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

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 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: 1.7.94 Whittington Hospital Healthcare Record John Smith DoB : 12.5.46 Dipak Kalra

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

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

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 ISO 18308 EHR Architecture Requirements HL7 EHR Functional Model ISO EN 13940 Systems for Continuity of Care ISO EN 12967-1 HISA Enterprise Viewpoint Information models 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 Computational services 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

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

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

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

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 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 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 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

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

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 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

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 3

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

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

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

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

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

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 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”

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

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

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 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

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

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

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

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