Michael G. Kahn MD, PhD Biomedical Informatics Core Director
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Transcript of Michael G. Kahn MD, PhD Biomedical Informatics Core Director
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, [email protected]
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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