Transcript Translational Medicine 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
TRANSFoRm is partially funded by the European Commission DG INFSO (FP7 247787)
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TRANSFoRm Consortium
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Knowledge in healthcare
Specific
research
knowledge
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EHR systems
Wide coverage
Vast quantity
May lack in detail
and quality
Routinely
collected
knowledge
• Clinical trials
• Controlled populations
• Well-defined questions
Actionable
knowledge
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• Distilled scientific
findings
• Usable in clinical
practice
• Decision support
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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:
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Implementation methods
Models for data and process organization
Services
Architectures
Demonstrations with industry
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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:
Research Question:
Effectiveness of continuous
versus on demand PPI use?
Electronic CRF embedded in
the eHR
RCT with event-initiated
patient-related
outcome
measures:
• Privacy model
• Record linkage (browsing,
selecting, extracting)
• Data quality tool
• Provenance tool
• 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
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Overall Architecture (1)
End User Tools and
Services
Middleware (Distributed
Infrastructure)
Support Services
Distributed
Nodes
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Overall Architecture (2)
End User Tools and Services
Study Design
Protocol Designer
Query Workbench
Study Management
GP Decision Support
Identify/Recruit Eligible Patients
Interactive Consultation Decision Support Tool
Eligibility Criteria
Designer
eCRF Designer
Consent Data Manager
Timeline Designer
Study eCRF Data Collector
Query formulation and
execution tool
Patient-reported outcome Manager
CDE Designer
Semantic Mediation
Data Mining and Analysis
Vocabulary Service
Clinical Evidence Extraction Tool
Clinical Evidence Content Manager
Clinical Evidence Service
Data Provenance
Provenance Capture Service
Data Mining Analysis Toolkit
Data quality tool
Event-based trigger tool
Provenance Audit Tool
Visual model explorer
Support Services (e.g. rule based security,
authentication)
Middleware (Distributed Infrastructure)
Distributed Nodes
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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).
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Provenance
• Processing history of a data item
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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
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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
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Architecture
Provenance API
Provenance API
Prov. Query
Service
Capture
DB1 DBn
Prov. Query
Service
Capture
SQL
SPARQL
Representation
Reasoner
CP DB
Local
Servic
e
SQL
SPARQL
Representation
Reas. &
DM
Storage
Metadata
Registry
Storage
MD DB
LP DB
Centralized Zone
Local Zone
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Example: Provenance of a query
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Example: Linkage process
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Example: Decision support
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The challenge of representing knowledge in an
interoperable computable form
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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
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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)
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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
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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)
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TRANSFoRm Integrated
VS Server Architecture
TRANSFoRm Integrated VS Server
Lucene Index
GWT Ajax Web
Client
GWTRPC Servlet
LexEVS Runtime
UMLS
Database
Query Service
Read Codes v2
Database
Web Services
Client
Metro JAX-WS
Web Service
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A screenshot of the web-based interface
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RCD v2/ICPC2
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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.
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The UK NHS Connecting for Health
Terminology Centre - mappings from
Read Codes version 2 to SNOMED CT.
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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.
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ICPC2-ICD10 Thesaurus and mappings Transition Project @ University of
Amsterdam
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The TRANSFoRm team is updating the
ICPC-ICD 10 mapping and Thesaurus
– to eventually incorporate the new
UMLS
Metathesaurus
SNOMED CT
Codes
Read Codes v2
Codes
UMLS
Metathesaurus
ICD-10
Thesaurus/Codes
ICPC2
Codes
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Demonstration of integrating
a current prototype of the
Study Designer and
TRANSFoRm Integrated
Vocabulary Service
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ePCRN Study Designer and TRANSFoRM VS integration
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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
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eCRF Workflow
Protocol
Timeline
CRFs
Design
Study
Study
Database
Collect
Data
Analyse
Data
The following provide some example screenshots of
the eCRF technology on a current prototype
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Protocol Design
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Timeline Design
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CRF Design
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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
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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
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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
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General model of evidence
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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
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Protégé – defining concepts and relations
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Protégé – defining instances of knowledge
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Sesame query platform
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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
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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
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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 workbench that supports the identification of
patient eligible to participate in clinical trials
Support large scale phenotypegenotype 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
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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
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