Transcript Clinical Observations Interoperability

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
– Eligibility Criteria
– Sample Protocols
• Challenges
• Next Steps
Clinical Sites
Quebec
Canada
Toronto,
Canada
Tokyo
Japan
Paris,
France
Lyon,
France
London
Edinburgh,
UK
Melbourne,
Australia
Sao Paulo,
Brazil
Bad Bramstedt, Groningen,
Germany
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?
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/ClinicalObservationsIntero
perability?action=AttachFile&do=get&target=Eligibilit
y+Screening_FINAL_10-8-2007.doc
Research Eligibility Screening Use Case,
Research Coordinator
selects protocol for patient
screening:
9-24-2007
Clinical Research Protocol
Eligibility Criteria:
- Inclusion
- Exclusion
EMR DATA
Meds
Research
Coordinator
views list of
patients and
selects which
ones to approach
in person for
evaluation and
recruitment.
Clinical
Evaluation and
Recruitment
Diagnoses
Procedures
Demographics
Patient MR #
Potentially
Eligible for
Protocol
# Criteria
Met / Total
Criteria in
Protocol
Criteria #1
(Pass/Fail/
Researcher
Needs to
Evaluate)
No Criteria #2
(Pass/Fail/
Researcher
Needs to
Evaluate)
Criteria #3
(Pass/Fail/
Researcher
Needs to
Evaluate)
…
0011111
Yes
6/8 criteria
met
Pass
Pass
Pass
…
0022222
No
3/8 criteria
met
Pass
Fail
Pass
…
0033333
Yes
5/8 criteria
met
Pass
Pass
Fail
…
…
…
…
…
…
…
…
Secondary Scenario, (Patient-Centric) Eligibility Screening
Available protocols mapped to EMR:
Physician evaluates patient
in clinical setting.
Patient data entered in
EMR.
Research
Protocol #1
Research
Protocol #2
Research
Protocol #3
… Research
Protocol #n
Eligibility
Criteria:
- Inclusion
- Exclusion
Eligibility
Criteria:
- Inclusion
- Exclusion
Eligibility
Criteria:
- Inclusion
- Exclusion
Eligibility
Criteria:
- Inclusion
- Exclusion
EMR DATA
Physician views
list of research
protocols in
institution for
which the patient
might be eligible
Meds
Diagnoses
Refer
patient to
researcher
Refer researchers to
patient
Procedures
Demographics
Protocol #
Potentially
Eligible for
Protocol
# Criteria
Met / Total
Criteria in
Protocol
Criteria #1
(Pass/Fail/
Researcher
Needs to
Evaluate)
No Criteria #2
(Pass/Fail/
Researcher
Needs to
Evaluate)
Criteria #3
(Pass/Fail/
Researcher
Needs to
Evaluate)
…
1
Yes
6/8 criteria
met
Pass
Pass
Pass
…
2
No
3/8 criteria
met
Pass
Fail
Pass
…
3
Yes
5/8 criteria
met
Pass
Pass
Fail
…
…
…
…
…
…
…
…
EMR instructs physician
for further action
Further
Clinical
Evaluation
EMR
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
Constructs
– DO NOT CITE
Example
#
%
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 Clvalues.
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%
diagnosis
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
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%
452
100%
Total
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
???
SNOMED CT ??
Labs
LOINC
LOINC-inspired
subset; maintained by
NCI
???
Medications
RxNorm & NDF-RT
???
SNOMED CT ??
(for some realms)
SNOMED CT
NCI Thesaurus subset
???
Vitals
none
CDISC defined value
sets; maintained by
NCI
???
Demographics
Various
CDISC defined value
sets; maintained by
NCI
various
Anatomy (probably
used as qualifiers for
eligibility criteria)
Challenge: Information Model Standards
Info Models
Clinical Research
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
BRIDG
Domain analysis model to
harmonize CDISC & HL7 models;
user-friendly
Detailed Clinical
Models
--
Care Delivery
-Reference Information
Model (RIM)
-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.