response – order set**

- Copyright 2007 Trinity Health – Novi, Michigan

**Notes:**

The CDS rules places the problem for Risk for Impaired Skin Integrity on the problem list and order interventions. The Braden assessment was licensed for use in the organizations. 
cost of a pressure ulcer estimated treatment $500 to $40,000 per ulcer.  Important to intervene with Skin Surveillance, Pressure Management or Pressure Ulcer Preventions initiated by nurses. 

## Skin IntegrityData Model

- _Patient Data_
- Admission into MO Facility

- _Trigger - Documented Data Elements Used: _ Assessment limits (eforms)
- Braden Scale Review:
-    Finds Meds:
- Date & time;

- _Action Response_
-  CDS message
-  CDS actions: 1) Order _Activate .......(TH) Orderset_
- 2) Add Problem “.......if not pre-existing Problem List

- _Performed Response_
- Order the ........ Order set  (required, pre-selected or optional orders) – Existing orders found
- Documented
- Patient Education (from) completed

- _Patient Outcome_
-  No Pressure Ulcer

- _Primary Responding Clinician _
- Role  RN, (other disciplines charting falls – PT, OT)
- Relationship ( Encounter)
- _Secondary Responding Clinician_
-   Specialties (NA)

- _Conditional_ _Limits  - Inclusions_
- IC:Age >
- IC:Encounter type  - Inpatient types HOSPICEINPATIENTP2; INPATIENT; IPMAJORSURGERY; LABORDELIVERYP2; LONGTERMCAREP2; OBSERVATIONP2; PSYCHIATRICINPATIENTP2; REHABINPATIENTP2; RESIDENTIALINPATIENTP2; SKILLEDNURSINGFACILP2

- _Conditional_ _Limits  - Exclusions_
- Problem List: Risk for Pressure Ulcer
- Date & time
- Medication Review:

- Future Options

**Notes:**
       

## Challenges – Clinical Decision Support (CDS)

- 1. Priorities: Specific CDS goals & objectives
- High alerts?   CHF?   Diabetes?  Medication exchange?
- 2. Design:  Capabilities to address goals
- CDS Types of Interventions?   Clinical solutions?
- 3. Share & Select: Specifications for CDS rules or interventions to achieve goals within workflow
- 4. Test & Launch in Rural areas
- 5. Monitor & Maintain
- 6. Evaluate interventions impact = outcome effect

**Notes:**
Develop plan for optimizing CDS functionality of CIS to support Med utilization and target outcomes 

Try Challenges to sustain over time with weekly changes to the EHR. 
Applying CDS to improve outcomes in healthcare organizations:
Identify CDS stakeholders, and with them determine specific CDS goals and objectives (Evidence-based practice standards - CHF, Diabetes, Skin Integrity, Fall Prevention) Valid design
Catalogue information systems infrastructure available to address objectives (HIT technology, RIM & terminology standards - interoperability and control procedures) Maintenance
Select and Specify CDS interventions to achieve goals and objectives within workflow.   (Workflow standards – interdisciplinary care delivery, professional practice)

Test the rules safe operation behind the clinicians view.
Monitoring consistency with pt population
Validate proposed interventions and implementation plan; develop interventions and their logistics. (Workflow standards – accuracy, promptness, appropriateness, completeness) Inter reliability
Evaluate intervention impact; enhance infrastructure for  interventions needed.  Outcomes effectiveness:::Review results of value for CDS

Graphic shows an animated person choosing between an apple and a pear to illustrate the challenge of selecting the correct specifications for CDS Rules or interventions.

## National Pressures

**Institute**** ****Safe    Medication Practices**

**Joint Commission**

| Patient Safety | Surgical sites |
| --- | --- |
| Sentinel Events | Pediatric dosing |

- Copyright 2007 Trinity Health – Novi, Michigan

**Health Information Technology Standards Panel**

- Medication Mgmt Use Case IS07
**	****Interoperability specification that defines specific standards to facilitate access to medication and allergy information **
**	**

- Wrong dose, rate or frequency;
- Therapeutic duplication – supp/oral
- Effects of drug on Pregnancy;
- Allergies, Drug Interactions;
- Failure to monitor response pre-, during, and post- therapy.

**Notes:**
4 quadrants. Setting priorities: 
High-risk Institute for Safe Medicine Practices (ISMP)

Priority conditions – Medicare and Medicaid (CMS)
Ischemic heart disease
Major depression
Medication Management
Nosocomial infections
Pain control
Pregnancy & childbirth
Severe & persistent mental illness
Stroke
Tobacco dependence
Obesity
Priority conditions – Institute of Medicine (IOM)
Asthma 
Cancer Management 
Diabetes
End of Life with advance organ-system failure
Frailty associated with old age
Hypertension
Immunization

Illustration of various prescriptions is shown for the Institute of Safe Medication Practices

## Acknowledgements

- The research team acknowledges the support of the Agency for Healthcare Research and Quality for Health Information Technology grant #HS015196, Trinity Information Systems and Trinity Health of Novi, Michigan, Mercy Medical Center – North Iowa, The University of Iowa and the University of Missouri Center for Health Care Quality.
- Tammy J. Schwichtenberg, MSN, RN
- Clinical Informatics Manager, Mercy Medical Center – North Iowa, Mason City, IA
- Mike G. Shaw, BSN, RN
- Clinical Analyst, Trinity Information Systems, Farmington Hills, MI
- Douglas Wakefield, PhD\
- Center for Health Care Quality University of Missouri - Columbia, MO
- Marcia M. Ward, PhD
- Dept Health Management and Policy, University of Iowa, Iowa City, IA
- Donald K. Crandall, MD
- Clinical Informatics Consultant, Trinity Health – Novi, MI

## Contact Information

- Brokel, Jane, M.S. PhD, RN
-     Role: Assistant Professor – College of Nursing
-     University of Iowa, Iowa City, IAEmail: _jane-brokel@uiowa.edu_Phone: 319-335-7133
-     Role: Informatics – Consultant
-     Trinity Health – Novi, MI
-     Email: brokelj@trinity-health.org

## Thank You

**Clinical Decision Support Capabilities and Effects in Rural Areas**

- Copyright 2007 Trinity Health – Novi, Michigan