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Computer-based Support for Improving Patient Medication Management

Computer-based Support for Improving Patient Medication Management

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Computer-based Support for Improving Patient Medication Management

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  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 Go Stop Stop Go Stop Stop Stop Go Stop ?

  5. Many a Slip ‘Twixt the Cup and the Lip Stop Stop Stop 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! Go Stop Stop Go Stop Stop Stop Go Stop ?

  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. Data Sources

  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 * Narrative text

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

  15. Transition from Outpatient to Inpatient

  16. 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

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

  19. 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

  20. Health Knowledge for Decision Support

  21. Health Knowledge for Decision Support ?

  22. Infobuttons Anticipate Need and Provide Queries i

  23. 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

  24. 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

  25. 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

  26. Education at the Moment of Need i

  27. Education at the Moment of Need i 1 Understand Information Needs

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

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

  30. Education at the Moment of Need 4 2 Get Information From EMR Resource Terminology i 1 Understand Information Needs 3 Resource Selection

  31. Education at the Moment of Need 4 5 2 Automated Translation Get Information From EMR Resource Terminology i 1 Understand Information Needs 3 Resource Selection

  32. Education at the Moment of Need 4 5 2 Automated Translation Get Information From EMR Resource Terminology 6 i 1 Querying Understand Information Needs 3 Resource Selection

  33. Education at the Moment of Need 4 5 2 Automated Translation Get Information From EMR Resource Terminology 6 i 1 Querying Understand Information Needs 3 7 Resource Selection Presentation