What is Wrong with EMR?

1. What is Wrong with EMR? James J. Cimino , M.D., Columbia University Jonathan M. Teich ,M.D., Ph.D., Partners Healthcare System Vimla L. Patel , Ph.D., DSc. McGill University Jiajie Zhang , (Organizer), Ph.D., UT Houston

2. What is an ideal EMR?

3. This Is The Ideal EMR! Dramatically improve the quality of healthcare Paper-based Records Good Features • Data • Alerts • Reminders • Decision support • Medical knowledge • Communications • Other aids X • At all points of care Bad Features • For all healthcare professionals Electronic Medium X Good Features • At all times Bad Features • Complete • Accurate • Timely

4. X wide acceptance Status of Current EMR many non-trivial problems

5. Four Perspectives • System functions (Cimino) • Clinical (Teich) • Cognitive (Patel) • Human-Computer Interaction (Zhang)

6. System Functions PerspectiveJames Cimino • Two Models of EMRs • Old Vision • New Vision

7. Two Models of the EMR • Financial system add-on • Repository

8. Admission Admission Admission Glucose Glucose Glucose Drug Admin Allergies Primary Diagnosis Other Diagnoses Aspirin Penicillin Reaction Diagnosis/S Diagnosis/S Diagnosis/P Allergy Financial System Add-On Patient

9. Admission Allergies Glucose Drug Admin: Aspirin Allergy: Aspirin Allergy: Penicillin Glucose Diagnosis Glucose Diagnosis Repository Patient

10. Old Vision • Medical record as history • Terminology as codes

11. Problems with the Old Vision • Cognitive overload • Poor coordination of caregivers • Computer unable to help with plan

12. New Vision • Medical record as Daytimer® • Terminology as knowledge base

13. Medical Record as Daytimer® • Future is more important than past • Goals are stated • Alternatives are anticipated • Status can be determined • Computer can help with planning and timing

14. Terminology as Knowledge Base • Coded data become symbols • Care plans can be codified • Computer can share tasks in the care process

15. Promises of the New Vision • Initial status is determined • Problems and tasks codified • Daily notes are updates to status • Progress can be charted • Milestones can be anticipated • Patient’s distance from desired state can be determined • Expert systems can help with state transitions

16. Workflow, scenarios of care, and the EMR Jonathan Teich, MD, PhD November 9, 1999

17. A little disclaimer

19. What are the Winners? • Results review • No work required • E-mail • Immediate benefit • Important or requested alerts • No work, high importance • Non-judgmental, valuable immediate benefit • Order sets

20. What are the Losers? • Supplying reasons for orders • Multiple click sequences • Prednisone tapers without support

21.  EMR’s run into acceptance problems because… • They’re not oriented to the scenario • Immediate effort > Immediate benefit • They’re slow • “Perfect is the enemy of good” • They over-use technology • They have an activation energy • They follow data categories, not clinical scenarios

22. Scenarios – ambulatory • Coming into the office • New patient precis • Familiar patient review • End-of-visit • Phone call

23. Scenarios – inpatient • Rounds • Results review • Orders • Entry and processing • Prioritize tasks among several patients [ED display] • Daily notes • Signing out

24. Scenarios – community/patient • Ask a question • Request a service • Convey information • Do it all without phone tag

25. The ‘bang per buck’ approach • Computerize if: • Immediate Workflow benefit + cost benefit + (future usefulness x lifetime) > Extra work + Extra time • WI + CS + (FU X L) > WE + TE • Example: inpatient notes • Example: ambulatory documentation • Add no-input benefits

26. Property: Workflow • The one-click rule • Common things quickly, uncommon things possible • Associated information

27. Property: Data re-use • Med list -> prescription -> refill • Meds, allergies -> conflict checking • Problem list, dx -> pathway, flu shot reminder • Structured docu -> later default [aging]

28. Other issues • Shortcuts, templates, and pre-fills • Parsimoniousness – problem list • Simple • Usable • Avoiding aggravation • Appropiate alerting modes • Avoiding over-alerting • Inclusive approach – data capabilities

29. Ambulatory documentation • Modes • Dictation • Hand-entry • Structured entry • Structured dictation • Voice recognition • Partial structure • Cost it out

30. Post-high-tech: people, paper, and free text • Sign-out sheets • Structured documentation • Mix structured and free entry

31. “Make something better and nothing worse”

32. Studies of the provider’s day • Tang – time spent in activities • Awoniyi • Lee – what is liked vs. what is used • What problems can be helped by information tools?

33. What’s Wrong With The EMR:A Cognitive Perspective Vimla L. Patel, PhD, DSc, FRSC Cognitive Studies in Medicine Centre for Medical Education McGill University Montreal, Canada

34. EMR and Paper-Based Records • Cognitive artifacts • Embody processes • Promote use of heuristics But, as I will show: • They support different cognitive processes • Paper: Focus is on exploration and discovery • EMR: Focus is on problem solving

35. Category of Information Hand-Written Patient Record Electronic Patient Record 1. Chief Complaint 10 28 2. Past Medical History 13 13 3. Life Style 33 19 4. Psychological Profile 10 11 5. Family History 7 14 6. History of Present Illness 55 27 7. Review of Systems 52 25 8. Physical Examination 60 55 9. Diagnosis 14 9 10. Investigation 29 17 11. Treatment 21 24 Total Entries 304 252 Information in EMR and Hand-Written Records

36. Information Management and EMR Use % of Record Contents

37. First section from paper-based record(Pre-CPR) 74 year old woman, whose diagnosis was made in February, as she complained of polyuria/nocturia and fatigue for a few years. She was told her sugar was very high and she was sent to Dr. K., who started her on Diabeta 5 mg/d and sent her to Dr. S. in ophthalmology who reported normal retina. She lost weight, her polyuria improved, her bladder urgency got better, and her glucose values improved dramatically. She does no monitoring at home. She had to be hospitalized for an ankle fracture after falling on ice, for 3 months. At follow-up, Dr. K. seemed pleased with the results.

38. First Section from Electronic Medical Record (EMR) CHIEF COMPLAINT: Type II diabetes mellitus PERSONAL HISTORY SURGICAL: cholecystectomy: Age 60 years old MEDICAL: hypothyroidism: asymptomatic since 25 years LIFE STYLE MEDICATION DIABETA (Tab 2.5 MG) Sig: 1 tab(s) Oral before breakfast SYNTHROID (Tab 0.125 MG) Sig: 1 tab(s) Oral before breakfast HABITS: smoking: 0 alcohol: 0

39. First Section from Paper-Based Record (Post-EMR) Diabetes type I X age 4 Currently on N54 - N28 R6 - R2 Measure with OT II Glucose levels: <130 130-180 >180 AM IIIIIII IIIIIIIIIIIIIII Lunch Supper IIIIIIIIII Bedtime IIIIIIIII IIIIIIIIIIII Last HbA1C since April 96: 7.4/7.2/6.7/6.6/8.9 - higher values in log book Retinopathy: NIL March 97 Nephropathy: NIL Oct. 96

40. Sections of EMR Accessed by Intermediate and Expert Users for Two Typical Cases Category of Information (in order on CPR screen) Categories Accessed by Intermediate Categories Accessed by Expert 1. Chief Complaint 1 1 2. Past Medical History 2 3 3. Life Style 3 6 4. Psychosocial Profile 6 3 5. Family History 7 6 6. History of Present Illness 8 8 7. Review of Systems 9 3 8. Physical Examination 10 4 9. Diagnosis 11 8 10. Investigations 9 11. Treatment 11 9 2 11

41. Semantic Network (relationships between propositions) CONCEPTUAL REPRESENTATION situational model PROPOSITIONAL REPRESENTATION text-based model Propositional Analysis (a form of representation of a semantic network in memory) NATURALLANGUAGE expressed through Semantic Representation of Natural Language Analysis THOUGHTS AND IDEAS

42. Types of Propositions • Single propositions • A single idea • “She lost weight” • Embedded propositions • Contain one or more single propositions • “She lost weight relative to her premorbid baseline” • Linking propositions • Connect different portions of the text • “She lost weight and control of her blood sugar improved”

43. Propositional Analysis: Pre-EMR 74 year old woman who was diagnosed in February, as she complained of polyuria/nocturia and fatigue for a few years. She was told her sugar was very high...

44. Propositional Analysis: EMR Chief complaint: Referred by Dr. D. Type II diabetes mellitusPersonal history: Surgical: cholecystectomy; Age 60 years oldRemoval of kidney stone on left side

45. Propositional Analysis: Post-EMR Diabetes type I. Currently in N54-N28. Measure with OT IIGlucose levels: <130/130-180/>180

46. P r o pos it io n P r e - E M R E MR P os t - E MR T y pe ( %) ( %) ( %) S i n gl e 1 7 ( 3 7) 5 0 ( 8 6) 1 5 ( 7 9) E m b edd e d 1 8 ( 3 9) 5 ( 9 ) 2 ( 1 1 ) L i n k in g 1 1 ( 2 4) 3 ( 5 ) 2 ( 1 1 ) Proposition Type by Medical Record

47. Changes in Reasoning Patterns • Paper Records • Data-driven reasoning • Electronic Medical Record • Problem-directed reasoning • Return to Paper Record after EMR • Problem-directed reasoning • Residual effect of EMR on behavior (after EMR removed)

48. Multiple Hypotheses Patient Data Return to Paper Record Patient Data Hypotheses Same as EMR! Diagnostic Reasoning Paper Record Electronic Medical Record

49. Paper-Based Medical Records • Record before EMR: • Coherent discourse structure • Fewer inferences needed • Temporal order of causal relationships explicit • Data-driven reasoning • Increases cognitive load • Linking propositions constrain interpretation • Return to paper-based record after EMR • Hypothesis-directed reasoning • EMR structure maintained: No linking propositions • Less Information: more inferences needed

50. Electronic Medical Record • Supports hypothesis-directed reasoning • Provides flexible structure for data entry and review • Provides direction and reminders • Suffers from lack of linking propositions • Interpretation relies on the users • Despite time-stamping of events, lacks representation of temporal relationships in evolution of disease processes • Area for research and implementation