Medication Reconcilliation Using Natural Language Processing ...

Medication Reconcilliation Using Natural Language Processing ...

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 Doesnt Report Taking Reports Taking Doesnt Report Taking Reports Taking Doesnt Report Taking Reports Taking Doesnt Report Taking Problems and Solutions

Errors due to: Not starting medications the patient should be taking Starting medications the patient shouldnt 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 Friedmans Medical Language Extraction and Encoding (MedLEE) US National Library of Medicines Unified Medical Language System (UMLS) Columbias 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 WebCIS WebCIS WebCIS Eclipsys WebCIS Eclipsys WebCIS Eclipsys WebCIS WebCIS WebCIS Narrative Coded Narrative Narrative

Coded Coded Coded Coded Narrative Narrative Narrative Coded 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

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