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Enhancing Patient Recruitment for Clinical Trials Using EMR Data

This session addresses the challenges of patient recruitment for clinical trials, leveraging EMR data for identifying appropriate candidates. Learn about the use case scenario, eligibility criteria, sample protocols, and next steps in improving interoperability in clinical research.

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Enhancing Patient Recruitment for Clinical Trials Using EMR Data

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  1. Clinical Observations Interoperability:A Use Case Scenario Rachel Richesson, PhD, MPH* University of South Florida College of Medicine Clinical Observations Interoperability SessionHCLSIG Face to Face, November 8, 2007 http://esw.w3.org/topic/HCLS/ClinicalObservationsInteroperability * Acknowledgements to the members of the COI Task Force

  2. Outline • Motivation and Background • Need • Use Case Scenario • Eligibility Criteria • Sample Protocols • Challenges • Next Steps

  3. Clinical Sites QuebecCanada Toronto,Canada TokyoJapan Paris, France Lyon,France London Edinburgh,UK Melbourne,Australia Sao Paulo,Brazil Bad Bramstedt, Germany Groningen, Netherlands Cambridge, UK

  4. Motivation and Background • Identification & recruitment of eligible subjects is an obstacle for the conduct of clinical research. • Current screening mostly manual. • Unlikely that all of the data required to assess eligibility for a given protocol will be available in the EMR. • Final eligibility determined by the clinical research staff with F2F assessment. • Applications that identify likely candidates (“probably eligible”) would help researchers target recruitment efforts.

  5. Need for Patient Recruitment • Ability to rapidly identify and recruit children for the right Clinical Trial • Children get access to the latest advances in medicine • Clinical researchers get cohorts to conduct studies • Use Case Scenario: • Can we leverage existing EMR data to identify and recruit appropriate patients for Clinical Trials?

  6. Use Case • Patient Recruitment for Clinical Trials using EMR data • Team effort • Several iterations • Final use-case posted to wiki (URL below): http://esw.w3.org/topic/HCLS/ClinicalObservationsInteroperability?action=AttachFile&do=get&target=Eligibility+Screening_FINAL_10-8-2007.doc

  7. Variations • EMR data-driven triggers • Certain values/clinical scenarios in the EMR data for a patient would trigger retrieval and analysis of more EMR data • This could lead to a dynamic identification of the patient as a recruit for an ongoing clinical trial. • Physician-directed recruitment • Identify appropriate clinical trials for which a patient is eligible, based on his/her data.

  8. Sample Protocol Ages Eligible for Study:  18 Years   -   95 Years,  Genders Eligible for Study:  Both Inclusion Criteria: • Patients will be eligible if they are 18 years of age or older • Fluent in English • Have a known diagnosis of asthma • Will receive treatment for asthma during the current hospitalization or emergency room visit. Exclusion Criteria: • Cognitive deficits • Other pulmonary diseases or severe comorbidity • Do not have out-patient access to a telephone

  9. Eligibility Criteria:Based on Sampled RDCRN Eligibility Criteria (n=452) ; Rachel Richesson, Unpublished Data– DO NOT CITE

  10. Constructs Represented by Sampled RDCRN Eligibility Criteria (n=452)  - cont’d. Note: This is *not* a representative sample so the #/%’s are meaningless.

  11. Challenge: Terminology Standards

  12. Challenge: Information Model Standards

  13. Next Steps • Seek buy-in for Use Case that represents a real world need and provides value to a wide variety of stakeholders in the Healthcare and Life Sciences • Develop a collaborative framework comprising of Providers, Pharma and Vendors • Work towards a POC that demonstrates the feasibility of using EMR data for Clinical Research Next Attraction: Detailed Clinical Models by Tom Oniki

  14. Acknowledgements • Jeff Krischer, PhD, U. of South Florida • Office of Rare Diseases • National Center for Research Resources (RR019259) • DOD - Advanced Cancer Detection Systems (DAMD17-01-2-0056 ) This content does not necessarily represent the official views of NCRR or NIH or DOD.

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