Computer-based Support for Improving Patient Medication Management James J. Cimino Chief, Laboratory for Informatics Development National Institutes of

Computer-based Support for Improving Patient Medication Management

James J. CiminoChief, Laboratory for Informatics Development

National Institutes of Health Clinical Center

Senior Scientist, Lister Hill Center for Biomedical CommunicationsNational Library of Medicine

Informatics Grand RoundsDartmouth-Hitchcock Medical Center

May 16, 2008

Challenges to Medication Management

• Lack of information about the patient– Patient’s condition– Patient’s co-morbidities– Medications the patient is supposed to take– Medications the patient is actually taking

• Access to medical knowledge– Knowing about availability of knowledge resources– Knowing how to use knowledge resources– Effort to use knowledge resources

Solutions

• Medication reconciliation– Collect information from disparate sources– Present information to support decision making

• Infobuttons– Anticipate user’s information needs– Automate access to appropriate resources– Automate retrieval from these resources

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

• Multiple 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

http://www.dbmi.columbia.edu/cimino/medrec/

Next Step: High-Quality Decision Making

• Providing patient information evokes additional information needs

• These needs are stereotypical

• Resources exist to address these needs

• If we can predict the needs, we can provide links

• Information available in the context can be used to target the resources

Health Knowledge for Decision Support

Health Knowledge for Decision Support

?

Infobuttons

Anticipate Need and Provide Queries

i

Information Needs of CIS Users

• Common tasks may have common needs

• System knows:– Who the user is– Who the patient is– What the user is doing– What information the user is looking at

• We can predict the specific need

• User is sitting at a computer!

• We can automate information retrieval

First Attempt: The Medline Button

• CIS on mainframe

• BRS/Colleague (Medline) on same mainframe

• Get them to talk to each other

• Search using diagnoses and procedures

First Attempt: The Medline Button

• CIS on mainframe

• BRS/Colleague (Medline) on same mainframe

• Get them to talk to each other

• Search using diagnoses and procedures• Technical success

• Practical failure

Education at the Moment of Need

i


UnderstandInformation

Needs

1i


Get InformationFrom EMR


Needs

1

2

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ResourceSelection


Needs

1

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ResourceSelection

ResourceTerminology


Needs

1

24

3

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ResourceSelection

ResourceTerminology


Needs

AutomatedTranslation

1

254

3

i



ResourceSelection

ResourceTerminology

QueryingUnderstandInformation

Needs


1

254

6

3

i



ResourceSelection

ResourceTerminology

Querying

Presentation


Needs


1

254

6

3

7

i

Infobuttons vs. Infobutton Manager

Pageof

Hyperlinks

InfobuttonClinical System Resourc

e

InfobuttonManager

ContextQuery

KnowledgeBase

s

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HR-LabDetail

IM-LabDetail

Usage in In-Patient Drug Contexts

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HR-InPatientDrugs

IM-InPatientDrugs

Usage in Diagnosis Context

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IM-Diagnoses

Usage in Lab Order Entry Context

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IM-LabOrder

Usage in InPat Drug Order Entry

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HR-DrugOrder

IM-DrugOrder

The Coumadin Story

• Chair of Medicine wants link to Coumadin protocol

• First, I have to find the guidelines

The Coumadin Story

• Chair of Medicine wants link to Coumadin protocol

• First, I have to find the guidelines

• Then I have to add the question to the IM table

The Coumadin Story

• Chair of Medicine wants link to Coumadin protocol• First, I have to find the guidelines• Then I have to add the question to the IM table

• Finally, I link the question to the context

The Coumadin Story

• Chair of Medicine wants link to Coumadin protocol• First, I have to find the guidelines• Then I have to add the question to the IM table• Finally, I link the question to the context

• Voilá!

New York Presbyterian Hospital (Eclipsys)

NY Office of Mental Health (Psykes)

NY Office of Mental Health (Psykes)

Regenstrief Medical Record System

Cryststal Run Healthcare (NextGen)

AMIA 2007 Demo Participants

• Health care & academic institutions– Intermountain Healthcare, Columbia University,

Partners Healthcare

• Content providers– Wolters Kluwer Health, ACP, Micromedex,

UpToDate, Ebsco, Lexicomp

Institution-Specific Requirements

• What are the users’ information needs?

• In what contexts do those needs arise?

• What resources will resolve the needs?

• How do we deal with terminology?

• How can the Infobutton Manager be integrated into the clinical information system?

• The institution’s librarian is the best person to resolve most of these issues

Infobutton ManagerMaintenance Tool

Functions:Browse

AddUpdateDelete

Clinician

Infobutton Manager

TranslationTable

TermTranslation

ContextTable

ContextMatching

InfobuttonTable

QueryConstruction

Page ofLinks

SystemMaintainer

Institution Customization Tasks

Librarian Infobutton Tailoring Environrment (LITE)

• Specify user contexts

• Identify terminology in each context

• Information needs in each context

• Resources for resolving information needs

• Automating translation and querying

Infobutton ManagerMaintenance Tool

Functions:Browse

AddUpdateDelete

Clinician

Infobutton Manager

TranslationTable

TermTranslation

ContextTable

ContextMatching

InfobuttonTable

QueryConstruction

Page ofLinks

SystemMaintainer

Institution Customization Tasks

LITE TasksLibrarian Infobutton

Tailoring Environment(LITE)

InfobuttonManagerLog File

LITELog File

LITE Auditing

LITE Monitoring

Infobutton ManagerMonitoring

InstitutionLibrarian

ContextDefinition

ResourceUtilization

TerminologySpecification

QuestionConstruction

ResourceSelection

Clinician

Infobutton Manager

TranslationTable

TermTranslation

ContextTable

ContextMatching

InfobuttonTable

QueryConstruction

Page ofLinks

LITE Research Plan

• Conduct community assessment

• Refine LITE features

• Establish forum for feedback from librarians

• Develop LITE in an iterative manner

• Develop a user manual and tutorial

• Evaluate usability of LITE by librarians

• Evaluate the use of LITE

• Disseminate the results of the project

• Promote the use of the IM and LITE

Status Report

• Drupal site

• Community of users

• Clear through Institutional Review Board

• Enroll “subjects”

• Make each draft a forum topic

• Collect feedback

• Iterate

www.infobuttons.org

lite.dbmi.columbia.edu

Conclusions• Diverse medication data can be automatically

integrated

• Organizing data by time and drug class can highlight possible errors

• Infobuttons can anticipate and resolve clinicians’ information needs

• Institution-specific tailoring is required

• International standard will stimulate wider adoption

• Librarian Infobutton Tailoring Environment will put the Infobutton Manager on autopilot

Acknowledgments

• Medication Reconcilliation– Carol Friedman for use of MedLEE– Jianhua Li for programming– Tiffani Bright for background research– US National Library of Medicine

• Infobuttons– Jianhua Li for programming– Many student contributors– Guilherme Del Fiol– Noemie Elhadad– National Library of Medicine

Documents

Computer-based Support for Improving Patient Medication Management James J. Cimino Chief, Laboratory for Informatics Development National Institutes of