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Mining electronic health records: towards better research applications and clinical care


Mining electronic health records: towards better research applications and clinical care Standardising the representation of clinical information: – PowerPoint PPT presentation

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Title: Mining electronic health records: towards better research applications and clinical care

Mining electronic health records towards better
research applications and clinical
care Standardising the representation of
clinical information for patient care and for
Dipak Kalra Professor of Health
Informatics University College London
EHR trends
  • Patient-centered (gatekeeper?), life long records
  • Multi-disciplinary / multi-professional
  • Transmural, distributed and virtual
  • Structured and coded (cf. semantic
  • More metadata and coding at a granular level !
  • Intelligent (cf. decision support), clinical
  • Predictive (e.g. genetic data, physiological
  • More sensitive content (privacy protection)
  • Personalised
  • Pervasive bio-sensors, wearables...

Georges De Moor
Capturing and combining diverse sources of
Dipak Kalra
Towards integrated health
Genomic data
Environmental Data
Phenomic data
Integrated Electronic Health Records
Georges De Moor
The rich re-use of Electronic Health Records
Dipak Kalra
Requirements the EHR must meet ISO 18308
The EHR shall preserve any explicitly defined
relationships between different parts of the
record, such as links between treatments and
subsequent complications and outcomes.
The EHR shall preserve the original data values
within an EHR entry including code systems and
measurement units used at the time the data were
originally committed to an EHR system.
The EHR shall be able to include the values of
reference ranges used to interpret particular
data values.
The EHR shall be able to represent or reference
the calculations, and/or formula(e) by which data
have been derived.
The EHR architecture shall enable the retrieval
of part or all of the information in the EHR that
was present at any particular historic date and
The EHR shall enable the maintenance of an audit
trail of the creation of, amendment of, and
access to health record entries.
Dipak Kalra
Interoperability standards relevant to the EHR
ISO EN 13606-1 Reference Model
Dipak Kalra
Contextual building blocks of the EHR
Part or all of the electronic health record for
one person, being communicated
EHR Extract
High-level organisation of the EHRe.g. per
episode, per clinical speciality
Set of entries comprising a clinical care
session or document e.g. test result, letter
Headings reflecting the flow of information
gathering, or organising data for readability
Clinical statements about Observations, Evaluati
ons, and Instructions
Multipart entries, tables,time series,e.g. test
batteries, blood pressure, blood count
Element entries leaf nodes with valuese.g.
reason for encounter, body weight
Data values
Date types for instance values e.g. coded terms,
measurements with units
Dipak Kalra
In a generated medical summary
List of diagnoses and procedures
Dipak Kalra
Clinical interpretation context
Dipak Kalra
Examples of clinical interpretation context
  • within the overall clinical story
  • past, present
  • intended treatments, planned procedures
  • clinical circumstances of an observation
  • e.g. standing, fasting
  • presence / absence / certainty of the finding
  • hypotheses, concerns
  • a diagnosis for a relative
  • but not the patient!
  • confidence and evidence
  • seniority of the author
  • justification, clinical reasoning, guideline

Dipak Kalra
Examples of medico-legal context
  • Authorship, responsibilities, signatories
  • Dates and times
  • occurrence, clinical encounter, recording,
    schedules, intentions
  • Information subjects
  • whose record is this? (who is the patient?)
  • about whom is this observation? (e.g. family
  • who provided this information
  • Version management
  • Access privileges
  • which need to be defined in ways that can be
    interpreted across organisational and national
  • Consents

Dipak Kalra
Clinical information standards
  • Formally model clinical domain concepts
  • e.g. smoking history, discharge summary,
  • Encapsulate evidence and professional consensus
    on how clinical data should be represented
  • published and shared within a clinical community,
    or globally
  • imported by vendors into EHR system data
  • Support consistent data capture, adherence to
  • Enable use of longitudinal EHRs for individuals
    and populations
  • Define a systematic EHR target for queries for
    decision support and for research

Archetypes (openEHR and ISO 13606-2)
Dipak Kalra
Example archetype for adverse reaction
Dipak Kalra
openEHR Clinical Knowledge Manager
Using archetypes for querying EHR repositories
Dipak Kalra
Example clinical questions
  • Find the age and gender of patients who have been
    diagnosed with Hodgkin's disease, where the
    initial diagnosis occurred between the ages 50
    and 70 inclusive
  • What is the percentage of patients diagnosed with
    primary breast cancer in the age range 30 to 70
    who were surgically treated and had post
    operative haematoma/seroma?
  • What percentage of patients with primary breast
    cancer who relapsed had the relapse within 5
    years of surgery?
  • What is the average survival of patients with
    Chronic Myeloid Leukaemia (CML) and both with and
    without splenomegaly at diagnosis?

Dipak Kalra
Semantic interoperability
  • New generation personalised medicine
    underpinned by -omics sciences
    and translational research needs to integrate data
     from multiple EHR systems with data from
    fundamental biomedical research, clinical and
    public health research and clinical trials
  • Clinical data that are shared, exchanged
    and linked to newknowledge need to be formally re
    presented to become machine processable. 
  • This is more than just adopting existing standards
     or profiles, it is mapping clinical content
    to a commonly understood meaning
  • One can exchange in a perfectly standardised
    message complete meaningless information, hence
    the importance of content-related quality
    criteria (clinically meaningful) and of true
    semantic interoperability

Dipak Kalra
EHR and knowledge integration
These areas need to be represented
consistently to deliver meaningful and safe
Dipak Kalra
EHR reference model data types near-patient
device interoperability archetypes templates
architecture identifiers for people policy
models structural roles functional roles purposes
of use care settings pseudonymisation
Consistent representation, access and
Rich EHR interoperability
guidelines care pathways continuity of care
clinical terminology systems terminology
sub-sets value sets and micro-vocabularies term
selection constraints post-co-ordination terminolo
gy binding to archetypes semantic context
model categorial structures
Dipak Kalra
ARGOS semantic interoperability recommendations
  • Nine strategic actions that now need to be
    championed,as a global mission
  • 1. Establish good practice
  • 2. Scale up semantic resource development
  • 3. Support translations
  • 4. Track key technologies
  • 5. Align and harmonise standardisation efforts
  • 6. Support education
  • 7. Assure quality
  • 8. Design for sustainability
  • 9. Strengthen leadership and governance

Dipak Kalra
Semantic interoperability resource priorities
  • Widespread and dependable access to maintained
    collections of coherent and quality-assured
    semantic resources
  • clinical models, such as archetypes and templates
  • rules for decision making and monitoring
  • workflow logic
  • which are
  • mapped to EHR interoperability standards
  • bound to well specified multi-lingual terminology
    value sets
  • indexed and correlated with each other via
  • referenced from modular (re-usable) care pathway
  • SemanticHealthNet will establish good practices
    in developing such resources
  • using practical exemplars in heart failure and
    coronary prevention
  • involving major global SDOs, industry and patients

Dipak Kalra
Accelerating and leveraging knowledge discovery
  • We need to accelerate the discovery of new
    knowledge from large populations of existing
    health records
  • EHRs can provide population prevalence data and
    fine grained co-morbidity data to optimise a
    research protocol, and help identify candidates
    to recruit
  • almost half of all pharma Phase III trial delays
    are due to recruitment problems

Dipak Kalra
Electronic Health Records for Clinical Research
  • The IMI EHR4CR project runs over 4 years
    (2011-2014) with a budget of 16 million
  • 10 Pharmaceutical Companies (members of EFPIA)
  • 22 Public Partners (Academia, Hospitals and SMEs)
  • 5 Subcontractors
  • One of the largest public-private partnerships
  • Providing adaptable, reusable and scalable
    solutions (tools and services) for reusing data
    from EHR systems for Clinical Research
  • EHRs offer significant opportunity for the
    advancement of medical research, the improvement
    of healthcare, and the enhancement of patient

The EHR4CR Scenarios
  • Protocol feasibility
  • Patient identification recruitment
  • Clinical trial execution
  • Serious Adverse Event reporting
  • across different therapeutic areas (oncology,
    inflammatory diseases, neuroscience, diabetes,
    cardiovascular diseases etc.)
  • across several countries (under different legal

EHR4CR will deliver
  • Requirements specification
  • for EHR systems to support clinical research
  • for integrating information across hospitals and
  • Innovative Business Model
  • for sustainability
  • to stimulate the marketplace
  • Technical Platform (tools and services)
  • Pilots for validating the solutions
  • different scenarios
  • different therapeutic areas
  • several countries

CHAPTER Centre for Health service and Academic
Partnership in Translational E-Health
Research Co-ordinator Prof Harry Hemingway
(UCLH BRC, QMUL BRU) Maternal Child health
(GOSH BRC) Infection (BRC, HPA) Neurodegeneration
(UCLH, BRU) Eyes (Moorfields, BRC)
New UCLP Informatics Platform
CHAPTER portal interface to beneficiaries
Secure Data Warehouse in NHS Trusted
Party CHAPTER harmonizes consent, linkage, data
sharing, anonymization, IG
The IMI is a unique Public-Private Partnership
(PPP) between the pharmaceutical industry
represented by the European Federation of
Pharmaceutical Industries and Associations
(EFPIA) and the European Union represented by the
European Commission
EMIF Project Vision
To enable and conduct novel research into human
health by utilising human health data at an
unprecedented scale
  • Think Big
  • Access to information on gt 40 million patients
  • AD research on 10-times more subjects than ADNI
  • Metabolics research on gt 20,000 obese T2DM
  • Linkage of clinical and omics data
  • Development of a secure (privacy, legal) modular
  • Continue to build a network of data sources and
    relevant research

Think Big
  • Co-ordinator Janssen
  • Bart Vannieuwenhuyse
  • 60 partners (3 consortia Efpia)
  • 170 individuals involved
  • 14 European countries represented
  • 48 MM worth of resources (in-kind / in-cash)
  • 3 projects in one

Project objectives
  • EMIF one project three topics
  • EMIF-Platform Develop a framework for
    evaluating, enhancing and providing access to
    human health data across Europe, to support the
    two specific topics below as well as research
    using human health data in general
  • Lead Prof. Johan van der Lei, Erasmus University
  • EMIF-Metabolic Identify predictors of
    metabolic complications in obesity, with the
    support of EMIF-Platform
  • Lead Prof. Ulf Smith, University of Gothenburg
  • EMIF-AD Identify predictors of Alzheimers
    Disease (AD) in the pre-clinical and prodromal
    phase, with the support of EMIF-Platform
  • Lead Prof. Simon Lovestone, Kings College London

EMIF platform for modular extension
EMIF governance
Research Topics
EMIF - Metabolic
Data Privacy
Analytical tools
EMIF - Platform
Semantic Integration
Information standards
Data access / mgmt
Key objectives EMIF-Metabolic
  • A detailed understanding of the inter-individual
    variability in susceptibility to specific
    metabolic complications of obesity (i.e.
    diabetes, dyslipidemia, and liver steatosis and
    cancers) and the specific effects of the
    different constitutional, environmental and
    obesity-specific factors.
  • The identification of novel susceptibility
    markers for metabolic complications of obesity
    genetic, epigenetic and omics platforms
  • The identification and characterization of
    high-risk individuals for targeted interventions.
  • The development of an algorithm leading to a
    diagnostic test that would predict high risk for
    the metabolic complications of obesity.
  • The identification of novel targets or pathways
    for future therapeutic interventions.

Key objectives EMIF-AD
  1. Collection of data required for the development
    and validation of new biomarkers for AD
  2. Characterisation of study population and
    definition of extreme phenotypes
  3. Discovery of new biomarkers for the diagnosis and
    prognosis of predementia AD
  4. Validation of new biomarkers and development of
    strategies for selection of subjects in AD
    prevention trials

Key objectives EMIF-Platform
  • Access to harmonised data
  • Access to harmonised patient medical information
    from different data sources across Europe
  • comprehensive health data comprising clinical,
    biomarker and other detailed health information
    on a number of populations and specific cohorts
    (pediatrics, adults, including vulnerable
  • Governance
  • Procedures and SOPs that govern access and
    utilisation of patient level data
  • Robust measures to enable linkage and sharing
    whilst preserving privacy
  • Tools
  • Solutions in the areas of data privacy and
    ethics, standards and semantic interoperability
  • patient health data linkage and access to a
    combined patient health information base
  • Business Model
  • That governs the use of the project output as
    well as the support for future research projects

Browsing through directory of data fingerprints
Controlled data access based on usage rights
(Private Remote Research Environments)
Common Data Model
Analytical tools / methods
Challenges with re-use of patient level data
Long-term view
Clinical Care
Clinical Research
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