Special Topics in Vendor-Specific Systems Assessing Decision Support Capabilities of Commercial EHRs This material (Comp14_Unit7) was developed by Columbia

Special Topics in Vendor-Specific Systems

Assessing Decision Support Capabilities of Commercial EHRs

This material (Comp14_Unit7) was developed by Columbia University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 1U24OC000003.


Learning Objectives

2

In this unit we will focus on the history of clinical decision support systems and why they are important for electronic records. In addition, after completing this unit, you will be able to articulate some of the decision support capabilities and customization capabilities of various vendor systems.

Health IT Workforce Curriculum Version 3.0/Spring 2012

Special Topics in Vendor-Specific Systems Assessing Decision Support Capabilities of

EHRs

Clinical Decision Support

• “…any computer program designed to help health professionals make clinical decisions…deal with medical data about patients or with the knowledge of medicine necessary to interpret such data.”

3Health IT Workforce Curriculum Version 3.0/Spring 2012


EHRs

Types of CDS Applications

• Expert Systems– Primary intended as diagnostic aids

• Alerts/Reminders– Interruptive or passive



EHRs

Diagnostic Expert Systems

• Generate differential diagnosis based on list of user-entered findings



EHRs


• INTERNIST-I (1974) • Rule-based expert system designed at the University

of Pittsburgh (R. Miller, H. Pople, V. Yu)• Diagnosis of complex problems in general internal

medicine• Designed to capture the expertise of just one man,

Jack D. Myers, MD, chairman of internal medicine in the University of Pittsburgh School of Medicine

• It uses patient observations to deduce a list of compatible disease states



EHRs


• Quick Medical Reference (QMR)• In the mid-1980s, INTERNIST-I was succeeded by a

microcomputer-based consultant developed at the University of Pittsburgh called Quick Medical Reference (QMR)

• QMR was intended to rectify the technical and philosophical deficiencies of INTERNIST-I

• QMR remained dependent on many of the same algorithms developed for INTERNIST-I, and the systems are often referred to together as INTERNIST-I/QMR



EHRs


• MYCIN (1976)• Developed at Stanford University as the doctoral

dissertation of Edward Shortliffe • Written in LISP• Rule-based expert system designed to diagnose and

recommend treatment for certain blood infections (extended to handle other infectious diseases)

• Clinical knowledge in MYCIN is represented as a set of IF-THEN rules with certainty factors attached to diagnoses



EHRs

Diagnostic Expert Systems• DXplain

• Laboratory of Computer Science at the Massachusetts General Hospital (Barnett GO, Cimino JJ, et al.)

• Uses a set of clinical findings (signs, symptoms, laboratory data) to produce a ranked list of diagnosis using a Bayesian Network

• Knowledge base has 2,200 diseases and 5,000 symptoms

• Provides justification for why each of these diseases might be considered, suggests what further clinical information would be useful to collect for each disease



EHRs

Types of CDS Applications



EHRs

• Expert Systems– Primary intended as diagnostic aids

• Alerts/Reminders– Interruptive or passive

Alerts/Reminders

• Tools for focusing attention– Remind the clinician of issues that might be

overlooked

• Examples– Clinical laboratory systems that alert clinicians

of critical abnormal results– CPOE systems that alert ordering providers of

possible drug interactions or incorrect drug dosages



EHRs

Why Alerts/Reminders Are Needed

• It is simply unrealistic to think that individuals can synthesize in their heads scores of pieces of evidence, accurately estimate the outcomes of different options, and accurately judge the desirability of those outcomes for patients.



EHRs

Computerized Reminders – Early Efforts

• It appears that [computerized] prospective reminders do reduce errors, and that many of these errors are probably due to man's limitations as a data processor rather than to correctable human deficiencies

McDonald, CJ. (1976).



EHRs

Example Alert

Vawdrey, D. (2010).



EHRs

Arden Syntax

• The Arden syntax is an artificial intelligence (AI) frame-based grammar for representing and processing medical conditions and recommendations as “Medical Logic Modules (MLMs)”

• Intent was for MLMs to be used in shared across EHRs• Arden syntax is now part of HL7• The name, "Arden Syntax", was adopted from Arden

House, the upstate New York location where early meetings held to develop and refine the syntax and its implementation



EHRs

Example MLM

Vawdrey, D. (2010).



EHRs

penicillin_order :=

event {medication_order

where class = penicillin};

data:

penicillin_allergy :=

`read last {allergy

where agent_class = penicillin};

;;

evoke: penicillin_order ;;

logic:

If exist (penicillin_allergy) then conclude true;

endif;

;;

action:

write

"Caution, the patient has the following documented allergy to penicillin: "

|| penicillin_allergy ;;

Use of the Arden Syntax



EHRs

• The Regenstrief Institute, Inc. uses Arden Syntax MLMs in its CARE system to deliver reminders or hints to clinicians regarding patient treatment recommendations

• LDS Hospital in Salt Lake City (HELP System) contributed much to this standard as well as the general body of knowledge

• Eclipsys Sunrise uses Arden Syntax MLMs to provide decision support capabilities

• Siemens and other EHR vendors also use Arden Syntax

Epic UserWeb - Community Library

• Contains 15,000 clinical decision support rules known as Best Practice Alerts that are shared among Epic users

• Content is human readable

• The UserWeb has 12,000 active users (2008)



EHRs

Epic UserWeb Example Screen

Wright AB et al.(2008).



EHRs

Rules for Implementing CDS Alerts

Communication and acceptance:

• 1. Has the clinical rule or concept that will be promoted by the intervention been well communicated to the medical staff in advance?

• 2. Does the intervention, if accepted, change the overall plan of care, or is it intended to cause a limited, corrective action (such as preventing an allergic reaction to a drug)?

• 3. Are the data used to trigger the alert likely to be accurate and reliable, and are they a reliable indicator for the condition you are trying to change?

• 4. What is the likelihood that the person receiving the alert will actually change his or her patient management as a result of the alert?

• 5. Is the patient likely to agree that the recommended actions are beneficial?



EHRs

Rules for Implementing CDS Alerts

Intervention technique:

• 6. Is an alert the right type of intervention for the clinical objective, and is it presented at the right time?

• 7. Is the intervention presented to the right person?

• 8. Is the alert presented clearly, and with enough supporting information, so that the clinician feels confident in taking the recommended action immediately?

• 9. Does the intervention slow down the workflow?

• 10. Is the overall alert burden excessive (“alert fatigue”)? Were the study providers receiving other types of alerts at the same time?

• 11. Is the clinical information system, including the use of CDS (e.g., the alerts), well-liked and supported by clinicians in general?

Monitoring:

• 12. Is there a way to monitor the response to the alert on an ongoing basis?



EHRs

Integrating Alerts into the Clinical Workflow

Vawdrey, D. (2010).



EHRs

Case Study: Workflow Integration

• EHRs historically have been difficult for customers to customize or modify due to the closed architecture employed by most vendors

• Helios– Open Architecture platform enabling custom

development and/or integration of third-party modules

• Use case: Write a note in an inpatient EHR system and submit a professional bill in a separate outpatient EHR



EHRs

Allscripts Custom Integrated Billing Solution

Vawdrey, D. (2010).



EHRs

Integrated Billing Solution:Technical Architecture

Vawdrey, D. (2010).



EHRs

Allscripts (Eclipsys) Sunrise Acute CareAllscripts (Eclipsys) Sunrise Acute Care

Allscripts Enterprise EHR V11

ObjectsPlus (Helios)ObjectsPlus (Helios) Unity

BillingSystem

Web Service Calls


Summary

• All EHR vendors provide decision support capabilities and options for customization

• Sharing content with other organizations may be desirable

• Vendor adoption of industry standards and “open architecture” may benefit EHR users



EHRs


ReferencesReferences:

1. McDonald, CJ. (1976). Protocol-based computer reminders, the quality of care and the non-perfectability of man. N Engl J Med; 295(24): 1351-5.

2. Shortliffe, E.H. (1987). Computer programs to support clinical decision making. JAMA, vol.258(1), p61-66. 3. Shortliffe, E.H. (1976). Computer-Based Medical Consultations: MYCIN, Elsevier/North Holland, New York.

4. Miller, A.R., Pople, E.H., Myers, D.J. (1982). Internist-I, and experimental computer-based diagnostic consultant for general internal medicine. New England Journal of Medicine, vol307 (8), p.468-476.

5. Sittig, D.F., Teich, J.M., Osheroff, J.A., Singh, H. (2009). Improving Clinical Quality Indicators Through Electronic Health Records: It Takes More Than Just a Reminder. Pediatrics, 124;375.

6. Miller RA., and Masarie FE Jr. (1989). Use of the Quick Medical Reference (QMR) program as a tool for medical education. Methods Inf Med.;28:340-345.

7. Shortliffe, E.H. (1976). Computer-Based Medical Consultations: MYCIN, Elsevier/North Holland, New York

8. Barnett, O.G., Cimino, J.J., Hupp, J.A., Hoffer, E.P. (1987). Dxplain: an evolving diagnostic decision –support system. JAMA, vol258 (1), p.67-74.

9. Eddy, D.M. (1990). Anatomy of a decision. JAMA, vol.263(3), p.441-443.



EHRs


ReferencesImages:

Slide 14, 22: Vawdrey, D. (2010). Clinical workflow alert system. Department of Biomedical Informatics, Columbia University Medical Center.

Slide 16: Vawdrey, D. (2010). Personal syntax: example of MLM. Department of Biomedical Informatics at Columbia University Medical Center.

Slide 19: Wright AB et al. Creating and sharing clinical decision support content with Web 2.0: Issues and examples. J Biomed. Inf. (42:2), 2008, 334-346.

Slide 24: Vawdrey, D. (2010). Allscripts custom integrated billing solution . Department of Biomedical Informatics, Columbia University Medical Center.

Slide 25: Vawdrey, D. (2010). Integrated billing solution: technical architecture. Department of Biomedical Informatics at Columbia University Medical Center



EHRs

Documents

Special Topics in Vendor-Specific Systems Assessing Decision Support Capabilities of Commercial EHRs This material (Comp14_Unit7) was developed by Columbia