THE CONVERGENCE INITIATIVE TO MAXIMISE THE VALUE FROM EUROPEAN RESEARCH

Transcription

1 THE CONVERGENCE INITIATIVE TO MAXIMISE THE VALUE FROM EUROPEAN RESEARCH Georges De Moor EuroRec, Ghent University Electronic Health Records for Clinical Research 192

2 Growing number of Participating Projects Electronic Health Records for Clinical Research 193

3 The principal driver for the Convergence Initiative is... Electronic Health Records for Clinical Research 194

4 Electronic Health Records for Clinical Research 195

5 The Convergence Initiatives: History First Convergence Workshop: Reykjavik, Iceland (June 3, 2010) (EFMI STC: initiated by DebugIT, Christian Lovis) (EC Support) Second Convergence Workshop: Oslo, Norway (August 28, 2011) (MIE 2011: initiated by DebugIT, Christian Lovis) (EC and IMI support) Third Convergence Workshop: Basel, Switzerland (November 7, 2012) (EHR4CR Annual Conference: initiated by EHR4CR, Georges De Moor) Fourth Convergence Workshop: Brussels, Belgium (March 20, 2013) (EuroRec STC: initiated by EHR4CR and SHN, Dipak Kalra and Georges De Moor) Event during the ehealth week: Athens, Greece (May 15, 2014) (EuroRec/Expand: Dipak Kalra) Electronic Health Records for Clinical Research 196

6 Tendency to reinvent wheels WE INVENT VERY SIMILAR ENABLERS information models, clinical models, message models ontologies, term lists, mappings and translations tools of various kinds service architectures WE STUMBLE OVER VERY SIMILAR DISABLERS privacy protection, governance, data access, data sharing information security data quality WE OVERLOOK VERY SIMILAR OPPORTUNITIES alignment of evaluation metrics synergy of business models to ensure sustainability Electronic Health Records for Clinical Research 197

7 Convergence provides the opportunity to collaborate, sustain Pool cross project expertise Agree on common functional components work from the same terrain map of the ICT landscape share details about what you are working on models, tools, repositories, services, clinical knowledge content, rules, queries... Share requirements and specifications for these Adopt the same standards indeed, adopt standards! Agree to use each other s outputs Welcome solutions that are not invented here!! Agree not to develop something... Divide and conquer large and complex problems together Build sustainable solutions Electronic Health Records for Clinical Research 198

8 Some balances have to be found... forcing a single approach V supporting multiple experimental pathways specific solutions V generic solutions quick fixes V durable solutions rapid prototyping V robust versus co ordinated development the cost of implementing for yourself V the cost of implementing for our community investing in doing V investing in learning basic functional testing V quality assured software the effort of deploying locally V supporting our community competition V co operation... V... co opetition Electronic Health Records for Clinical Research 199

9 Electronic Health Records for Clinical Research 200

10 5 Topic areas 1. Semantic Interoperability (Dipak Kalra) 2. Quality Metrics (Bart Vannieuwenhuyse) 3. Privacy Policies and Requirements (Anne Bahr, Peter Singleton) 4. Privacy Protection Techniques and Security (Brecht Claerhout) 5. Business Modelling (Danielle Dupont, Andreas Schmidt) Electronic Health Records for Clinical Research 201

11 1. Semantic Interoperability (SIOP) Semantic Interoperability clinical information needed to support safe shared care and patient centred care e.g. emergency summaries, chronic disease management, wellness... consistent usage of terms and data structures that are frequently needed for research, aligned with those used in clinical care methods and tools to harmonise heterogeneous representations Metadata common models and descriptors supporting the wide scale understanding of research data sets so that they are correctly (re )used by others sharing access to such data harmonisation between disease registry, population health, cohort study and EHR metadata Electronic Health Records for Clinical Research 202

12 SIOP Initial Convergence Clusters Metadata for registries, public health, clinical care Terminology content sub sets for defined purposes, created by individual projects and shared identifying and describing drugs (models, terminology etc.) Ontology / Semantic Web approaches to semantic harmonisation, usually for querying heterogeneous systems Electronic Health Records for Clinical Research 203

13 Data Quality initial Convergence Clusters Context of data creation meta data Should be made explicit Provenance must be clear medical context clarity on reimbursement and medical practice Clarity on who created the data Mapping to common ontologies Type of use drives selection of data Data should be fit for intended use Care vs Research Options to select data sources on available meta data Electronic Health Records for Clinical Research 204

14 2. Quality Metrics Cf. next presentation from Bart Vannieuwenhuyse Electronic Health Records for Clinical Research 205

15 3. Privacy and Security (joint) Develop and agree common definitions for categories of personal data Define common standards for transforming clinical care data into research data Define de identification level requirements according to consent and risk Share experience of data sharing agreements Electronic Health Records for Clinical Research 206

16 Privacy and Security initial Convergence Clusters Risk based de identification Data sharing agreements Shared inventory of country specific or care setting specific issues and constraints Comparison of solutions used for authentication, access control and audit Electronic Health Records for Clinical Research 207

17 4. Business Modelling Sharing innovative business modelling approaches Securing dedicated expert resources Conducting comprehensive e scan & market analyses Developing business modelling strategic plans Performing customer mapping & segmentation Designing evidence based value propositions Establishing the cost effectiveness of innovation to decision makers & payers Aligning organizational infrastructures for sustainability Building a sound financial scheme under uncertainty Conducting advanced business model simulations Producing comprehensive business plans Carrying out advanced risk assessments for mitigation Electronic Health Records for Clinical Research 208

18 CONVERGENCE: THE DATA QUALITY ISSUE (EMIF PROJECT) Bart Vannieuwenhuyse Janssen Electronic Health Records for Clinical Research 209

19 EMIF consortium Academic partners Patient organisation SME partners EFPIA partners 56 partners 14 European countries represented 56 MM worth of resources (in-kind / in-cash) 3 projects in one 5 year project ( ) To be a trusted European hub for health care data intelligence enabling new insights into diseases and treatments Electronic Health Records for Clinical Research 210

20 Data in consortium: Large variety in types of data Types: Primary care data sets Hospital data Administrative Regional record-linkage systems Registries and cohorts (broad and disease specific) Biobanks Combined more than 52MM subjects from 7 EU countries. Additionally, for AD around 25K subjects from 10 countries and for Metabolic 94K subjects from 10 countries Electronic Health Records for Clinical Research 211

21 EMIF topics and project structure EMIF governance Metabolic Call 5 CNS Call 5 TBD EMIF - Metabolic Patient generated data Risk stratification EMIF - AD Risk factor analysis Prevention algorithms Predictive screening Research Topics EMIF - Platform Data Privacy Analytical tools Semantic Integration Information standards Data access / mgmt IMI Structure and Network Electronic Health Records for Clinical Research 212

22 Quality of data Data quality is the end product of a whole process Q Quality of Solution Quality of Usage Metrics 1 Metrics 2 All elements need to be of the right quality A Rolls Royce with three wheels is not an impressive car Electronic Health Records for Clinical Research 213

23 Challenges with Real World Data (RWD) Data gaps Missing data elements (e.g. outcomes) Studies 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 handovers 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 Electronic Health Records for Clinical Research 214

24 Example of value from RWD RWE of ACHeI 2500 patient years of therapy documented in EHR-system > 8 fold dataset compared to Cochrane Cost effective Text mining derivation of service utilisation and costs. Created in one month Resembles cognitive decline curve derived from clinical trials MMSE score Cholinesterase inhibitors and Alzheimer s disease S. Lovestone et al, not published time(years) Electronic Health Records for Clinical Research 215

25 Leverage between EHR4CR and EMIF = convergence Topic Leverage opportunity Architecture Federated queries and data extraction approaches Semantics Share ontologies and semantic representation models Privacy Ethics Quality labelling and certification of data sources Business model Status in EU countries collaborate around new privacy regulation Share procedures, guidelines, SOP s Adopt developed best practices and collaborate Share methodologies and collaborate Electronic Health Records for Clinical Research 216

26 Aligned and complementary Comprehensive solutions and available data sources to support research Optimal reuse of existing healthcare data across the entire pharmaceutical product life cycle Benefit for care delivery and research better treatments through better insights Electronic Health Records for Clinical Research 217