Mining electronic health records: towards better research applications and clinical

care

Standardising the representation of clinical

information: for patient care and for

researchDipak Kalra

Professor of Health InformaticsUniversity College London

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

Capturing and combining diverse sources of information

Date: 1.7.94

Whittington

Hospital

Healthcare Record

John Smith DoB: 12.5.46

Clinical trials,functional genomics Population health registries

Medical devices,Bio-sensors

Clinical applications

Decision support, knowledge managementand analysis components

Mobile devices

Environmental data

Social computing:forums, wikis and blogs

Integrating information

Centering services on

citizens

Creating and using knowledge

Dipak Kalra

The rich re-use of Electronic Health Records

Point of care delivery

Continuing care (within the institution)

Long-term shared care (regional

national, global)

TeachingResearch

Clinical trials

explicit consent

EducationResearch

EpidemiologyData mining

de-identified

+/- consent

Public healthHealth care

managementClinical audit

implied consent

Citizen in the community

Social careOccupational

healthSchool health

WellnessFitness

Complementary health

rapid bench to bed translation

Disease registriesScreening recall

systems

implied consent

real-time knowledge directed care

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 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

Information models

EHR system reference model openEHREHR interoperability Reference Model ISO/EN 13606-1HL7 Clinical Document ArchitectureClinical content model representation openEHR ISO/EN 13606-2 archetypesISO 21090 Healthcare DatatypesISO EN 12967-2 HISA Information Viewpoint

Interoperability standards relevant to the EHR

Computational servicesEHR Communication Interface Specification ISO/EN 13606-5ISO EN 12967-3 HISA Computational ViewpointHL7 SOA Retrieve, Locate, and Update Service DSTU

Business requirementsISO 18308 EHR Architecture RequirementsHL7 EHR Functional ModelISO EN 13940 Systems for Continuity of CareISO EN 12967-1 HISA Enterprise Viewpoint

SecurityEHR Communication Security ISO/EN 13606-4ISO 22600 Privilege Management and Access ControlISO 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 List of diagnoses and procedures procedures

Procedure Appendicectomy1993

Diagnosis Acute psychosis2003

Diagnosis Meningococcal meningitis1996

Procedure Termination of pregnancy1997

Diagnosis Schizophrenia2006

Can we safely interpret a diagnosis without its context?

Dipak Kalra

Clinical interpretation context

Emergency Department

“They are trying to kill me”

Symptoms

Reason for encounter Brought to ED by family

Mental state exam Hallucinations

Delusions of persecution

Disordered thoughts

Management plan Admission etc.....

Diagnosis Schizophrenia

Working hypothesis Certainty

Seen by junior doctor

Junior doctor,emergency situation,a working hypothesis

soschizophrenia is

not areliable diagnosis

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 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

• ConsentsDipak Kalra

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 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 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

Descriptions,findings,

intentions

Professionalism and accountability

Health Records

Prompts,remindersBio-sciences

Diseases and treatments

Medical Knowledge

Pathologicalprocesses

Evidence ontreatment

effectiveness

Clinical outcomesEpidemiology

Clinical audit

Care plans

Research

These areas need to be represented consistentlyto deliver meaningful and safe interoperability

Dipak Kalra

Rich EHR interoperabilit

y

EHR reference modeldata typesnear-patient device interoperabilityarchetypestemplates

guidelinescare pathwayscontinuity of care

clinical terminology systemsterminology sub-setsvalue sets and micro-vocabulariesterm selection constraintspost-co-ordinationterminology binding to archetypessemantic context modelcategorial structures

architectureidentifiers for peoplepolicy modelsstructural rolesfunctional rolespurposes of usecare settingspseudonymisation

workflow

reco

rd s

tru

ctu

re

an

d c

on

text

privacy

term

inolo

gy

syst

em

s

Consistent representation,

access and interpretation

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 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

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

3

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 safety

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

5

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

CHAPTERCentre for Health service and Academic Partnership in

Translational E-Health ResearchCo-ordinator: Prof Harry Hemingway

Data quality and

Acquisition

Consent & Access

Curation & Sharing

Integration

Linkage

Computational / semi-automated

analysis

Visualisation

Biostatistics

T2: Novel trial delivery

T3: Patient journey quality and outcomes

T4: Supporting decision making for

health gain•Clinician•Patient•Organisation

T1: Omics and phenotyping

CLINICAL RESEARCH PROGRAMMES

Cardiovascular (UCLH BRC, QMUL BRU)Maternal & Child health (GOSH BRC)

Infection (BRC, HPA)Neurodegeneration (UCLH, BRU)

Eyes (Moorfields, BRC)

TRANSLATIONAL CYCLE

INFORMATICS CYCLE

CHAPTER

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

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 involved14 European countries represented48 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 pre-clinical and prodromal phase, with the support of EMIF-Platform– Lead: Prof. Simon Lovestone, King’s College London

EMIF – platform for modular extension

EM

IF -

Met

abo

lic

EM

IF -

AD

Data Privacy

Analytical tools

Semantic Integration

Information standards

Data access / mgmt

IMI Structure and Network

Research Topics

EMIF governance

Pre

ve

nti

on

alg

ori

thm

s

Pre

dic

tiv

e s

cre

en

ing

Ris

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tra

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tio

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Call 5Call 5

Ris

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sis

Pa

tie

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rate

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ata

TBD

EM

IF -

Pla

tfo

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Metabolic CNS

Cross Validation

Source of new epidemiology insights for patient sub-segments

4

Researcher

Browsing through directory of “data fingerprints”

Controlled data access based on usage rights (Private Remote Research Environments)

Com

mon

Dat

a M

odel

Anal

ytica

l too

ls /

met

hods

Cohorts

AD

Cohorts

Metabolics

Principle: EMIF will offer a platform to integrate available data allowing pooled analysis

1

EHR datasetsEHR datasets

Data enrichment

Historic patient data allowing “roll-back” to study trajectories

2

Cohorts

AD

Cohorts

Metabolics

Principle: EHR data enables the search for patients with specific characteristics to form new cohorts.

Patient selection

3

Long-term view

System biology

Biomarker definition

Lead identification

Clinical trial Execution

Market Access

Ongoing safety tracking

incident monitoring &detection

retrieval of similar patient history

outcome analysis

care management patients at risk

re-admission prevention

diagnosis &treatment assistance

Clinical Care Clinical Research