TRANSFoRm: Vision of a learning healthcare system - PowerPoint PPT Presentation


PPT – TRANSFoRm: Vision of a learning healthcare system PowerPoint presentation | free to download - id: 71e4aa-ZGU5M


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation

TRANSFoRm: Vision of a learning healthcare system


Translational Research and Patient Safety in Europe TRANSFoRm: Vision of a learning healthcare system – PowerPoint PPT presentation

Number of Views:112
Avg rating:3.0/5.0
Slides: 40
Provided by: Derek199
Learn more at:


Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: TRANSFoRm: Vision of a learning healthcare system

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)

TRANSFoRm Consortium
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

Aims of TRANSFoRm
  • To develop the infrastructure in primary care to
  • 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

TRANSFoRm Use Cases
Type 2 Diabetes
Decision Support
  • Research Question
  • Effectiveness of continuous versus on demand PPI
  • Electronic CRF embedded in the eHR
  • RCT with event-initiated patient-related outcome
  • Trigger within EHR
  • Semantic Mediator
  • eCRF tool (embedded in EHR)
  • Research Question
  • Are well selected SNPs in T2D patients associated
    with variations in drug response to oral
  • Genotype-Phenotype record linkage study
  • Privacy model
  • Record linkage (browsing, selecting, extracting)
  • Data quality tool
  • Provenance tool
  • 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

Overall Architecture (1)
Overall Architecture (2)
End User Tools and Services
Study Design
Query Workbench
Study Management
GP Decision Support
Eligibility Criteria Designer
Identify/Recruit Eligible Patients
Interactive Consultation Decision Support Tool
Protocol Designer
Consent Data Manager
eCRF Designer
Study eCRF Data Collector
Timeline Designer
Query formulation and execution tool
Patient-reported outcome Manager
CDE Designer
Clinical Evidence Service
Data Mining and Analysis
Data Provenance
Semantic Mediation
Vocabulary Service
Clinical Evidence Extraction Tool
Provenance Capture Service
Provenance Audit Tool
Data quality tool
Support Services (e.g. rule based security,
Middleware (Distributed Infrastructure)
Distributed Nodes
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).

  • 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
  • Information systems tracking data evolution

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

Example Provenance of a query
Example Linkage process
Example 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)
  • 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)

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
  • 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
  • Evolving technologies and APIs for EVS
  • LexEVSAPI various versions from 3.0 to 5.0

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
  • the service uses direct Java Database
    Connectivity (JDBC) to access other vocabulary
    databases (e.g. Read Codes V2, ICPC2)

TRANSFoRm Integrated VS Server Architecture
A screenshot of the web-based interface
  • 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
  • The UK NHS Connecting for Health Terminology
    Centre - mappings from Read Codes version 2 to
  • 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 _at_ University of Amsterdam
  • The TRANSFoRm team is updating the ICPC-ICD 10
    mapping and Thesaurus
  • to eventually incorporate the new Thesaurus into

UMLS Metathesaurus
Read Codes v2 Codes
UMLS Metathesaurus
ICD-10 Thesaurus/Codes
ICPC2 Codes
Demonstration of integrating a current prototype
of the Study Designer and TRANSFoRm Integrated
Vocabulary Service
ePCRN Study Designer and TRANSFoRM VS integration
eCRF Introduction
  • eCRF is electronic version of case report form
  • 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

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
Protocol Design
Timeline Design
CRF Design
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
  • Alerts/suggests for potential missed diagnoses

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

  • 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

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

Protégé defining concepts and relations
Protégé defining instances of knowledge
Sesame query platform
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

TRANSFoRm Vision
  • TRANSFoRm will drive the integration of clinical
    research and clinical practice by developing
    tools services to facilitate greater system

TRANSFoRm Tools Services
Pharmaceutical Companies
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
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... 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 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
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
Contact details
  • 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