1. 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
* Acknowledgements to the members of the COI Task Force

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

4. 24 Open Studies
Over 1700 enrolled in studies
Over 3200 enrolled in contact registry

5. 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

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

8. Use Case Patient Recruitment for Clinical Trials using EMR data
Team effort
Several iterations
Final use-case posted to wiki (URL below):

11. 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.

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
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% 

14. 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.

15. 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

16. 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

17. 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

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 (DAMD )
This content does not necessarily represent the official views of NCRR or NIH or DOD.