1. Professor of Health Informatics University College London
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

2. EHR trends 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...
Georges De Moor

3. Capturing and combining diverse sources of information
Clinical trials, functional genomics
Population health registries
Medical devices,
Bio-sensors
Clinical applications
Decision support,
knowledge management
and analysis components
Mobile devices
Environmental data
Social computing:
forums, wikis and blogs
Integrating information
Centering services on citizens
Creating and using knowledge
Date:
Whittington
Hospital
Healthcare Record
John Smith
DoB :
Dipak Kalra

4. Towards integrated health
Biosensors
Genomic data
Environmental
Data
Phenomic data
Integrated Electronic Health Records
Georges De Moor

5. The rich re-use of Electronic Health Records
Wellness
Fitness
Complementary health
Citizen in the community
Social care
Occupational health
School health
rapid bench to bed translation
real-time knowledge directed care
Point of care
delivery
Teaching
Research
Clinical trials
explicit consent
Disease registries
Screening recall systems
implied consent
Continuing care
(within the institution)
Education
Research
Epidemiology
Data mining
de-identified
+/- consent
Public health
Health care management
Clinical audit
implied consent
Long-term shared care (regional national, global)
Dipak Kalra

6. 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

7. Interoperability standards relevant to the EHR
Business requirements
ISO EHR Architecture Requirements
HL7 EHR Functional Model
ISO EN Systems for Continuity of Care
ISO EN HISA Enterprise Viewpoint
Information models
EHR system reference model openEHR
EHR interoperability Reference Model ISO/EN
HL7 Clinical Document Architecture
Clinical content model representation openEHR ISO/EN archetypes
ISO Healthcare Datatypes
ISO EN HISA Information Viewpoint
Computational services
EHR Communication Interface Specification ISO/EN
ISO EN HISA Computational Viewpoint
HL7 SOA Retrieve, Locate, and Update Service DSTU
Security
EHR Communication Security ISO/EN
ISO Privilege Management and Access Control
ISO Classification of Purposes of Use of Personal Health Information
Clinical knowledge
Terminologies: SNOMED CT, etc.
Clinical data structures: Archetypes etc.

8. ISO EN 13606-1 Reference Model
Dipak Kalra

9. Contextual building blocks of the EHR
EHR Extract
Part or all of the electronic health record for one person, being communicated
Folders
High-level organisation of the EHR e.g. per episode, per clinical speciality
Compositions
Set of entries comprising a clinical care session or document e.g. test result, letter
Sections
Headings reflecting the flow of information gathering, or organising data for readability
Entries
Clinical “statements” about Observations,
Evaluations, and Instructions
Clusters
Multipart entries, tables,time series, e.g. test batteries, blood pressure, blood count
Elements
Element entries: leaf nodes with values e.g. reason for encounter, body weight
Data values
Date types for instance values e.g. coded terms, measurements with units
Dipak Kalra

10. Can we safely interpret a diagnosis without its context?
In a generated medical summary
List of diagnoses and procedures
Procedure
Appendicectomy
1993
Diagnosis
Meningococcal meningitis
1996
Procedure
Termination of pregnancy
1997
Diagnosis
Acute psychosis
2003
Diagnosis
Schizophrenia
2006
Can we safely interpret a diagnosis without its context?
Dipak Kalra

11. Clinical interpretation context
Emergency Department
Seen by junior doctor
Reason for encounter
Brought to ED by family
Junior doctor,
emergency situation,
a working hypothesis so
schizophrenia is not a
reliable diagnosis
“They are trying to kill me”
Symptoms
Mental state exam
Hallucinations
Delusions of persecution
Disordered thoughts
Diagnosis
Schizophrenia
Working hypothesis
Certainty
Management plan
Admission etc.....
Dipak Kalra

12. 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

13. 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

14. Clinical information standards
Formally model clinical domain concepts
e.g. “smoking history”, “discharge summary”, “fundoscopy”
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 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 )
Dipak Kalra

15. Example archetype for adverse reaction
Dipak Kalra

16. openEHR Clinical Knowledge Manager

17. Using archetypes for querying EHR repositories
Dipak Kalra

18. 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

19. 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

20. EHR and knowledge integration
Evidence on treatment effectiveness
Clinical outcomes
Epidemiology
Clinical audit
Care plans
Research
Bio-sciences
Diseases and treatments
Medical Knowledge
Pathological processes
Descriptions,
findings,
intentions
Professionalism and accountability
Health Records
Prompts,
reminders
These areas need to be represented consistently
to deliver meaningful and safe interoperability
Dipak Kalra

21. Consistent representation, access and interpretation
record structure and context
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 interpretation
privacy
workflow
Rich EHR interoperability
guidelines
care pathways
continuity of care
terminology systems
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

22. 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

23. 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 ontologies
referenced from modular (re-usable) care pathway components
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

24. 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

25. Electronic Health Records for Clinical Research
The IMI EHR4CR project runs over 4 years ( ) 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 safety 3

26. 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 frameworks) 9

27. EHR4CR will deliver Requirements specification
for EHR systems to support clinical research
for integrating information across hospitals and countries
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 5

28. CHAPTER
Centre for Health service and Academic Partnership in Translational E-Health Research
Co-ordinator: Prof Harry Hemingway

29. TRANSLATIONAL CYCLE CLINICAL RESEARCH PROGRAMMES INFORMATICS CYCLE
T4: Supporting decision making for health gain
Clinician
Patient
Organisation
T1: Omics and phenotyping
Data quality and Acquisition
Consent & Access
Curation & Sharing
Integration
Linkage
Computational / semi-automated analysis
Visualisation
Biostatistics
CLINICAL RESEARCH
PROGRAMMES
Cardiovascular (UCLH BRC, QMUL BRU)
Maternal & Child health (GOSH BRC)
Infection (BRC, HPA)
Neurodegeneration (UCLH, BRU)
Eyes (Moorfields, BRC)
INFORMATICS CYCLE
T3: Patient journey quality and outcomes
T2: Novel trial delivery
CHAPTER

30. New UCLP Informatics Platform
Beneficiaries
Researchers
Clinicians and Policy Makers
Industry Partners
Public
CHAPTER portal: interface to beneficiaries
University
CHAPTER platform: harmonized datasets and distributed analyses
We will share and link rich and diverse clinical data in GP practices, hospital informations systems etc in secure data warehouses, within the NHS firewall (red line)
In the University, CHAPTER will harmonise data and analyses, distributed where necessary.
What grows out of this soil? Our portal the green top layer is the interface with beneficiaries, allowing researchers, policy makers, industry partners and the public to engage.
Secure Data Warehouse in NHS Trusted Party
CHAPTER harmonizes consent, linkage, data sharing, anonymization, IG GP
Primary
Care
Hospitals
Community
Organizations
Personal Health Records
NHS
CHAPTER

31. 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

32. 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

33. Think Big Co-ordinator Janssen 60 partners (3 consortia + Efpia)
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”

34. 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 Rotterdam
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 Alzheimer’s Disease (AD) in the pre- clinical and prodromal phase, with the support of EMIF-Platform
Lead: Prof. Simon Lovestone, King’s College London

35. EMIF – platform for modular extension
EMIF governance
Metabolic
Prevention algorithms
Predictive screening
Risk stratification
Call 5
Risk factor analysis
Patient generated data
TBD
CNS
Research Topics
EMIF - Metabolic
EMIF - AD
Data Privacy
Analytical tools
Semantic Integration
EMIF - Platform
Information standards
Data access / mgmt
IMI Structure and Network

36. 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.

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

38. 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 groups).
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

39. Analytical tools / methods
Researcher
Browsing through directory of “data fingerprints”
Controlled data access based on usage rights (Private Remote Research Environments)
Cohorts AD
Metabolics
Principle: EMIF will offer a platform to integrate available data allowing pooled analysis 1
Cohorts AD
Metabolics
Principle: EHR data enables the search for patients with specific characteristics to form new cohorts.
Patient selection 3
Cross Validation
Source of new epidemiology insights for patient sub-segments 4
Common Data Model
Analytical tools / methods
EHR datasets
Data enrichment
Historic patient data allowing “roll-back” to study trajectories 2

40. Challenges with re-use of patient level data
Data gaps
Missing data elements (e.g. outcomes)
RCT’s require details that may not be routinely collected
Coding often only at first level (e.g. ICD-9) therefore missing granularity
80% of info stored as unstructured data
Data quality
Longitudinal coherence
Coding for administrative reasons (up – down coding)
Coding often months after patient encounter
Data provenance – who entered the data?
“Semantics”
Many standards – many versions
Complex care – many HCP’s involved – many hand-overs
Need to pool data cross sites and cross different countries
Pharma focused on CDISC
Privacy
Clearly a top priority
Different interpretations by country, by region – complex
TRUST

41. Long-term view Clinical Care Clinical Research
incident monitoring & detection
retrieval of similar patient history
outcome analysis
care management patients at risk
re-admission prevention
diagnosis & treatment assistance
System biology
Biomarker definition
Lead identification
Clinical trial Execution
Market Access
Ongoing safety tracking