This document discusses using an electronic medical record (EMR) to support clinical research. It outlines how EMR data can be used throughout the research process, including determining study feasibility, including data in grant applications, patient recruitment, study interventions, data collection, and assessing study outcomes. While EMRs provide rich clinical data and can streamline aspects of research, the data was primarily collected for clinical care so requires validation for research purposes. Fully integrating research workflows into EMRs remains a challenge.

1. Use of an EMR-based Registry
to Support Clinical Research
December 14, 2012
John Sharp, MSSA, PMP, FHIMSS
Manager, Clinical Research Informatics
Quantitative Health Sciences

2. Clinical Data Repositories
The Clinical Data Repository (CDR) is the
“perfect infrastructure” to run clinical trials
“we’re moving into an era of data. To have data analytics
allow for population-based care management, we need
to make sure we have the tools that really work for
providers.”
Steven R. Steinhubl, MD, director of Geisinger Health
System’s Cardiovascular Wellness Center
Clinical Innovation and Technology 10/30/12
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3. Outline
• Determining study feasibility with EMR data
• EMR data for inclusion in grant applications
• EMR data in clinical trial recruitment
• EMR use in study intervention
• EMR data for collecting data
• EMR data for study outcomes
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4. Using EMR data in Research
• Rich clinical data available
• Large amounts of data – 4M patients, > 10 years
• CKD Registry – almost 60,000 patients since 2005
• Largest registry of its kind for CKD
But
• Some missing data
• Data not as clean as clinical trial data management
• Some patients lost to follow up
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5. Overcoming obstacles to the use of EMR data
• Large volume of data
• Inference tools
• Data validation
• Improving data entry which can enable research
• Becoming familiar with data outputs
• Standardize definitions, rules
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6. CKD Registry Technical Architecture
• Oracle
Nightly • Oracle • Refresh
Epic Extract Clarity • Views QHS weekly
or
monthly
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7. STUDY FEASIBILITY
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8. Study Feasibility
• “We have developed and validated a comprehensive
EHR-based CKD registry, which identified nearly
60,000 CKD patients with their attendant co-
morbidities in the CCHS. The CKD Registry enabled
us to identify nearly 18,000 patients with CKD Stages
3B and 4 who may be eligible for enrollment in our
proposed randomized control trial.”
• From NIDDK grant application
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9. Should all proposals be required to do study feasibility?
• Prevent studies which fail to recruit adequate number
of subjects
• Inclusion/exclusion criteria can be modified to select a
broader or narrower cohort
• Plan for recruiting at main campus only or including
Family Health Centers, Regional Hospitals or other
sites
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10. USING EMR DATA IN GRANT
APPLICATIONS
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11. Grant Application from Registry - 1
• We have developed an EHR-based CKD registry at the
Cleveland Clinic and validated comorbid conditions.
Patients who had at least one face-to-face outpatient
encounter with a Cleveland Clinic health care provider
and a) had two eGFR values <60 ml/min/1.73 m2 more
than 90 days apart as of January 1, 2005 and/or b)
were designated with International Classification of
Diseases (ICD-9) codes for kidney disease (used twice
in an outpatient encounter) were included
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12. Grant Application from Registry - 2
Mean age was 69.5 ±13.4 years, with 55% females and
12% African Americans. The kappa statistics to assess
the extent of agreement between the administrative
dataset derived from the EHR and actual EHR chart
review showed substantial agreement (≥ 0.80) for all
conditions except coronary artery disease and
hypertension which had moderate agreement (<0.60)
suggesting the reliability of the registry. Our CKD
registry will be used to identify and recruit patients for
this clinical trial.
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13. PATIENT RECRUITMENT
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14. Patient Recruitment – contact by phone, mail
• Filter patients in the registry who fit inclusion/exclusion
criteria
• Generate address list for initial contact
• Primary care physician – contact to allow to opt out of
recruiting a patient
• Phone contact follow up
• Some Epic sites using silent alerts – when a patient fits
criteria, message sent to study coordinator
• Physician alerts – often ignored. See Embi, J Am Med
Inform Assoc. 2012 Jun;19
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15. Patient Recruitment
• Report which filters on eligible patients
• Indicates location of next appointment
• Includes patient phone number
• Research coordinator can call patient and arrange a
meeting to sign consent, do pre-screening
questionnaire
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16. Patient Recruitment Report
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17. Recruitment Results
Month Count
Aug-12 23
Sep-12 6
Oct-12 20
Nov-12 11
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18. STUDY INTERVENTION
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19. Study Intervention – Enhanced MyChart
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20. Study Intervention
• Patient use of enhanced MyChart vs. patient use of
standard MyChart
• Does it make a difference in outcomes?
• Current studies show mixed results
• PHR use, but not intensity of use, was associated with
improved diabetes quality measure profiles. To
maximize value, next-generation PHRs must be
designed to engage patients in everyday diabetes self-
management. J Gen Intern Med. 2012 Apr;27(4):420-4
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21. EMR FOR COLLECTION DATA
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22. Data Collection for Research through the EMR
• Registries – discrete data available
• Use of Smart Data Elements – gather discrete data for
a registry or clinical trial
• Clinical trials – routinely transfer lab data electronically
from Epic/Clarity to study database
• Other tests/procedures, e.g., echo results, imaging
studies, pathology
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23. Issues in Data Collection through the EMR
• Special forms created for a clinical trial not easily
archived, may pop up in clinical workflow after study
end
• Determining which visits are research visits – transfer
only that data
• Health Status measures through MyChart – not
routinely used, how to use at research visit?
• Knowledge Program and Clinical Solutions addressing
this
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24. Data Collection from the EMR
• For CKD study:
– Collecting specific lab results
– Collecting comorbidities
– Collecting information on Patient Navigator interventions
– Information on outpatient encounters
– Outcomes will be focused on lab results – rate of progression of
CKD
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25. STUDY OUTCOMES
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26. Data on Study Outcomes
• Straightforward if the outcomes are discrete data, such
as, lab results, procedure results
• More challenging if the outcomes are within notes
(encounter notes, pathology or microbiology notes,
imaging notes)
• May require interpretation and then entry into a
separate database with web forms
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27. Additional Issues in EMR data in Research
• Data preparation for analysis
• Lack of coding of data into standard ontologies, such
as, LOINC, CDISC, MEDRA, WHODrug
• Dynamic nature of EMR data, e.g., labs flagged as
initial results vs. final
• Development of Smart Data Elements may extend
timeline
• Device data not always available
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28. Data Management – Inside or Outside the EMR
Inside Outside
• Fit with clinical workflow • More flexibility in form
design, data collection
• All data in one place
• Need for custom • Import data from EMR,
data less dynamic
templates, forms
• Health Status measures • Can code data
through MyChart or other • Health status/QOL
means measures via REDCap or
other survey tool
• Requires data integration
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29. Epic 2010 – Research Studies
Can implement multiple features
• Study recruitment
• Associating patients with studies
• Associating encounters with studies
• Associating orders with studies
• Billing for research, including charge routing and
review
• Releasing information for research patients
• Potential interface with Clinical Trial Management
System
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30. Conclusions
• EMRs can be used throughout the research pipeline
• Use of the EMR can streamline data collection
• However, EMR data use in research requires
validation since data is collected primarily for clinical
care
• There is not yet seamless integration of research
workflow into the EMR
• Disease Registries outside the EMR can be utilized for
many aspects of clinical trials
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31. In Press
• Electronic Health Records: a new tool to combat
chronic kidney disease?
• Clinical Nephrology
• SD Navaneethan, SE Jolly, J Sharp, A Jain, JD
Schold, MJ Schreiber, JV Nally
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