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Medication Reconciliation Using Natural Language Processing and Controlled Terminologies James J. Cimino, Tiffani J. Bright, Jianhua Li Department of Biomedical.

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Presentation on theme: "Medication Reconciliation Using Natural Language Processing and Controlled Terminologies James J. Cimino, Tiffani J. Bright, Jianhua Li Department of Biomedical."— Presentation transcript:

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 Stop Go Stop Go Stop Go ?

3 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

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! Stop Go Stop Go Stop Go ?

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 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

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 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

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

13 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

14 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

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

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25 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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