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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 The Challenge of Medication Reconciliation Go Stop Stop Stop Stop Stop Stop ? Go Go Many a Slip ‘Twixt the Cup and the Lip Stop Stop Stop Stop Patient is Supposed to Take Patient is Taking Patient is not Taking Patient is not Supposed to Take Reports Taking Doesn’t Report Taking Reports Taking Doesn’t Report Taking Reports Taking Doesn’t Report Taking Reports Taking Doesn’t Report Taking 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” Electronic Health Records to the Rescue! Go Stop Stop Stop Stop Stop Stop ? Go Go 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! Our Approach to Medication Reconciliation • Multiple inpatient and outpatient systems • Natural language processing to get codes • Medical knowledge base to group codes • Chronological presentation 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 Data Sources Data Source 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 System Data Type WebCIS Narrative Coded WebCIS WebCIS Narrative WebCIS Narrative Coded Eclipsys Coded WebCIS Coded Eclipsys Coded WebCIS Eclipsys Narrative WebCIS Narrative WebCIS Narrative Coded WebCIS 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 Medications by Source Prior Clinic Note * Prior Outpatient Medications Admission Note * Admission Note Plan * Admission Orders Admission Pharmacy Orders 157 211 102 41 88 152 Records with Data 17 13 14 12 8 14 Active Orders at Discharge 93 8 11.6 Discharge Pharmacy Orders Discharge Instructions * Discharge Plan * Clinic Note After Discharge * 171 60 123 140 14 7 16 16 12.2 8.6 7.7 8.8 Outpatient Medications after Discharge 225 13 17.3 Data Source * Narrative text Meds Meds per Patient 9.2 16.2 7.3 3.4 11.0 10.9 MedLEE Terms Found 48 Other Meds (8%) 30 Non-Med, (5%) 545 UMLS (87%) MED Terms 16 non-AHFS (1.0%) 1517 AHFS (99.0%) Mapped to UMLS 8 Other Meds (4%): INH, MVI, asa, Os-Cal, darvocet, hctz, niacin, toprol 4 Non-Med (3%): cream, antiinflammatory, lotion, lozenge, po 169 UMLS (93%) Mapped to AHFS 2 non-AHFS (0.5%): oxygen, medication 442 AHFS (99.5%) Transition from Outpatient to Inpatient Patient #9 201204: Anticoagulants 240400: Cardiac Drugs 240800: Hypotensive Agents Prior Clinic Note coumadin verapamil cozaar Prior Outpatient Medications Coumadin 5 mg Tab Verapamil 180 mg Extended Release Tablet Losartan Potassium 100 mg Tablet Admission Note coumadin verapamil cozaar Admission Note Plan coumadin Verapamil SR Oral 240 MG Losartan Oral 50 MG VERAPAMIL SR TAB 240 MG LOSARTAN POTASSIUM TAB 50 MG Admission Orders Warfarin Sodium Oral 10 MG Admission Pharmacy Orders WARFARIN TAB 5 MG 10 MILLIGRA M 280000: CNS Agents 281604: Antidepressants cymbalta Pregabalin 50mg Capsule cymbalta Transition from Outpatient to Inpatient Patient #9 201204: Anticoagulants 240400: Cardiac Drugs 240800: Hypotensive Agents 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 280000: CNS Agents 281604: Antidepressants DULOXETINE CAP 20 MG Discharge Instructions cymbalta Discharge Plan cymbalta Clinic Note After Discharge Outpatient Medications after Discharge coumadin verapamil Coumadin 5 mg Tab Verapamil 180 mg Extended Release Tab cymbalta Losartan Potassium 100 mg Tablet Pregabalin 50mg Capsule 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 Conclusions • Diverse medication data can be automatically integrated • Organizing data by time and drug class can highlight possible errors Acknowledgements • Carol Friedman for use of MedLEE • US National Library of Medicine: Research Grant 5R01LM007593-05 Training Grant LM07079-1
