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
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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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A Lifecycle View of Clinical Research
T1 Biomedical Research
Basic
Research Data
Pilot
Studies
Outcomes
Research
Investigator Initiated T1  T2 Translational Research
Industry Sponsored Commercialization
New
Research
Questions
Clinical
Practice
EMR
Data
Evidencebased Patient
Care and
Evidence-based
Policy
Review
Outcomes
Reporting
Public
Information
Study Design
& Approval
Clinical
Trial Data
Submission
& Reporting
Required
Data Sharing
Study
Setup
Recruitment
& Enrollment
Study
Execution
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From: C Broverman, Partners
How EMR’s could accelerate
clinical research (Front-end)
Trial Step
EMR potential role
Study setup
 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 role
Submission
& Reporting
 Provide data extraction formats that support data exchange
standards
 Document and report adverse events
Evidencebased 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.
Evidencebased
clinical care
 Implement study findings as clinical documentation, orders sets,
point-of-care rules/alerts
 Monitor changes in care and outcomes in response to evidencebased clinical decision support
 Provide easy access to detailed clinical care data for motivating
new clinical trial hypotheses
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The EMR & Clinical Research: “Front-End” Issues
T1 Biomedical Research
Basic
Research Data
Pilot
Studies
Outcomes
Research
Investigator Initiated T1  T2 Translational Research
Industry Sponsored Commercialization
New
Research
Questions
Clinical
Practice
EMR
Data
Evidencebased Patient
Care and
Evidence-based
Policy
Review
Outcomes
Reporting
Public
Information
Study Design
& Approval
Clinical
Trial Data
Submission
& Reporting
Required
Data Sharing
Study
Setup
Recruitment
& Enrollment
Study
Execution
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From: C Broverman, Partners
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 50
21 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 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)
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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 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
Orders
No, Research on paper
Non-research in EMR
No
Medications
Yes: eMAR shows all meds
Yes
Lab results
Yes (via LIS, not in EMR)
Non-research in EMR
No
Radiology results
Yes
Yes
Notes
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
?
?
Yes
?
Notes
?
?
Vitals, allergies, care plan, weight, flow
sheets, nursing notes, discharge plans
?
?
Nursing Kardex
?
?
Research forms or questionnaires
?
?
Radiology results
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The EMR & Clinical Research: “Back-End” Issues
T1 Biomedical Research
Basic
Research Data
Pilot
Studies
Outcomes
Research
Investigator Initiated T1  T2 Translational Research
Industry Sponsored Commercialization
New
Research
Questions
Clinical
Practice
EMR
Data
Evidencebased Patient
Care and
Evidence-based
Policy
Review
Outcomes
Reporting
Public
Information
Study Design
& Approval
Clinical
Trial Data
Submission
& Reporting
Required
Data Sharing
Study
Setup
Recruitment
& Enrollment
Study
Execution
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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
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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!
[email protected]
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