Clinical Observations Interoperability: A Use Case Scenario

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 Stan Huff, Tom Oniki,Bo Anderssen, Kerstin Forsberg, Dan Corwin, Alan Ruttenberg, Susie Stephens, Vipul Kashyap and other members of the COI Task Force.

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

5. 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

6. 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.

7. 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?

8. 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

11. 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.

12. 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

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 Eligibility Criteria:Based on Sampled RDCRN Eligibility Criteria (n=452) ; Rachel Richesson, Unpublished Data– DO NOT CITE

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

15. Challenge: Terminology Standards

16. Challenge: Information Model Standards

18. 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

19. 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.