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Medication Reconciliation Using Natural Language Processing and Controlled Terminologies

Explore the challenge of medication reconciliation and how NLP and controlled terminologies can help solve medication errors and communication gaps. Learn about our approach to medication reconciliation through NLP, coding, and organizing data by time and drug class.

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Medication Reconciliation Using Natural Language Processing and Controlled Terminologies

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  1. Medication Reconciliation Using Natural Language Processing and Controlled Terminologies James J. Cimino, Tiffani J. Bright, Jianhua Li Department of Biomedical Informatics Columbia University College of Physicians and Surgeons New York, New York, USA

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

  3. Many a Slip ‘Twixt the Cup and the Lip Stop Stop Stop Stop

  4. 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”

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

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

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

  8. 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

  9. Data Sources

  10. 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

  11. Medications by Source * Narrative text

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

  13. Transition from Outpatient to Inpatient

  14. Transition from Outpatient to Inpatient

  15. 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

  16. Conclusions Acknowledgements • Carol Friedman for use of MedLEE • US National Library of Medicine: Research Grant 5R01LM007593-05 Training Grant LM07079-1 • Diverse medication data can be automatically integrated • Organizing data by time and drug class can highlight possible errors

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