clinical observations interoperability: a use case scenario
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Clinical Observations Interoperability:A Use Case Scenario
Rachel Richesson, PhD, MPH*
University of South Florida College of MedicineClinical 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– Eligibility Criteria– Sample Protocols
• Challenges
• Next Steps
Clinical Sites
Toronto,Canada
Paris, France
Edinburgh,UK
Cambridge,UK
Groningen, Netherlands
TokyoJapan
Melbourne,Australia
Sao Paulo,Brazil
Lyon,France
QuebecCanada
Bad Bramstedt,Germany
London
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?
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
Clinical Research ProtocolEligibility Criteria:
- Inclusion- Exclusion
EMR DATA
Meds Procedures
Diagnoses Demographics
…FailPassPass5/8 criteria met
Yes0033333
…………………
Pass
Pass
Criteria #3
(Pass/Fail/ Researcher Needs to Evaluate)
…
…
…
FailPass3/8 criteria met
No 0022222
Pass
No Criteria #2
(Pass/Fail/ Researcher Needs to Evaluate)
Pass 6/8 criteria met
Yes0011111
Criteria #1
(Pass/Fail/ Researcher Needs to Evaluate)
# Criteria Met / Total Criteria in Protocol
Potentially Eligible for Protocol
Patient MR #
Research Coordinator selects protocol for patient screening:
Research Coordinator views list of patients and selects which ones to approach in person for evaluation and recruitment.
Clinical Evaluation and Recruitment
Research Eligibility Screening Use Case, 9-24-2007
EMR DATA
Meds Procedures
Diagnoses Demographics
…FailPassPass5/8 criteria met
Yes3
…………………
Pass
Pass
Criteria #3
(Pass/Fail/ Researcher Needs to Evaluate)
…
…
…
FailPass3/8 criteria met
No 2
Pass
No Criteria #2
(Pass/Fail/ Researcher Needs to Evaluate)
Pass 6/8 criteria met
Yes1
Criteria #1
(Pass/Fail/ Researcher Needs to Evaluate)
# Criteria Met / Total Criteria in Protocol
Potentially Eligible for Protocol
Protocol #
Physician evaluates patient in clinical setting.
Physician views list of research protocols in institution for which the patient might be eligible
Further Clinical Evaluation
Secondary Scenario, (Patient-Centric) Eligibility Screening
Patient data entered in EMR.
Research Protocol #1
Eligibility Criteria:
- Inclusion- Exclusion
Research Protocol #2
Eligibility Criteria:
- Inclusion- Exclusion
Research Protocol #3
Eligibility Criteria:
- Inclusion- Exclusion
… Research Protocol #n
Eligibility Criteria:
- Inclusion- Exclusion
Available protocols mapped to EMR:
EMR
EMR instructs physician for further action
Refer re-searchers to patient
Refer patient to researcher
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.
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 7%
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%
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 2%
administration Sibling with AGS enrolled in study. 6 1%
family history Cardiac : Do any other family members have either cardiac feature? 5 1%
mental status IQ of at least 80. 4 1%
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 0%
vitals Patients must not have systolic blood pressure < 90mm Hg. 1 0%
Total 452 100%
Constructs Represented by Sampled RDCRN Eligibility Criteria (n=452) - cont’d.
Note: This is *not* a representative sample so the #/%’s are meaningless.
Challenge: Terminology StandardsConstruct CHI CDISC HL7
Findings SNOMED CT or NCI Thesaurus
NCI Thesaurus subset ??
SNOMED CT
Procedures SNOMED CT ??? SNOMED CT ??
Labs LOINC LOINC-inspired subset; maintained by NCI
???
Medications RxNorm & NDF-RT ??? SNOMED CT ??(for some realms)
Anatomy (probably used as qualifiers for eligibility criteria)
SNOMED CT NCI Thesaurus subset ???
Vitals none CDISC defined value sets; maintained by NCI
???
Demographics Various CDISC defined value sets; maintained by NCI
various
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
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.