TRANSF o R m : Vision of a learning healthcare system

1. Translational Research and Patient Safety in Europe TRANSFoRm: Vision of a learning healthcare system Vasa Curcin, Imperial College London Theo Arvanitis, University of Birmingham Derek Corrigan, Royal College of Surgeons Ireland www.transformproject.eu TRANSFoRm is partially funded by the European Commission  - DG INFSO (FP7 247787)

2. Translational Research and Patient Safety in Europe TRANSFoRm Consortium

3. Knowledge in healthcare • Clinical trials • Controlled populations • Well-defined questions • Distilled scientific findings • Usable in clinical practice • Decision support • EHR systems • Wide coverage • Vast quantity • May lack in detail and quality

4. Translational Research and Patient Safety in Europe Aims of TRANSFoRm • To develop the infrastructure in primary care to support: • Epidemiological research using GP records, including genotype-phenotype studies and other record linkages • Research workflow embedded in the EHR • Decision support for diagnosis • Infrastructure components: • Implementation methods • Models for data and process organization • Services • Architectures • Demonstrations with industry 3

5. TRANSFoRm Use Cases Type 2 Diabetes GORD Decision Support • Research Question: • Are well selected SNPs in T2D patients associated with variations in drug response to oral antidiabetics? • Genotype-Phenotype record linkage study: • Privacy model • Record linkage (browsing, selecting, extracting) • Data quality tool • Provenance tool • Research Question: • Effectiveness of continuous versus on demand PPI use? • Electronic CRF embedded in the eHR • RCT with event-initiated patient-related outcome measures: • Trigger within EHR • Semantic Mediator • eCRF tool (embedded in EHR) • Experimental Study: Comparing approaches to diagnostic support • Alerting versus suggesting Clinical Prediction rule web service (with underlying ontology) • Prototype DSS integrated into EHR system • Domains: • chest pain • abdominal pain • shortness of breath

6. Middleware (Distributed Infrastructure) Translational Research and Patient Safety in Europe End User Tools and Services Distributed Nodes Support Services Overall Architecture (1)

7. Event-based trigger tool Clinical Evidence Content Manager Data Mining Analysis Toolkit Visual model explorer Overall Architecture (2) End User Tools and Services Study Design Query Workbench Study Management GP Decision Support Identify/Recruit Eligible Patients Interactive Consultation Decision Support Tool Eligibility Criteria Designer Protocol Designer Consent Data Manager eCRFDesigner Study eCRF Data Collector Timeline Designer Query formulation and execution tool Patient-reported outcome Manager CDE Designer Clinical Evidence Service Semantic Mediation Data Mining and Analysis Data Provenance Vocabulary Service Clinical Evidence Extraction Tool Provenance Capture Service Provenance Audit Tool Data quality tool Support Services (e.g. rule based security, authentication) Middleware (Distributed Infrastructure) Distributed Nodes

8. Translational Research and Patient Safety in Europe Models in TRANSFoRm • Clinical Data Integration Model (CDIM) • Mapping clinical data from EHRs and aggregated data repositories • Clinical Research Information Model (CRIM) • Research process information • Evolution of Primary Care Research Information Model (PCROM). 9

9. Provenance • Processing history of a data item • Actors involved • Data sources used • Operations performed • Data movements • Authorizations • Supports: • Reliability • Accountability • Auditability • Related research areas: • Workflows standardizing representation of data processing • Information systems tracking data evolution

10. Provenance benefits • System monitoring • Entity oriented, rather than raw logs • Exploratory investigations possible • User accountability • Data items and actions directly connect back to the user • Traceability • Every step in the evolution of a result becomes easily accessible • Reuse and repurposing • Storing previous queries for use by recommendation engines and the users directly • Data warehouse • Large-scale statistics about resource usage, organisational performance and user activity

11. Architecture DB1 DBn Provenance API Provenance API Prov. Query Service SQL SPARQL Local Service Prov. Query Service SQL SPARQL Capture Capture Represen-tation Represen-tation CP DB Reasoner Reas. & DM Storage Storage Metadata Registry MD DB LP DB Local Zone Centralized Zone

12. Example: Provenance of a query

13. Example: Linkage process

14. Example: Decision support

15. 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) • including patient safety (medical errors) • 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 • TRANSFoRm provides this through an Integrated Vocabulary Service (TRANSFoRm VS)

16. Challenges for EU TRANSFoRm • Vocabulary Services have been traditionally provided by the NCI • Cross Mapping of Vocabularies • The case of Primary Care in Europe: the difficulty with using the NCI EVS in European Primary Care is that neither Read codes nor ICPC2 are part of the service • some mapping exists with UMLS (but many concepts missing) • EVS can be augmented by referencing Read codes to UMLS codes via SNOMED CT (where a cross-mapping exists) and by adding the ICPC-2 mapping through ICD-10 • Evolving technologies and APIs for EVS • LexEVSAPI – various versions from 3.0 to 5.0

17. Application Architecture • The TRANSFoRm Integrated Vocabulary Service is designed to allow end users to search and retrieve clinical vocabulary concepts and associated content • a web interface and a web service API • the service uses the LexEVS (version 5.1) technology to access a backend UMLS vocabulary database • the service uses direct Java Database Connectivity (JDBC) to access other vocabulary databases (e.g. Read Codes V2, ICPC2)

18. TRANSFoRm Integrated VS Server Architecture

19. A screenshot of the web-based interface

20. RCD v2/ICPC2 UMLS Metathesaurus • 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 • to eventually incorporate the new Thesaurus into UMLS. SNOMED CT Codes Read Codes v2 Codes UMLS Metathesaurus ICD-10 Thesaurus/Codes ICPC2 Codes

21. Demonstration of integrating a current prototype of the Study Designer and TRANSFoRm Integrated Vocabulary Service

22. ePCRN Study Designer and TRANSFoRM VS integration

23. eCRF Introduction • eCRF is electronic version of case report form (CRF) • CRF are forms designed by clinical study investigators to collect data about each subject during the whole study process • CRFs are usually filled in by research staff, but can also be filled in by practice staff if CRFs are ‘integrated’ into practice system • Collected data are saved and analysed afterwards

24. eCRF Workflow Protocol Timeline CRFs Study Database Design Study Collect Data Analyse Data The following provide some example screenshots of the eCRF technology on a current prototype

25. Protocol Design

26. Timeline Design

27. CRF Design

28. Decision Support Tool The Decision Support Tool will provide patient-specific advice at the moment of consultation so that clinicians are able to access and quantify likely differential diagnoses framed in terms of diagnostic probability and alternative diagnostic possibilities. Decision Support Tool Characteristics: • Embedded within the eHR • Triggered by a ‘reason for encounter’ • Presents diagnostic prompts based on ontology service for clinical prediction rules • Collects ontologically controlled diagnostic cue data • Alerts/suggests for potential missed diagnoses

29. Decision support objectives • Develop a model of clinical evidence upon which diagnostic decision support is provided • Formulating and quantifying potential differential diagnoses based on presenting patient cues from primary care EHRs • Linked to primary care EHRs and based on clinical knowledge derived from electronic sources of research data

30. Method • Ontological approach - define an ontology of clinical evidence to represent what we know about defined clinical scenarios • Provides for top down and bottom up reasoning to formulate potential diagnoses • Clinical Prediction Rule – used as one possible mechanism to interpret that evidence by applying some quantification of the importance of the constituent diagnostic cues

31. General model of evidence

32. Development Tools • Protégé – Ontology Development • Sesame Triple Store – provides persistent representation enabling dynamic update of knowledge • Sesame API – provides for programmatic update/manipulation and provision of clinical evidence web service for decision support

33. Protégé – defining concepts and relations

34. Protégé – defining instances of knowledge

35. Sesame query platform

36. Future DSS work • Focus moving to defining query and update interfaces as part of a defined evidence service • Allow for generation and update of knowledge from data mining done on electronic sources of primary care data • Working with EHR vendors to define user interface requirements

37. TRANSFoRm Vision TRANSFoRm will drive the integration of clinical research and clinical practice by developing tools & services to facilitate greater system interoperability. TRANSFoRm Tools & Services Pharmaceutical Companies CTDMS CTDMS CTDMS eHR System eHR System eHR System eHR Vendors Clinical Research Networks Clinical Repositories Clinical Repositories Clinical Repositories CTDMS: Clinical Trial Data Management Software e.g. Oracle Clinical eHR: Electronic Heath Record e,g, EMIS Web Clinical Repositories e.g. GO-DARTs Tayside

38. TRANSFoRm Anticipated Benefits Support quicker and more economic recruitment and follow-up of Randomised Clinical Trials... ...with an integrated eHR interface that enables the rich capture of clinical data, including symptoms and signs Improve patient safety... ...by providing not only a diagnosis support tool but also a query workbenchthat supports the identification of patient eligible to participate in clinical trials Support large scale phenotype-genotype association studies and follow-up on trials... ... through distributed interoperability of eHR data and clinical data repositories that maintain provenance, confidentiality and security Drive the integration and re-use of clinical data stored in different eHR systems... ... with software tools and web-services that support clinical research by enabling use of controlled vocabulary and standardised data elements Enhance uptake of eHR systems that offer support for clinical care and research... ... by adopting an open-source business model, allowing eHR vendors and data integrators direct cost savings and the ability to reach more customers through improved pricing flexibility

39. Contact details • www.transformproject.eu • 1st year deliverables publicly available • eCRF – EHR integration • Theo Arvanitis, University of Birmingham • Decision Support System • Derek Corrigan, Royal College of Surgeons Ireland • Provenance, general queries • Vasa Curcin, Imperial College London