TRANSFoRm: Vision of a learning healthcare system

Transcription

1 TRANSFoRm: Vision of a learning healthcare system Vasa Curcin, Imperial College London Theo Arvanitis, University of Birmingham Derek Corrigan, Royal College of Surgeons Ireland TRANSFoRm is partially funded by the European Commission DG INFSO (FP )

2 TRANSFoRm Consortium

3 Knowledge in healthcare Specific research knowledge Clinical trials Controlled populations Well-defined questions EHR systems Wide coverage Vast quantity May lack in detail and quality Routinely collected knowledge Actionable knowledge Distilled scientific findings Usable in clinical practice Decision support 3

4 Aims of TRANSFoRm To develop the infrastructure in primary care to support: Epidemiological research using GP records, including genotypephenotype 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 Research Question: Are targeted 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 GORD 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) Decision Support 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 5

6 Overall Architecture (1) End User Tools and Services Support Services Middleware (Distributed Infrastructure) Distributed Nodes

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

8 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 Example: Provenance of a query

12 Example: Linkage process

13 Example: Decision support 13

14 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) 14

15 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

16 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) 16

17 TRANSFoRm Integrated VS Server Architecture 17

18 A screenshot of the web based interface 18

19 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 University of Amsterdam The TRANSFoRm team is updating the ICPC-ICD 10 mapping and Thesaurus to eventually incorporate the new UMLS Metathesaurus Read Codes v2 Codes UMLS Metathesaurus ICPC2 Codes SNOMED CT Codes ICD-10 Thesaurus/Codes 19

20 Demonstration of integrating a current prototype of the Study Designer and TRANSFoRm Integrated Vocabulary Service 20

21 epcrn Study Designer and TRANSFoRM VS integration 21

22 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 22

23 ecrf Workflow Design Study Protocol Timeline CRFs Collect Data Study Database Analyse Data The following provide some example screenshots of the ecrf technology on a current prototype 23

24 Protocol Design 24

25 Timeline Design 25

26 CRF Design 26

27 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 27

28 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

29 General model of evidence

30 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

31 Protégé defining concepts and relations

32 Protégé defining instances of knowledge

33 Sesame query platform

34 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

35 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 CTDMS CTDMS CTDMS Pharmaceutical Companies ehr System ehr System ehr System ehr Vendors Clinical Repositories Clinical Repositories Clinical Repositories Clinical Research Networks 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 35

36 TRANSFoRm Anticipated Benefits Support quicker and more economic recruitment and follow up of Randomised Clinical Trials... Improve patient safety... Support large scale phenotypegenotype association studies and follow up on trials... Drive the integration and re use of clinical data stored in different ehr systems... Enhance uptake of ehr systems that offer support for clinical care and research......withanintegratedehrinterfacethatenablestherich capture of clinical data, including symptoms and signs...by providing not only a diagnosis support tool but also a query workbench that supports the identification of patient eligible to participate in clinical trials... through distributed interoperability of ehr data and clinical data repositories that maintain provenance, confidentiality and security... with software tools and web services that support clinical research by enabling use of controlled vocabulary and standardised data elements... 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 36

37 Contact details 1 st 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 37