Using Healthcare Data for Research @ The Hyve - Campus Party 2016
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The document discusses the use of open-source software in healthcare data for biomedical research, emphasizing the importance of collaboration between academia and industry. It outlines the services provided by The Hyve, including software development and data science, and highlights several projects focused on translational research, population health, and personal health data. It also addresses the challenges and opportunities in using health data for secondary research purposes, emphasizing the need for access to diverse and large datasets.
Using Healthcare Data for Research @ The Hyve - Campus Party 2016
- 1. Using healthcare data for biomedical research MAY 27, 2016 Kees van Bochove, CEO & Founder, The Hyve
- 2. Open Source u Source code openly accessible and reusable for everyone u Enables pre-competitive collaboration: both academics and industry can use and enhance it u Transparency: verification (scientific as well as IT security) can be done by anyone, no ‘black box’
- 4. Interdisciplinary team so5ware engineers, data scien1sts, project managers & staff; exper1se in bioinforma1cs, medical informa1cs, so5ware engineering, biosta1s1cs etc. 4
- 5. YoungCB meeting @ The Hyve 5
- 6. New The Hyve offices being built at the Arthur van Schendellaan in Utrecht, The Netherlands 6
- 7. 7 3 Health Data Areas The Hyve is active in u Translational Research Data (‘Clinical & bioinformatics data’) u Population Health Data (‘Real world data’) u Personal Health Data (‘Wearable sensors data’) Example project:
- 9. 9 Center for Translational Molecular Medicine (CTMM) u Public-private consortium u Dedicated to the development of Molecular Diagnostics and Molecular Imaging technologies u Focusing on the translational aspects of molecular medicine. u 120 partners u universities, academic medical centers, medical technology enterprises and chemical and pharmaceutical companies. u Budget 300 M€ u 22 projects / research consortia u TraIT is the Translational Research IT project supporting these projects with a joint IT infrastructure
- 10. 10 TraIT Consortium Growing TraIT project team
- 11. TraIT data workflow Hospital (IT) Translational Research (IT) data domains clinical data imaging data experimental data biobanking integrated data translational analytics workbench HIS PACS LIS Galaxy tranSMART/ cohort explorer R tranSMART/i2b2 datawarehouseCBM-NL OpenClinica NBIA + AIM e.g. PhenotypeDB, Annai Systems e.g. Galaxy, Chipster Samples (IT) P s e u d o n y m i z a t i o n Public Data BIMS
- 12. 12 TranSMART Platform: Scientific Function CLINICAL GENETICS SENSORS IMAGING DATA UNDER STANDING BIOLOGY MEDICINE
- 13. 13 TranSMART Open Source History u February 2012: J&J releases tranSMART as open source on GitHub under GPL v3 u December 2012: CTMM TraIT project decides to use tranSMART as core infrastructure component u January 2013: IMI eTRIKS starts, uses tranSMART as core infrastructure component u February 2013: kickoff of tranSMART Foundation, U. Michigan publishes PostgreSQL port u March 2014: IMI EMIF kickoff, tranSMART is used as data integration component
- 14. Contributors
- 15. Amsterdam, June 2013: tranSMART Workshop Attendees from 10 Pharma companies, 11 University Medical Centers and 12 IT companies http://lanyrd.com/2013/transmart 15 VUmc Sanofi Recombina nt / Deloitte University of Michigan Thomson Reuters Pfizer Astra Zeneca CDISC University of Luxembourg Philips Johnson & Johnson The Hyve 70
- 16. Ann Arbor, Michigan, October 2014: Annual Meeting http://lanyrd.com/2014/transmart 16 130
- 17. Bio IT World, Boston, April 2015 http://bit.ly/1R2N6uz 17 TranSMART wins all the prizes: Best Show Award, Best Practices Award, Best Poster Award
- 18. Amsterdam, October 2015: Annual Meeting http://lanyrd.com/2015/transmart-foundation-annual-meeting/ 18 160
- 19. TranSMART: working with clinical data
- 20. cBioPortal for Cancer Genomics 20 current community a.o.
- 21. Focus: intuitive exploratory analysis of the data
- 23. What Ewan Birney has to say about it … (GA4GH Leiden 2015) 23
- 24. Time To Market: 11 – 18 years 6 NegotiationforReimbursement 27memberStates EMAFiling Pre-ClinicalResearch Closed&OpenInnovation Clinical Trials EMAApprovalforSale HTAApproval Phase 1 Phase 2 Phase 3 5,000 10,000 Compounds 250 Compounds 3 – 6 Years 6 – 7 Years 5 Therapies 1 Therapy 2 – 5 Years Number of Patients/Subjects 20-100 100-500 1000-5000 Regulatory Review Drug Discovery Pre Clinical Testing PhV Monitoring Total Cost: $2 - $4 Billion USD Sources: Drug Discovery and Development: Understanding the R&D Process, www.innovation.org; CBO, Research and Development in the Pharmaceutical Industry, 2006; Forbes, Matthew Herper, “The Truly Staggering Cost Of Inventing New Drugs”, February 10, 2012 Current EU “Patient Journey” is expensive and slow New therapies don’t reach patients until here Phase 4 : $0.6BDrug Development : $2.6B
- 25. Secondary use of health data to enrich research 25 The value of healthcare data for secondary uses in clinical research and development — Gary K. Mallow, Merck, HIMSS 2012 1 2 3 4 5 6 7 8 9 1,000 10,000 100,000 1 million Years #PatientExperiences/Records The “burning platform” for life sciences Pharma-owned highly controlled clinical trials data Clinical practice, patients, payers and providers own the data Product Launch R&D Phase IV Challenge Today, Pharma doesn’t have ready access to this data, yet insights for safety, CER and other areas are within this clinical domain, which includes medical records, pharmacy, labs, claims, radiology etc.
- 26. 26 Clinical Trials vs Observational Studies Clinical Trials Observational Studies Study Design Controlled (hypothesis driven) “Real world” data Sample Size Small (10.000 is large) Large (millions of people) Endpoints Efficacy, safety Effectiveness, economic value Statistics Descriptive statistics (e.g. ANOVA) Epidemiological modeling Cost Expensive Not so expensive Perspective Study population Society in general
- 27. To become the trusted European hub for health care data intelligence, enabling new insights into diseases and treatments EMIF vision 27 Discover Assess Reuse
- 28. Data available through EMIF consortium § Large variety in “types” of data § Data is available from more than 53 million subjects from seven EU countries, including Primary care data sets Hospital data Administrative data Regional record- linkage systems Registries and cohorts (broad and disease specific) Biobanks >25,000 subjects in AD cohorts >90,000 subjects in metabolic cohorts
- 29. >40million MAAS SDR EGCUT PEDIANET SCTS IMASIS HSD AUH IPCI ARS SIDIAP PHARMO THIN 100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000 Approximatetotal(cumulative)numberofsubjects Available data sources in EMIF 29 EMIF-Platform EMIF-Available Data Sources; EXAMPLES 1K 2K 52K 400K 475K 2.8M 2.3M 10M Status Jan 2016 3.6M 1.6M 1M 12M 6M
- 30. 30 OMOP & OHDSI - Overview u OMOP: Common Data Model for observational healthcare data: persons, drugs, procedures, devices, conditions etc. u OHDSI: Large-scale analytics tools for observational data An open source community, a.o. developing: u Tools to support the ETL / mapping process into OMOP (White Rabbit etc.) u Tools to perform analytics: e.g. Achilles for data profiling, Calypso for feasibility assessment www.omop.org www.ohdsi.org
- 31. 31 OMOP Common Data Model v5.0 v OMOP = Observational Medical Outcomes Partnership v CDM = Common Data Model v SQL Tables
- 32. 32 OHDSI: Example Query Result Slide from P. Ryan, Janssen
- 33. Re-use of healthcare data 33 Prof. Johan van der Lei Erasmus MC University Medical Center “We need to learn from experience and find ways to unite the large volumes of data in Europe. At the end of the day, we are in this for better health care.” Co-coordinator EMIF-Platform EMIF-Platform
- 35. 35 Challenges in managing chronic diseases u Health assessments via physician visits are time limited and subjective, often not representative for everyday life of the patient u The disease state can change a lot in between visits, and important events are not visible to the physician But… it’s 2016: it’s now possible to objectively, remotely, and continuously measure aspects of patient physiology, behavior and symptoms
- 36. 36 RADAR-CNS: Focus areas from diagnose & treat à predict & pre-empt u Epilepsy u Monitoring and predicting epileptic seizures u Multiple Sclerosis u Monitoring exacerbations and disease state u Depression u Monitoring for possible relapses, plan timely interventions u Predict bipolar state transitions