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

Stop

Stop

Stop

Stop

Go

StopGo

Stop

Go

?

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

Stop

Stop

Stop

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!

Stop

Stop

Stop

Stop

Go

StopGo

Stop

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

1. Prior Clinic Note2. Prior Outpatient Medications3. Admission Note4. Admission Note Plan5. Admission Orders6. Admission Pharmacy Orders7. Active Orders at Discharge8. Discharge Pharmacy Orders9. Discharge Instructions10. Discharge Plan11. Clinic Note after Discharge12. Outpatient Medications after Discharge

Data SourcesData Source System Data Type

NarrativeCoded

NarrativeNarrative

CodedCodedCodedCoded

NarrativeNarrativeNarrative

Coded

WebCISWebCISWebCISWebCISEclipsysWebCISEclipsysWebCISEclipsysWebCISWebCISWebCIS

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

Data Source Meds Recordswith Data

Meds perPatient

Prior Clinic Note * 157 17 9.2Prior Outpatient Medications 211 13 16.2Admission Note * 102 14 7.3Admission Note Plan * 41 12 3.4Admission Orders 88 8 11.0

Admission Pharmacy Orders 152 14 10.9

Active Orders at Discharge 93 8 11.6

Discharge Pharmacy Orders 171 14 12.2

Discharge Instructions * 60 7 8.6Discharge Plan * 123 16 7.7

Clinic Note After Discharge * 140 16 8.8

Outpatient Medications after Discharge 225 13 17.3

* Narrative text

169 UMLS (93%)

8 Other Meds (4%):

INH, MVI, asa, Os-Cal,

darvocet, hctz, niacin, toprol

4 Non-Med (3%): cream, antiinflam-

matory, lotion, lozenge, po

MedLEE Terms Found30 Non-Med,

(5%)48 Other Meds (8%)

545 UMLS (87%)

Mapped to UMLS

MED Terms

442 AHFS (99.5%)

2 non-AHFS (0.5%): oxygen,

medication

16 non-AHFS (1.0%)

1517 AHFS (99.0%)

Mapped to AHFS

Patient #9201204:

Anticoag-ulants

240400: Cardiac Drugs

240800: Hypoten-

sive Agents

280000: CNS

Agents

281604: Antidep-ressants

Prior Clinic Note coumadin verapamil cozaar cymbalta

Prior Outpatient Medications

Coumadin 5 mg Tab

Verapamil180 mg

Extended Release Tablet

LosartanPotassium

100 mgTablet

Pregabalin 50mg Capsule

Admission Note coumadin verapamil cozaar cymbalta

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

Patient #9201204:

Anticoag-ulants

240400: Cardiac Drugs

240800: Hypoten-

sive Agents

280000: CNS

Agents

281604: Antidep-ressants

Admission Pharmacy Orders

WARFARINTAB 5 MG 10MILLIGRAM

VERAPAMILSR TAB 240

MG

LOSARTAN POTASSIUMTAB 50 MG

Active Orders at Discharge

Verapamil SR Oral 240 MG

Losartan Oral 50 MG

DischargePharmacy Orders

VERAPAMIL SR TAB 240 MG

LOSARTANPOTASSIUMTAB 50 MG

DULOXET-INE CAP 20 MG

Discharge Instructions

cymbalta

Discharge Plan cymbalta

Clinic Note After Discharge

coumadin verapamil cymbalta

OutpatientMedications after

Discharge

Coumadin 5mg Tab

Verapamil180 mg Exte-

ndedRelease Tab

LosartanPotassium 100

mg Tablet

Pregabalin50mg

Capsule

Transition from Outpatient to Inpatient

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