Supported by The Children’s Hospital Research Institute and the NIH/NCRR Colorado CTSI Grant Number UL1 RR025780. Its contents are the authors’ sole responsibility and do not necessarily represent official NIH views

Merging Clinical Care & Clinical Research in the EMR: Implementation Issues

Narrowing the Research-Practice Divide in Evidence-Based Medicine with Adoption of Electronic Health Record Systems: Present and Future Directions

Hosted by: National Institute on Drug Abuse13-14 July 2009

Michael G. Kahn MD, PhDBiomedical Informatics Core Director

Colorado Clinical and Translational Sciences InstituteAssociate Professor, Department of Pediatrics

University of Colorado

Director, Clinical InformaticsThe Children’s Hospital, DenverMichael.Kahn@ucdenver.edu

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Presentation Outline

• Promises• Challenges• Warnings• Solutions

Kahn MG, Kaplan D, Sokol RJ, DiLaura RP. Configuration Challenges: Implementing Translational Research Policies in Electronic Medical Records. Academic Medicine, 2007; 82(7) 661-9.

A presentation based on article @ http://www2.amia.org/meetings/s07/docs/pdf/s28panel_kahn_tri.pdf

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EMR versus EHR

• From NAHIT (National Alliance for Health Information Technology)

– EMR: The electronic record of health-related information on an individual that is created, gathered, managed, and consulted by licensed clinicians and staff from a single organization who are involved in the individual’s health and care.

– EHR: The aggregate electronic record of health-related information on an individual that is created and gathered cumulatively across more than one health care organization and is managed and consulted by licensed clinicians and staff involved in the individual’s health and care.

This talk focuses exclusively on E**M**R and clinical research (despite the title of this symposium!)

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The Promise of the Electronic Medical Record

• Merging prospective clinical research & evidence-based clinical care– A “front-end” focus

• Improving care one patient at a time (decision support)• Merging clinical care and clinical research data collection

• Clinically rich database for retrospective clinical research– A “back-end” focus

• Making discoveries across populations of patients• Improving care at the population / policy level

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Submission& ReportingEvidence-based

Review

NewResearchQuestions

StudySetupStudy Design

& Approval

Recruitment& Enrollment

StudyExecution

ClinicalPractice

PublicInformation

T1 Biomedical Research Investigator Initiated T1 T2 Translational ResearchIndustry Sponsored Commercialization

ClinicalTrial Data

BasicResearch Data

PilotStudies

RequiredData Sharing

OutcomesReporting

OutcomesResearch

Evidence-based Patient

Care and Policy

EMRData

A Lifecycle View of Clinical Research

From: C Broverman, Partners

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How EMR’s could accelerate clinical research (Front-end)

Trial Step EMR potential roleStudy set-up

Query EMR database to establish number of potential study candidates. Incorporate study manual or special instructions into EMR “clinical content” for

study encounters

Study enrollment

Implement study screening parameters into patient registration and scheduling. Query EMR database to contact/recruit potential candidates and notify the

patient’s provider(s) of potential study eligibility.

Study execution

Incorporate study-specific data capture as part of routine clinical care / clinical documentation workflows

Auto-populate study data elements into care report forms from other parts of the EMR database.

Embed study-specific data requirements (case record forms) as special tabs/documentation templates using structured data entry.

Implement rules/alerts to ensure compliance with study data collection requirements

Create range checks and structured documentation checks to ensure valid data entry

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How EMR’s could accelerate clinical research (Back-end)

Trial Step EMR potential roleSubmission & Reporting

Provide data extraction formats that support data exchange standards

Document and report adverse events

Evidence-based review

Assess congruence of new findings and existing evidence with current practice and outcomes (incorporate into meta-analyses)

Submit findings to electronic trial banks using published standards.

Evidence-based clinical care

Implement study findings as clinical documentation, orders sets, point-of-care rules/alerts

Monitor changes in care and outcomes in response to evidence-based clinical decision support

Provide easy access to detailed clinical care data for motivating new clinical trial hypotheses

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Submission& ReportingEvidence-based

Review

NewResearchQuestions

StudySetupStudy Design

& Approval

Recruitment& Enrollment

StudyExecution

ClinicalPractice

PublicInformation

T1 Biomedical Research Investigator Initiated T1 T2 Translational ResearchIndustry Sponsored Commercialization

ClinicalTrial Data

BasicResearch Data

PilotStudies

RequiredData Sharing

OutcomesReporting

OutcomesResearch

Evidence-based Patient

Care and Policy

EMRData

The EMR & Clinical Research: “Front-End” Issues

From: C Broverman, Partners

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Degrees of Constraints #1: The Regulatory Environment

Regulation Regulatory focus

HIPAA Privacy & Confidentiality of health records

45 CFR Part 2 Confidentiality of alcohol and substance abuse records

21 CFR Part 5021 CFR Part 56

FDA Protection of Human Subjects

21 CFR Part 11 FDA electronic records & e-signature rules

45 CFR Part 46 OHRP human subjects protection

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Degrees of Constraints #2: Involved parties & roles

Principal investigator With an established clinical relationship

With no established clinical relationship

Study subjects

Local Institutional Review Boards / Data safety monitoring boards

Research subject advocates

Funding sponsor

Non-study clinicians Standard care setting

Emergency care setting

EMR users

System managers EMR

Clinical trials

Data stewards

Institutional managers

Billing & compliance

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Degrees of Constraints #3: Clinical contexts

• Inpatient versus outpatient• Full grant versus partial grant• Orders versus results

• Radiology results versus laboratory results versus other clinical results

• Clinical documentation

• Need to ensure consistency with current practices, consents and assurances

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Degrees of Constraints #4: Who can see what?

Research …. Internal Access

External Access

Orders

Medications

Lab results

Radiology results

Notes

Vitals, allergies, care plan, weight, flow sheets, nursing notes, discharge plans

Nursing Kardex

Research forms or questionnaires

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Degrees of Constraints #5: Contractual obligations

• Pharmaceutical trials: Contractual requirements for confidentiality– Varies by contract terms

• NIH Certificates of Confidentiality– Certificates of Confidentiality are issued by the National Institutes of Health (NIH)

to protect the privacy of research subjects by protecting investigators and institutions from being compelled to release information that could be used to identify subjects with a research project. Certificates of Confidentiality are issued to institutions or universities where the research is conducted. They allow the investigator and others who have access to research records to refuse to disclose identifying information in any civil, criminal, administrative, legislative, or other proceeding, whether at the federal, state, or local level.

– (From http://grants2.nih.gov/grants/policy/coc/background.htm)

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Degrees of Constraints #6 (a & b): Integrating clinical research decisions into clinical care workflows

6a RegistrationDocumentationResults reviewBillingRelease of InformationData extraction into CTMS

6b Solutions must fit EMR functional capabilitiesSame vendor’s functional capabilities may differ between settings (inpatient versus outpatient)

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Working down the scenarios….

•Six workbooks•Sixteen research data domains•Data entry versus data visibility•Current versus Desired & Proposed Solution

576 cells to fill inWith 14 user roles: 8064 cells!

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Our previous solution: Based on three desiderata*

1. Patient safety trumps investigator’s needs– Number one priority for COMIRB, research advocates, risk

management

2. Confidentiality amongst TCH caregivers ≠ confidentiality/disclosures beyond TCH

3. When conflicts arise, return back to paper– Work with vendor to develop EMR-based solution

* Latin for “something desired as essential”

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Our previous solution: 3.5 answers required staying with paperResearch …. Internal External

Orders No, Research on paper Non-research in EMR

No

Medications Yes: eMAR shows all meds YesLab results Yes (via LIS, not in EMR)

Non-research in EMRNo

Radiology results Yes YesNotes Yes

If special confidentiality required, use paper notes

No

Vitals, allergies, care plan, weight, flow sheets, nursing notes, discharge plans

Yes Yes

Nursing Kardex No, Research tasks on paper Non-research tasks in EMR

No

Research forms or questionnaires No, paper only No

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Our current solution…..

Research …. Internal Access External Access

Orders ? ?

Medications ? ?

Lab results ? ?

Radiology results Yes ?

Notes ? ?

Vitals, allergies, care plan, weight, flow sheets, nursing notes, discharge plans

? ?

Nursing Kardex ? ?

Research forms or questionnaires ? ?

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Submission& ReportingEvidence-based

Review

NewResearchQuestions

StudySetupStudy Design

& Approval

Recruitment& Enrollment

StudyExecution

ClinicalPractice

PublicInformation

T1 Biomedical Research Investigator Initiated T1 T2 Translational ResearchIndustry Sponsored Commercialization

ClinicalTrial Data

BasicResearch Data

PilotStudies

RequiredData Sharing

OutcomesReporting

OutcomesResearch

Evidence-based Patient

Care and Policy

EMRData

The EMR & Clinical Research: “Back-End” Issues

From: C Broverman, Partners

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Data quality – The EMR’s dirty laundry

• Suppose the previous issues were solved and investigators can easily use the EMR as a rich source of data for clinical research……

…..what is the quality of the results that come back?

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Martial Status by Age: Would this result be worrisome?

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It’s tough being 6 years old…….

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Should we be worried?

• No– Large numbers will swamp out effect of anomalous

data or use trimmed data– Simulation techniques are insensitive to small errors

• Yes– Public reporting could highlight data anomalies– Genomic associations look for small signals (small

differences in risks) amongst populations

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GIGO: Garbage in Gospel Out

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Where are we going from here?

• Defining clear rules of what is required versus desired– Balancing patient safety versus research needs– May need to decide which rules to break– Who “owns” the final decisions on compromises?

• Working to eliminate artificial implementation barriers

• Designing workflows so that every patient is a research subject

• Using EMR data for clinical research with a high degree of skepticism. Seek multiple paths for confirming findings

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Thank you!

Michael.Kahn@ucdenver.edu