1. Clinical Observations Interoperability (COI): How can Semantic Web Technologies Help?
Vipul Kashyap*
Senior Medical Informatician, Clinical Informatics R&D
Partners Healthcare System
Clinical Observations Interoperability Session,
HCLSIG Face to Face November 8, 2007
Cambridge, MA
*Acknowledgments: Members of the HCLSIG/COI Task Force

2. Outline Healthcare and Life Sciences (HCLS): A Taxonomy
HCLS Ecosystem: Current and Goal State
Use Cases and Functional Requirements
The Role of Semantics:
Data Content
Data Exchange and Interoperability
Computable Protocol Specification
Data Retrieval
Conclusions
Coming Attractions!
Next Steps

3. Healthcare and Life Sciences: A Taxonomy
Research
Practice
Clinical
Biological
Translational
Medicine
Public Health
Biosurveillance

4. HCLS Ecosystem: Current State
Characterized by silos with uncoordinated
supply chains leading to inefficiencies in the system
Patients,
Public
Patients
FDA
National Institutes
Of Health
Center for
Disease
Control
Pharmaceutical
Companies
Hospitals
Payors
Universities,
Academic Medical
Centers (AMCs)
Clinical Research
Organizations (CROs)
Hospitals
Doctors
Biomedical Research
Clinical Practice
Patients
Patients
Clinical Trials/Research
Clinical Practice

5. HCLS Ecosystem: Goal State
NIH
(Research)
FDA
CDC
Pharmaceutical
Companies
Universities,
AMCs
Patients, Public
CROs
Hospitals
Doctors
Payors
From FDA, CDC
Clinical Observations Interoperability will be a
Critical Enabler to realize this Vision!

6. Use Cases and Functional Requirements
X identifies the Use Cases, Systems and Functional Requirement under consideration of the
COI Task Force
Based on the Functional Requirements Specification developed by EHRVA/HIMSS
Available at
on Google Spreadsheets

7. Use Case: Patient Screening
Clinical
Research
Protocol
Eligibility Criteria: -
Inclusion
Exclusion
EMR DATA
Meds
Procedures
Diagnoses
Demographics …
Fail
Pass
5/8 criteria
met
Yes
Criteria #3
(Pass/Fail/
Researcher
Needs to
Evaluate)
3/8 criteria No
No Criteria #2
6/8 criteria
Criteria #1
# Criteria
Met / Total
Criteria in
Potentially
Eligible for
Patient MR #
Research Coordinator
selects protocol for patient
screening:
Coordinator
views list of
patients and
selects which
ones to approach
in person for
evaluation and
recruitment.
Evaluation and
Recruitment
Focus of Next Talk by Rachel Richesson

8. Role of Semantic Web Technologies: Examples
Data Content
Precise, Generalized and Extensible Specification
Shareable Open Source Models of Clinical Data
Data Exchange and Interoperability
Re-use and alignment of independently developed Industry Standards
Multiple Ways of saying the same thing
Computable Protocol Specification
Precise and Unambiguous Specification
Approximate Matching
Data Retrieval
Ability to identify relevant patients in the absence of explicit data about a clinical condition

9. Precise, Generalized and Extensible Specifications
A Systolic Blood Pressure measurement is a pressure measurement
that has a value from 0 to 220 and units are mmHg
SystolicBloodPressureMeasurement
equivalentClass
ClinicalElement
that key value “SnomedCodeForSystolicBP”
and magnitude only float[>= 0.0, <= 220.0]
and units value mmHg
A Sitting Systolic Blood Pressure Measurement is a systolic blood
pressure measurement taken when a patient is sitting
SittingSystolicBloodPressureMeasurement
equivalentClass
SystolicBloodPressureMeasurement
that bodyPosition value Sitting

10. Shareable Open Source Models of Clinical Data
DCM
SDTM
BRIDG
Snomed
MedDRA
NCIT
…..
Clinical Trial 1
Healthcare Provider 1
Clinical Trial 2
Healthcare Provider 2
Clinical
Observations
Clinical
Observations … …
Clinical Trial M
Healthcare Provider N
Focus of Talk by Tom Oniki

11. Re-use and Alignment of Independently developed Industry Standards
SDTM:
SystolicBloodPressureMeasurement
equivalentClass
VSTEST
that VSTESTCD value “SYSBP”
and VSORRESU value mmHg
DCM:
SystolicBloodPressureMeasurement
subClassOf
key value “SnomedCodeForSystolicBP”
and magnitude only float[>= 0.0, <= 220.0]
and units value HL7:PQ:mmHg
Alignment/Mappings:
DCM:SystolicBloodPressureMeasurement equivalentClass
SDTM:VSTEST that VSTESTCD value “NCITCodeForSYSBP”
DCM:key equivalentProperty SDTM:VSTESTCD
DCM:units equivalentProperty VSORRESU
DCM:magnitude equivalentProperty VSSORRES
SDTM:SYSBP sameAs “NCITCodeForSYSBP”
“SnomedCodeForSystolicBP” sameAs “NCITCodeForSYSBP”
HL7:PQ:mmHg sameAs VSORRESU:mmHg

12. Enabling Clinical Observations Interoperability
“Mr. X”
“T1”
“Mr. X”
“T2”
name
name
recording_time
recording_time
systolicBP
systolicBP
Patient
(id = URI1)
SystolicBP
Measurement1
Patient
(id = URI1)
SystolicBP
Measurement2
magnitude
VSORRES
key
VSTESTCD
120
130
units
VSORRESU
SnomedCodeForSystolicBP
NCITCodeForSYSBP
mmHg
mmHg
EMR Data
Clinical Trials Data

13. Enabling Clinical Observations Interoperability
“mmHg”
“NCITCodeForSYSBP”
“T2”
“Mr. X”
recording_time
name
SystolicBP
Measurement2
systolicBP
Patient
(id = URI1)
magnitude
“T1”
130
recording_time
SystolicBP
Measurement1
magnitude
120

14. Merged Patient Data <http:URIForPatient> rdf:type DCM:Patient
DCM:name “Mr. X”
DCM:systolicBP DCM:systolicBPMeasurements
DCM:systolicBPMeasurements
rdf:type Collection
DCM:units mmHg
DCM:key “SnomedCodeForSystolicBP”
rdf:_1 _:SystolicBPMeasurement1
rdf:_2 _:SystolicBPMeasurement2
_:SystolicBPMeasurement1 DCM:recordingTime T1
_:SystolicBPMeasurement1 DCM:magnitude 120
_:SystolicBPMeasurement2 DCM:recordingTime T2
_:SystolicBPMeasurement2 DCM:magnitude 130

15. Multiple ways of saying the same thing!
Patient Record 1
Name = Mr. X
Recording Time = T1
Weight = 70 Kg
WeightType = “Dry”
Patient Record 2
Name = Mr. X
Recording Time = T2
DryWeight = 70 Kg
Are these two weight measurements comparable?
Assert the following Mapping:
DryWeight equivalentClass Weight that type value “Dry”
The OWL Ontology Engine will infer the rest.

16. Precise Protocol Specification
Prophylactic Irradiation to the Contralateral Breast for BRCA Mutation Carriers Undergoing Treatment for Breast Cancer
Ages Eligible for Study:  30 Years   -   90 Years, Genders Eligible for Study:  Female
Inclusion Criteria:
Female patient diagnosed with stage I-III breast cancer (AJCC 6), undergoing breast irradiation as part of her adjuvant therapy.
The patient must be a carrier of a deleterious mutation in BRCA 1/2. …
Exclusion Criteria:
Metastatic breast cancer.
Previous irradiation of the breast or chest wall.
Pregnancy.
Patients with active connective tissue diseases are excluded due to the potential risk of significant radiotherapy toxicity.

17. Precise Protocol Specification
Patient
that hasAge only float[>=30, <=60]
and hasGender value “Female”
and hasDiagnosis some StageI-IIIBreastCancer
and hasTherapy some BreastIrradiation
and hasMutation some (mutationType value deleterious and mutationGene value BRCA1/2)
and not (hasDiagnosis some (BreastCancer that cancerType value Metastatic))
and not (hasTherapy some Irradiation that hasLocation some (Chest or BreastWall))
and not (hasCondition some Pregnancy)
and not (hasDisease some (Disease that hasLocation some ConnectiveTissue
and type value Active)) …
Focus of Next Talk by Chintan Patel

18. Approximate Matching
Subsumption reasoning helps match patients against BreastIrradiationTherapy and it’s subclasses as opposed to a String match with “BreastIrradiationTherapy”.
Systematic query weakening by dropping logical conditions can be used to identify more patients who can later be filtered based on further testing.
“Many ways of stating the same thing”: Infer eligibility based on patient data that doesn’t explicitly assert data satisfying a logical condition.

19. Identify Patients in the absence of explicit data about a clinical condition
Patient Record 1
Name = Mr. X
Disease = Hematology
Disorder
Patient Record 2
Name = Mr. Y
Hypereosinophily = 2.0
Lymphosytosis = 6
Blood Lymphocytes = ATypical
Which of these two patients have Hematology Disorder?
Assert the following Mapping:
PatientWithHematologyDisorder
equivalentClass
Patient
that hypereosinophily only float[>1.5]
and lymphosytosis only float [> 5]
and bloodLymphocytes value ATypical
The OWL Ontology Engine will infer the rest.

20. Conclusions
Critical Need to Align Data, Information and Knowledge across the HCLS Ecosystem
Clinical Observations Interoperability is a critical enabler
Need for:
Open source sharable web based specifications and technologies
Semantic Technologies
Initial Analysis seem to suggest the feasibility and applicability of Semantic Web technologies.

21. Coming Attractions! Patient Recruitment Use Case:
Rachel Richesson
Open Source Detailed Clinical Models:
Tom Oniki
Demo: Semantic DB System
Chimezie Ogbuji
Demo: SHER System
Chintan Patel

22. Next Steps
There is a critical need to develop a collaborative framework of various stakeholders such as healthcare providers, pharmaceuticals and IT vendors to address this important problem.
Goal: To build consensus and seek participation (time, resources, money!) of these stakeholders to develop a POC that demonstrates feasibility and validates the value proposition.
Brainstorming Session: 4:30pm – 5:30pm