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

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

24 Open Studies Over 1700 enrolled in studies Over 3200 enrolled in contact registry

Clinical Sites London Quebec Canada Toronto, Canada Tokyo Japan Paris, France Lyon, France London Melbourne, Australia Edinburgh, UK Sao Paulo, Brazil Bad Bramstedt, Germany Groningen, Netherlands Cambridge, UK

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.

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? Easier to create also because of lack of tooling….

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

Variations EMR data-driven triggers Physician-directed recruitment 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.

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

Eligibility Criteria: Based on Sampled RDCRN Eligibility Criteria (n=452) ; Rachel Richesson, Unpublished Data – DO NOT CITE Constructs Example  #  %  diagnosis                                  Confirmed diagnosis of PCD.  66  15%  consent  Is the subject or legal representative able to give informed consent?  60  13%  finding  Known or suspected PHA (or variant PHA), which might include elevated (or borderline) sweat Cl- values.  54  12%  disease  Other disorders of chronic sino-pulmonary disease.  46  10%  condition  Intercurrent infection at initiation of study drug.  31  7%  lab  Decreased AS enzyme activity in cultured skin fibroblasts or other appropriate tissue.  34  8%  mutation  Atypical deletion.  30  logic  One of three criteria above is met when other affected family members meet the other two criteria.   26  6%  patient characteristic  Age between 1 day and 5 years old.  22  5%  medication  High dose folate or derivative within last 12 months/  19  4%  procedure done  Has had liver transplant.  15  3%   

Constructs Represented by Sampled RDCRN Eligibility Criteria (n=452)  - cont’d. Construct   Example  #  %  reproductive potential  If female of child bearing potential and sexually active, agrees to use an acceptable method of birth control.  12  3%  study arm  Group A: Low Risk.  10  2%  procedure findings  An abnormal long exercise CMAP test.  8  administration  Sibling with AGS enrolled in study.  6  1%  family history   Cardiac : Do any other family members have either cardiac feature?  5  mental status  IQ of at least 80.  4  anthropometry  Extreme low birth weight (<1500 g).  2  0%  risk behaviors  10. Has the subject smoked cigarettes or marijuana at all in the prior year?  1  vitals  Patients must not have systolic blood pressure < 90mm Hg.  Total  452  100%  Note: This is *not* a representative sample so the #/%’s are meaningless.  

Challenge: Terminology Standards Construct CHI CDISC HL7 Findings SNOMED CT or NCI Thesaurus NCI Thesaurus subset ?? SNOMED CT Procedures ??? SNOMED CT ?? Labs LOINC LOINC-inspired subset; maintained by NCI Medications RxNorm & NDF-RT (for some realms) Anatomy (probably used as qualifiers for eligibility criteria) NCI Thesaurus subset Vitals none CDISC defined value sets; maintained by NCI Demographics Various various RDN infrastructure helps  informaticist streamlines this Added new terms to these On an as-needed basis Done by DTCC

Challenge: Information Model Standards Info Models Clinical Research Care Delivery CDISC Standards SDTM – dataset submission to FDA PR – Protocol representation (eligibility criteria currently FT) Others…. (Bron, Bo & Kirsten) -- HL7 Standards RCRIM SIG consists of members from CDISC, NCI, FDA Reference Information Model (RIM) BRIDG Domain analysis model to harmonize CDISC & HL7 models; user-friendly Detailed Clinical Models In use at Intermountain Healthcare; real experience

Endorse standards; Define boundaries of use (“where”); Define guidelines for use (“how’) How place standards in context of workflow artifacts like PE form, MH form? Choosing standard just half the battle; Consistent use of standard required to communicate and share information

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

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.