Theodoros. N. Arvanitis, RT, DPhil, CEng, MIET, MIEEE ... · Codes version 2 to SNOMED CT. • The Read Codes v2 database in Transform VS is set up based on this mapping so that Read

TRANSFoRm

Theodoros. N. Arvanitis, RT, DPhil, CEng, MIET, MIEEE, AMIA, FRSM

Biomedical Informatics, Signals & Systems Research LaboratorySchool of Electronic, Electrical & Computer Engineering

College of Engineering and Physical SciencesUniversity of Birmingham

Birmingham Children’s Hospital NHS Foundation Trust

TRANSFoRm is partially funded by the European Commission - DG INFSO (FP7 247787)

1

Specific research

knowledge

Actionable knowledge

Routinely collected

knowledge

Knowledge in healthcare

2

• Clinical trials

• Controlled populations

• Well-defined questions

• EHR systems

• Wide coverage

• Vast quantity

• May lack in detail

and quality

• Distilled scientific

findings

• Usable in clinical

practice

• Decision support

The challenge of representing knowledge in an interoperable computable form

• Developing a user understandable, computable and extensible knowledge representation scheme for capturing clinical trials’ concepts and information (knowledge)

– with a multilingual support

• The foundation of interoperability lies with a shared understanding of concepts and data representation between systems:

– it is necessary to establish both syntactic (model-based) and semantic interoperability to represent knowledge in a computable form

3

Cohort identification as part Clinical trials life-cycle

Adapted from Source: Douglas Fridsma, MD, PhD

The University of Pittsburgh Cancer Institute Centre for Pathology and Oncology Informatics

Clinical Trials Research Core

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Query Formulation Workbench

• Provides tools necessary to author, store and deploy queries of clinical data toidentify subjects for clinical studies:

– query authoring for the identification of research subjects based on existingeHR data

– use of semantic mediator services

• Semantically aware

• Enables easy authoring of distributed searches to EHR and other clinical data sources

• Uses a controlled vocabulary service and appropriate standards-based technological solutions

• Automatically identifies ‘prevalent cases’ for research

– Count eligible subjects, flag the subjects for recruitment and consent by the local clinical care team

– Full compliance with data protection legislation and best practice

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Overall Design Approach

• The development of the system adopts a model-based approach, where the TRANSFoRm Clinical Research Information Model (CRIM) provides a computable information model for eligibility criteria.

• Criteria concepts, especially clinical concepts, can be browsed and selected through the TRANSFoRm Integrated Vocabulary Service.

– The vocabulary service provides mappings from standard UMLS concepts to standard EHR or clinical data sources’ coding schemes.

• The eligibility criteria are captured in a computable representation, based on the Clinical Data Integration Model (CDIM) ontology

– CDIM captures an extensible common representation of clinical care data

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Conceptual Architecture

Clinical Researcher

Vocabulary

Service

(1) C

linica

l Codes

(2) Search Criteria EHR

DS

(3) Local Query

(4) Query Result(5) Query Result

TRANSFoRm Query

Formulation Workbench

CDIM-DSM

mappingCDIM Ontology

Provenance Service

Distributed Infrastructure

for Data Extraction and

Linkage

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Vocabulary Service: RCD v2/ICPC2

• Read Codes (RCDv2) and International

Classification of Primary Care (ICPC2)

corpus of terms and their associated

mappings

– created to cater for the initial need of

the existence of specific primary care

oriented terminologies.

• The UK NHS Connecting for Health

Terminology Centre - mappings from Read

Codes version 2 to SNOMED CT.

• The Read Codes v2 database in

Transform VS is set up based on this

mapping so that Read Codes 2 concepts

can be linked to a UMLS search. Similar

approach for ICPC2.

• ICPC2-ICD10 Thesaurus and mappings -

Transition Project @ University of

Amsterdam

• The TRANSFoRm team is updating the

ICPC-ICD 10 mapping and Thesaurus

UMLS

Metathesaurus

Read Codes v2

Codes

SNOMED CT

Codes

ICPC2

Codes

ICD-10

Thesaurus/Codes

UMLS

Metathesaurus

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Demo of the Vocabulary Service

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Demo of Eligibility Criteria Creation

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Submitting Queries

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Identifying prevalent

cases through eligible counts

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Dr Theodoros N. Arvanitis

University of BirminghamBirmingham Children’s Hospital NHS Trust

University of Birmingham Clinical Informatics Research Team: Sarah Lim Choi Keung, James Rossiter, Lei Zhao

TRANSFoRm