CRIS and Institutional Repository Integration: Standardising Open Access
Mining electronic health records: towards better research applications and clinical care...
Transcript of 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
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
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tru
ctu
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text
privacy
term
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syst
em
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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
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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)
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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
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EM
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AD
Data Privacy
Analytical tools
Semantic Integration
Information standards
Data access / mgmt
IMI Structure and Network
Research Topics
EMIF governance
Pre
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Pre
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Ris
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Call 5Call 5
Ris
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TBD
EM
IF -
Pla
tfo
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Metabolic CNS
Cross Validation
Source of new epidemiology insights for patient sub-segments
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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
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l too
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met
hods
Cohorts
AD
Cohorts
Metabolics
Principle: EMIF will offer a platform to integrate available data allowing pooled analysis
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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
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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