Mining electronic health records:
towards better research applications and clinical care
Standardising the representation
of clinical information:
for patient care and for research
Dipak Kalra
Professor of Health Informatics
University College London
EHR trends
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Georges De
Moor
Patient-centered (gatekeeper?), life long records
Multi-disciplinary / multi-professional
Transmural, distributed and virtual
Structured and coded (cf. semantic interoperability)
More metadata and coding at a granular level !
Intelligent (cf. decision support), clinical pathways…
Predictive (e.g. genetic data, physiological models)
More sensitive content (privacy protection)
Personalised
Pervasive: bio-sensors, wearables...
Capturing and combining diverse sources of information
Clinical trials,
functional genomics
Population health registries
Decision support,
knowledge management
and analysis components
Integrating
information
Whittington
Hospital
Centering services
on citizens
Healthcare Record
John Smith
DoB : 12.5.46
Environmental data
Medical devices,
Bio-sensors
Dipak Kalra
Creating and using
knowledge
Mobile devices
Clinical
applications
Social computing:
forums, wikis and blogs
The rich re-use of Electronic Health Records
Wellness
Fitness
Complementary health
Citizen in the
community
rapid bench to bed translation
Social care
Occupational health
School health
real-time knowledge directed care
Point of care
delivery
Teaching
Research
Clinical trials
explicit consent
implied consent
Disease registries
Screening recall
systems
Continuing care
(within the institution)
Education
Research
Epidemiology
Data mining
de-identified
implied consent
+/- consent
Long-term shared care
(regional national, global)
Dipak Kalra
Public health
Health care
management
Clinical audit
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
time.
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
Business requirements
Information models
Computational services
ISO 18308 EHR Architecture Requirements
HL7 EHR Functional Model
ISO EN 13940 Systems for Continuity of Care
ISO EN 12967-1 HISA Enterprise Viewpoint
EHR system reference model openEHR
EHR interoperability Reference Model ISO/EN 13606-1
HL7 Clinical Document Architecture
Clinical content model representation openEHR ISO/EN 13606-2
archetypes
ISO 21090 Healthcare Datatypes
ISO EN 12967-2 HISA Information Viewpoint
EHR Communication Interface Specification ISO/EN 13606-5
ISO EN 12967-3 HISA Computational Viewpoint
HL7 SOA Retrieve, Locate, and Update Service DSTU
Security
EHR Communication Security ISO/EN 13606-4
ISO 22600 Privilege Management and Access Control
ISO 14265 Classification of Purposes of Use of Personal Health
Information
Clinical knowledge
Terminologies: SNOMED CT, etc.
Clinical data structures: Archetypes etc.
ISO EN 13606-1 Reference Model
Dipak Kalra
In a generated medical summary
List of diagnoses and procedures
1993
Procedure
Appendicectomy
1996
Diagnosis
Meningococcal meningitis
1997
Procedure
Termination of pregnancy
2003
Diagnosis
Acute psychosis
2006
Diagnosis
Schizophrenia
Can we safely interpret a diagnosis without its context?
Dipak Kalra
Clinical interpretation context
Emergency Department
Reason for encounter
Brought to ED by family
Symptoms
“They are trying
to doctor,
kill me”
Junior
Mental state exam
Diagnosis
Certainty
Management plan
Dipak Kalra
Seen by junior doctor
emergency situation,
Hallucinations
a working hypothesis
so
Delusions schizophrenia
of persecution
is not
a
Disordered
thoughts
reliable
diagnosis
Schizophrenia
Working hypothesis
Admission etc.....
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 references
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 history)
- who provided this information
• Version management
• Access privileges
- which need to be defined in ways that can be interpreted across
organisational and national boundaries
• Consents
Dipak Kalra
Clinical information standards
• Formally model clinical domain concepts
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e.g. “smoking history”, “discharge summary”, “fundoscopy”
• Encapsulate evidence and professional consensus on
how clinical data should be represented
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published and shared within a clinical community, or globally
imported by vendors into EHR system data dictionaries
• Support consistent data capture, adherence to
guidelines
• 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 new
knowledge need to be formally represented 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
Research
Epidemiology
Evidence on
treatment
effectiveness
Medical Knowledge
Bio-sciences
Pathological
processes
Diseases and
treatments
Clinical outcomes
Clinical audit
Care plans
Health Records
Descriptions,
findings,
intentions
Professionalism and
accountability
These areas need to be represented consistently
to deliver meaningful and safe interoperability
Dipak Kalra
Prompts,
reminders
privacy
record structure
and context
Consistent
representation,
Rich EHR
interoperability
access and
interpretation
clinical terminology systems
terminology sub-sets
value sets and micro-vocabularies
term selection constraints
post-co-ordination
terminology binding to archetypes
semantic context model
categorial structures
Dipak Kalra
terminology
systems
architecture
identifiers for people
policy models
structural roles
functional roles
purposes of use
care settings
pseudonymisation
EHR reference model
data types
near-patient device interoperability
archetypes
templates
workflow
guidelines
care pathways
continuity of care
Semantic interoperability resource
priorities
• Widespread and dependable access to maintained
collections of coherent and quality-assured semantic
resources
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clinical models, such as archetypes and templates
rules for decision making and monitoring
workflow logic
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mapped to EHR interoperability standards
bound to well specified multi-lingual terminology value sets
indexed and correlated with each other via ontologies
referenced from modular (re-usable) care pathway components
• SemanticHealthNet will establish good practices in
developing such resources
Dipak Kalra
using practical exemplars in heart failure and coronary prevention
involving major global SDOs, industry and patients
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
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The IMI EHR4CR project runs over 4 years (2011-2014) with a
budget of +16 million €
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10 Pharmaceutical Companies (members of EFPIA)
22 Public Partners (Academia, Hospitals and SMEs)
5 Subcontractors
One of the largest public-private partnerships
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Providing adaptable, reusable and scalable solutions (tools and
services) for reusing data from EHR systems for Clinical
Research
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EHRs offer significant opportunity for the advancement of
medical research, the improvement of healthcare, and the
enhancement of patient safety
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The EHR4CR Scenarios
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Protocol feasibility
Patient identification recruitment
Clinical trial execution
Serious Adverse Event reporting
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across different therapeutic areas
(oncology, inflammatory diseases, neuroscience, diabetes,
cardiovascular diseases etc.)
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across several countries (under different legal frameworks)
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EHR4CR will deliver
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Requirements specification
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for EHR systems to support clinical research
for integrating information across hospitals and countries
Innovative Business Model
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for sustainability
to stimulate the marketplace
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Technical Platform (tools and services)
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Pilots for validating the solutions:
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different scenarios
different therapeutic areas
several countries
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CHAPTER
Centre for Health service and Academic Partnership in
Translational E-Health Research
Co-ordinator: Prof Harry Hemingway
TRANSLATIONAL CYCLE
T1: Omics and
phenotyping
Data quality
and
Acquisition
Consent &
Access
Biostatistics
T4: Supporting
decision making for
health gain
•Clinician
•Patient
•Organisation
CLINICAL RESEARCH
PROGRAMMES
Visualisation
Cardiovascular (UCLH BRC, QMUL BRU)
Maternal & Child health (GOSH BRC)
Infection (BRC, HPA)
Neurodegeneration (UCLH, BRU)
Eyes (Moorfields, BRC)
Curation &
Sharing
INFORMATICS CYCLE
Computational /
semi-automated
analysis
T2: Novel trial
delivery
CHAPTER
Integration
Linkage
T3: Patient journey
quality and
outcomes
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 > 40 million patients
•AD research on 10-times more subjects than ADNI
•Metabolics research on > 20,000 obese & T2DM subjects
•Linkage of clinical and omics data
•Development of a secure (privacy, legal) modular platform
•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
1. 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 Rotterdam
2. EMIF-Metabolic: Identify predictors of metabolic complications in
obesity, with the support of EMIF-Platform
– Lead: Prof. Ulf Smith, University of Gothenburg
3. EMIF-AD: Identify predictors of Alzheimer’s Disease (AD) in the preclinical and prodromal phase, with the support of EMIF-Platform
– Lead: Prof. Simon Lovestone, King’s College London
EMIF – platform for modular extension
EMIF governance
Prevention algorithms
Risk factor analysis
EMIF - AD
Call 5
TBD
Predictive screening
CNS
Call 5
Risk stratification
Patient generated data
Research Topics
EMIF - Metabolic
Metabolic
EMIF - Platform
Data Privacy
Analytical tools
Semantic Integration
Information standards
Data access / mgmt
IMI Structure and Network
Researcher
Browsing through directory of “data fingerprints”
Controlled data access based on usage rights (Private Remote Research Environments)
AD
Metabolics
Metabolics
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Common Data Model
Cohorts
Cohorts
Cohorts
Cohorts
Principle: EMIF will
offer a
platform to integrate available
data allowing pooled analysis
Principle: EHR data enables the
search for patients with specific
characteristics to form new
cohorts.
Data enrichment
Patient selection
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EHR datasets
EHR datasets
Historic patient data
allowing “roll-back” to study
trajectories
Source of new epidemiology
insights for patient subsegments
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Cross Validation
Analytical tools / methods
1
AD
Long-term view
Clinical Care
incident monitoring &
detection
outcome analysis
retrieval of similar
patient history
care management
patients at risk
re-admission prevention
diagnosis &
treatment assistance
Clinical Research
System biology
Lead identification
Biomarker definition
Clinical trial Execution
Market Access
Ongoing safety tracking