1. Computer-based Support for Improving Patient Medication Management James J. Cimino Chief, Laboratory for Informatics Development National Institutes of Health Clinical Center Senior Scientist, Lister Hill Center for Biomedical Communications National Library of Medicine Informatics Grand Rounds Dartmouth-Hitchcock Medical Center May 16, 2008

2. Challenges to Medication Management Lack of information about the patient –Patient’s condition –Patient’s co-morbidities –Medications the patient is supposed to take –Medications the patient is actually taking Access to medical knowledge –Knowing about availability of knowledge resources –Knowing how to use knowledge resources –Effort to use knowledge resources

3. Solutions Medication reconciliation –Collect information from disparate sources –Present information to support decision making Infobuttons –Anticipate user’s information needs –Automate access to appropriate resources –Automate retrieval from these resources

4. The Challenge of Medication Reconciliation Stop Go Stop Go Stop Go ?

5. Many a Slip ‘Twixt the Cup and the Lip Patient is Supposed to Take Patient is not Supposed to Take Patient is Taking Reports Taking Doesn’t Report Taking Reports Taking Doesn’t Report Taking Patient is not Taking Reports Taking Doesn’t Report Taking Reports Taking Doesn’t Report Taking Stop

6. Problems and Solutions Errors due to: –Not starting medications the patient should be taking –Starting medications the patient shouldn’t be taking –Not communication starts/stops to next caregiver –Not communicating changes to patients Beers, et al. J Am Geriatric Society 1990: –83% of hospital admission histories missed one or more medications –46% missed three or more Problems occur at all transitions in care: –“Continue all outpatient medications”

7. Electronic Health Records to the Rescue! Stop Go Stop Go Stop Go ?

8. Computer Assisted Medication Reconciliation Poon et al.: JAMIA 2006: –Preadmission Medication List –Grouped medications by generic names Text sources Multiple sources Substitutions might occur Confusing chronology Information overload!

9. Our Approach to Medication Reconciliation Multiple inpatient and outpatient systems Natural language processing to get codes Medical knowledge base to group codes Chronological presentation

10. Methods All recent admissions for one physician (JJC) Multiple inpatient and outpatient resources Carol Friedman’s Medical Language Extraction and Encoding (MedLEE) US National Library of Medicine’s Unified Medical Language System (UMLS) Columbia’s Medical Entities Dictionary (MED) American Hospital Formulary Service (AHFS) classification Evaluation of ability to capture, code and organize

11. 1. Prior Clinic Note 2. Prior Outpatient Medications 3. Admission Note 4. Admission Note Plan 5. Admission Orders 6. Admission Pharmacy Orders 7. Active Orders at Discharge 8. Discharge Pharmacy Orders 9. Discharge Instructions 10. Discharge Plan 11. Clinic Note after Discharge 12. Outpatient Medications after Discharge Data Sources Data SourceSystemData Type Narrative Coded Narrative Coded Narrative Coded WebCIS Eclipsys WebCIS Eclipsys WebCIS Eclipsys WebCIS

12. Results 70 patient records reviewed 30 hospitalizations identified 17 met inclusion criteria MedLEE found 623/653 (95.4%) medications Total of 1533 medications (444 unique) in MED

13. Medications by Source Data SourceMeds Records with Data Meds per Patient Prior Clinic Note *157179.2 Prior Outpatient Medications2111316.2 Admission Note *102147.3 Admission Note Plan *41123.4 Admission Orders88811.0 Admission Pharmacy Orders1521410.9 Active Orders at Discharge93811.6 Discharge Pharmacy Orders1711412.2 Discharge Instructions *6078.6 Discharge Plan *123167.7 Clinic Note After Discharge *140168.8 Outpatient Medications after Discharge2251317.3 * Narrative text

14. MedLEE Terms Found Mapped to UMLS MED Terms Mapped to AHFS

15. Patient #9 201204: Anticoag- ulants 240400: Cardiac Drugs 240800: Hypoten- sive Agents 280000: CNS Agents 281604: Antidep- ressants Prior Clinic Notecoumadinverapamilcozaarcymbalta Prior Outpatient Medications Coumadin 5 mg Tab Verapamil 180 mg Extended Release Tablet Losartan Potassium 100 mg Tablet Pregabalin 50mg Capsule Admission Notecoumadinverapamilcozaarcymbalta Admission Note Plan coumadin Admission Orders Warfarin Sodium Oral 10 MG Verapamil SR Oral 240 MG Losartan Oral 50 MG Admission Pharmacy Orders WARFARIN TAB 5 MG 10 MILLIGRA M VERAPAMIL SR TAB 240 MG LOSARTAN POTAS- SIUM TAB 50 MG Transition from Outpatient to Inpatient

16. Patient #9 201204: Anticoag- ulants 240400: Cardiac Drugs 240800: Hypoten- sive Agents 280000: CNS Agents 281604: Antidep- ressants Admission Pharmacy Orders WARFARIN TAB 5 MG 10 MILLIGRAM VERAPAMIL SR TAB 240 MG LOSARTAN POTASSIUM TAB 50 MG Active Orders at Discharge Verapamil SR Oral 240 MG Losartan Oral 50 MG Discharge Pharmacy Orders VERAPAMIL SR TAB 240 MG LOSARTAN POTASSIUM TAB 50 MG DULOXET- INE CAP 20 MG Discharge Instructions cymbalta Discharge Plancymbalta Clinic Note After Discharge coumadinverapamilcymbalta Outpatient Medications after Discharge Coumadin 5 mg Tab Verapamil 180 mg Exte- nded Release Tab Losartan Potassium 100 mg Tablet Pregabalin 50mg Capsule Transition from Outpatient to Inpatient

17. Discussion Data from multiple coded and narrative sources can be coded automatically and merged into a single form The UMLS and MED are both needed for coding to a single terminology (AHFS) Further work on MedLEE and the MED are needed Drugs tend to group into one per class; allows for change from one generic to another Chronology by drug class can highlight changes in medication plans Changes can be intended or unintended, but should not be ignored The next step is medication reconciliation

26. http://www.dbmi.columbia.edu/cimino/medrec/

27. Next Step: High-Quality Decision Making Providing patient information evokes additional information needs These needs are stereotypical Resources exist to address these needs If we can predict the needs, we can provide links Information available in the context can be used to target the resources

28. Health Knowledge for Decision Support

29. ?

30. Infobuttons Anticipate Need and Provide Queries i

31. Information Needs of CIS Users Common tasks may have common needs System knows: –Who the user is –Who the patient is –What the user is doing –What information the user is looking at We can predict the specific need User is sitting at a computer! We can automate information retrieval

32. First Attempt: The Medline Button CIS on mainframe BRS/Colleague (Medline) on same mainframe Get them to talk to each other Search using diagnoses and procedures

37. First Attempt: The Medline Button CIS on mainframe BRS/Colleague (Medline) on same mainframe Get them to talk to each other Search using diagnoses and procedures Technical success Practical failure

38. Education at the Moment of Need i

39. Understand Information Needs 1 i

40. Education at the Moment of Need Get Information From EMR Understand Information Needs 1 2 i

41. Education at the Moment of Need Get Information From EMR Resource Selection Understand Information Needs 1 2 3 i

42. Education at the Moment of Need Get Information From EMR Resource Selection Resource Terminology Understand Information Needs 1 24 3 i

43. Education at the Moment of Need Get Information From EMR Resource Selection Resource Terminology Understand Information Needs Automated Translation 1 254 3 i

44. Education at the Moment of Need Get Information From EMR Resource Selection Resource Terminology Querying Understand Information Needs Automated Translation 1 254 6 3 i

45. Education at the Moment of Need Get Information From EMR Resource Selection Resource Terminology Querying Presentation Understand Information Needs Automated Translation 1 254 6 3 7 i

51. Infobuttons vs. Infobutton Manager Page of Hyperlinks Infobutton Clinical System Resource Infobutton Manager Context Query Knowledge Base s

59. Usage in Lab Contexts

60. Usage in In-Patient Drug Contexts

61. Usage in Diagnosis Context

62. Usage in Lab Order Entry Context

63. Usage in InPat Drug Order Entry

64. The Coumadin Story Chair of Medicine wants link to Coumadin protocol First, I have to find the guidelines

66. The Coumadin Story Chair of Medicine wants link to Coumadin protocol First, I have to find the guidelines Then I have to add the question to the IM table

68. The Coumadin Story Chair of Medicine wants link to Coumadin protocol First, I have to find the guidelines Then I have to add the question to the IM table Finally, I link the question to the context

70. The Coumadin Story Chair of Medicine wants link to Coumadin protocol First, I have to find the guidelines Then I have to add the question to the IM table Finally, I link the question to the context Voilá!

74. New York Presbyterian Hospital (Eclipsys)

75. NY Office of Mental Health (Psykes)

77. Regenstrief Medical Record System

78. Cryststal Run Healthcare (NextGen)

80. AMIA 2007 Demo Participants Health care & academic institutions –Intermountain Healthcare, Columbia University, Partners Healthcare Content providers –Wolters Kluwer Health, ACP, Micromedex, UpToDate, Ebsco, Lexicomp

87. Institution-Specific Requirements What are the users’ information needs? In what contexts do those needs arise? What resources will resolve the needs? How do we deal with terminology? How can the Infobutton Manager be integrated into the clinical information system? The institution’s librarian is the best person to resolve most of these issues

88. Infobutton Manager Maintenance Tool Functions: Browse Add Update Delete Clinician Infobutton Manager Translation Table Term Translation Context Table Context Matching Infobutton Table Query Construction Page of Links System Maintainer Institution Customization Tasks

89. Librarian Infobutton Tailoring Environrment (LITE) Specify user contexts Identify terminology in each context Information needs in each context Resources for resolving information needs Automating translation and querying

90. Infobutton Manager Maintenance Tool Functions: Browse Add Update Delete Clinician Infobutton Manager Translation Table Term Translation Context Table Context Matching Infobutton Table Query Construction Page of Links System Maintainer Institution Customization Tasks

91. LITE Tasks Librarian Infobutton Tailoring Environment (LITE) Infobutton Manager Log File LITE Log File LITE Auditing LITE Monitoring Infobutton Manager Monitoring Institution Librarian Context Definition Resource Utilization Terminology Specification Question Construction Resource Selection Clinician Infobutton Manager Translation Table Term Translation Context Table Context Matching Infobutton Table Query Construction Page of Links

92. LITE Research Plan Conduct community assessment Refine LITE features Establish forum for feedback from librarians Develop LITE in an iterative manner Develop a user manual and tutorial Evaluate usability of LITE by librarians Evaluate the use of LITE Disseminate the results of the project Promote the use of the IM and LITE

93. Status Report Drupal site Community of users Clear through Institutional Review Board Enroll “subjects” Make each draft a forum topic Collect feedback Iterate

94. www.infobuttons.org

95. lite.dbmi.columbia.edu

96. Conclusions Diverse medication data can be automatically integrated Organizing data by time and drug class can highlight possible errors Infobuttons can anticipate and resolve clinicians’ information needs Institution-specific tailoring is required International standard will stimulate wider adoption Librarian Infobutton Tailoring Environment will put the Infobutton Manager on autopilot

97. Acknowledgments Medication Reconcilliation –Carol Friedman for use of MedLEE –Jianhua Li for programming –Tiffani Bright for background research –US National Library of Medicine Infobuttons –Jianhua Li for programming –Many student contributors –Guilherme Del Fiol –Noemie Elhadad –National Library of Medicine