Special Topics in Vendor-Specific Systems Unit 7 Assessing decision support capabilities of commercial EHRs

Health IT Workforce Curriculum Version 1.0/Fall 2010 Objective Compare decision support capabilities and customizability options of vendor EHRs Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Objective (cont.) After this completing this unit, you should be able to: Understand the importance of clinical decision support (CDS) systems Describe decision support capabilities and customization capabilities of different vendor EHRs Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Clinical Decision Support (CDS) “…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.” EH Shortliffe JAMA 1987;251:61-6 Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Types of CDS Applications Expert Systems Primary intended as diagnostic aids Alerts/Reminders Interruptive or passive Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Diagnostic Expert Systems Generate differential diagnosis based on list of user-entered findings Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Diagnostic Expert Systems (con’t) 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 Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Diagnostic Expert Systems (con’t) 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 Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Diagnostic Expert Systems (con’t) 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 Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Diagnostic Expert Systems (con’t) 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 Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Types of CDS Applications Expert Systems Primary intended as diagnostic aids Alerts/Reminders Interruptive or passive Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Health IT Workforce Curriculum Version 1.0/Fall 2010 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 Computerized Provider Order Entry (CPOE) systems that alert ordering providers of possible drug interactions or incorrect drug dosages Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

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. David M. Eddy, MD, PhD JAMA 1990; 263:1265-1275 Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

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 Protocol-based computer reminders, the quality of care and the non-perfectability of man McDonald CJ. N Engl J Med; 295(24): 1351-5, Dec. 9, 1976 Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Health IT Workforce Curriculum Version 1.0/Fall 2010 Example Alert Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

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 Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Example Medical Logic Modules (MLM) 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 ;; Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Use of the Arden Syntax Siemens and other EHR vendors also use Arden Syntax Eclipsys Sunrise uses Arden Syntax MLMs to provide decision support capabilities LDS Hospital in Salt Lake City (HELP System) contributed much to this standard as well as the general body of knowledge The Regenstrief Institute, Inc. uses Arden Syntax MLMs in its CARE system to deliver reminders or hints to clinicians regarding patient treatment recommendations Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

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) Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010 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.

Epic UserWeb Example Screen Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

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? Pediatrics Sittig et al. 124 (1): 375 Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Rules for Implementing CDS Alerts (con’t) 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? Pediatrics Sittig et al. 124 (1): 375 Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Integrating Alerts into the Clinical Workflow Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Case Study: Allscripts/Eclipsys Helios 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 Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Allscripts/Eclipsys Integrated Billing Solution Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

Integrated Billing Solution: Technical Architecture Eclipsys Acute Care 5.0 Allscripts Enterprise EHR V11 Billing System Past Diagnoses, Charge Favorites, ICD & CPT Master Provider, Patient, Problems, CPT4, Note Identfier Provider, Patient, Diagnoses Diagnoses (Health Issues) ObjectsPlus (Helios) Universal Application Integrator (UAI) Web Service Calls Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010

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 Component 14/Unit 7 Health IT Workforce Curriculum Version 1.0/Fall 2010