Transcript Merging Clinical Care and Clinical Research in the EMR
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 Abuse 13-14 July 2009
Michael G. Kahn MD, PhD Biomedical Informatics Core Director Colorado Clinical and Translational Sciences Institute Associate Professor, Department of Pediatrics University of Colorado Director, Clinical Informatics The Children’s Hospital, Denver [email protected]
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
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!) 3
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 4
A Lifecycle View of Clinical Research
T1 Biomedical Research Basic Research Data Pilot Studies Outcomes Research
Clinical Practice
EMR Data Investigator Initiated T1 T2 Translational Research Industry Sponsored Commercialization
New Research Questions Study Design & Approval Study Setup
Evidence based Patient Care and Policy
Evidence-based Review
Clinical Trial Data
Submission & Reporting Recruitment & Enrollment Study Execution
Outcomes Reporting
Public Information
Required Data Sharing 5
From: C Broverman, Partners
How EMR’s could accelerate clinical research (Front-end)
Trial Step Study set up Study enrollment Study execution
EMR potential role
Query EMR database to establish number of potential study candidates.
Incorporate study manual or special instructions into EMR “clinical content” for study encounters 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.
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 6
How EMR’s could accelerate clinical research (Back-end)
Trial Step Submission & Reporting Evidence based review Evidence based clinical care EMR potential role
Provide data extraction formats that support data exchange standards Document and report adverse events 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.
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 7
The EMR & Clinical Research: “Front-End” Issues
T1 Biomedical Research Basic Research Data Pilot Studies Outcomes Research
Clinical Practice
EMR Data Investigator Initiated T1 T2 Translational Research Industry Sponsored Commercialization
New Research Questions Study Design & Approval Study Setup
Evidence based Patient Care and Policy
Evidence-based Review
Clinical Trial Data
Submission & Reporting Recruitment & Enrollment Study Execution
Outcomes Reporting
Public Information
Required Data Sharing 8
From: C Broverman, Partners
Degrees of Constraints #1: The Regulatory Environment
Regulation
HIPAA 45 CFR Part 2 21 CFR Part 50 21 CFR Part 56 21 CFR Part 11 45 CFR Part 46
Regulatory focus
Privacy & Confidentiality of health records Confidentiality of alcohol and substance abuse records FDA Protection of Human Subjects FDA electronic records & e-signature rules OHRP human subjects protection 9
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 10
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 11
Degrees of Constraints #4: Who can see what?
Research ….
Orders Medications Lab results Radiology results Notes Vitals, allergies, care plan, weight, flow sheets, nursing notes, discharge plans Nursing Kardex Research forms or questionnaires Internal Access External Access
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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) 13
Degrees of Constraints #6 (a & b):
Integrating clinical research decisions into clinical care workflows 6a Registration Documentation Results review Billing Release of Information Data extraction into CTMS 6b Solutions must fit EMR functional capabilities Same vendor’s functional capabilities may differ between settings (inpatient versus outpatient) 14
Working down the scenarios….
•Six workbooks •Sixteen research data domains •Data entry versus data visibility •Current versus Desired & Proposed Solution
576 cells
to fill in With 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 paper
Research ….
Internal External No Orders Medications Lab results Radiology results Notes No, Research on paper Non-research in EMR Yes: eMAR shows all meds Yes (via LIS, not in EMR) Non-research in EMR Yes Yes If special confidentiality required, use paper notes Yes Vitals, allergies, care plan, weight, flow sheets, nursing notes, discharge plans Nursing Kardex Research forms or questionnaires No, Research tasks on paper Non-research tasks in EMR No, paper only Yes No Yes No Yes No No
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Our
current
solution…..
Research ….
Orders Medications Lab results Radiology results Notes Vitals, allergies, care plan, weight, flow sheets, nursing notes, discharge plans Nursing Kardex Research forms or questionnaires Internal Access ?
?
?
Yes ?
?
External Access ?
?
?
?
?
?
?
?
?
?
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The EMR & Clinical Research: “Back-End” Issues
T1 Biomedical Research Basic Research Data Pilot Studies Outcomes Research
Clinical Practice
EMR Data Investigator Initiated T1 T2 Translational Research Industry Sponsored Commercialization
New Research Questions Study Design & Approval Study Setup
Evidence based Patient Care and Policy
Evidence-based Review
Clinical Trial Data
Submission & Reporting Recruitment & Enrollment Study Execution
Outcomes Reporting
Public Information
Required Data Sharing 19
From: C Broverman, Partners
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 24
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 27
Thank you!
[email protected]
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