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EPAD 2017 - James N'Dow

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What can be achieved by using Big Data in PCa
James N’Dow, Chairman EAU Guidelines Office

Published in: Health & Medicine
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EPAD 2017 - James N'Dow

  1. 1. What can be achieved by using Big Data in Prostate Cancer Prof. Dr. James N’Dow European Association of Urology
  2. 2. www.uroweb.org/guidelines #eauguidelines EAU Guidelines Endorsed by >60 national societies
  3. 3. Major Prostate Cancer challenges 90% of what is published in the scientific literature is unreliable and unfit to trigger a change of Clinical Practice Guideline Recommendation
  4. 4. Major Prostate Cancer challenges • Insufficient knowledge on patient characteristics. • Poor definition and lack of standardisation of Prostate Cancer-related outcomes. • Lack of standardised “care pathways” across different European geographies. • Inability to incorporate real world clinical outcomes data into the management of Prostate Cancer (screening, diagnosis & treatment).
  5. 5. CLINICAL DATA DATA STEWARD ANALYTICAL ENVIRONMENT STORAGE OFANALYSISRESULTS LARGE-SCALE PARALLEL COMPUTATION MULTI-PLATFORMANALYSISPIPELINES tranSMART ANALYSIS USER SPACE ‘OMICSDATA STUDYDESIGN& DEMOGRAPHICS HEALTH RECORDS HARMONIZED DATASETS GPU DATA ANALYST KCL, UCL, Marsden EORTC Erasmus / / Nijmegen Martini-Klinik/ NCT/ Dresden/Fraunhofer EU partners bringingbigdata Epidemiology datasets VA Dataset CaPSURE Non-EU partnerswith data sources HARMONISED DATA STORE DATA STANDARDS & ONTOLOGY MANAGEMENT DATA EXPLORATION DATA REDUCTION DATA INTEGRATION DATAACCESSENDPOINTS STANDARDS REGISTRY STANDARDTEMPLATES STANDARDVOCABULARIES ETLTOOLS i2b2 model OMOP model DATA CUSTODIAN ENVIRONMENT DATA ACCESS ANDSOURCES DATA PLATFORM DATA ANALYTICS UNISR/ St Raphaele UTA Lund University Goeteborgs University DATA SOURCES DATA TYPES DATA HARMONISATION DATA ANALYSIS PIONEER’S OUTCOMES IMPLEMENTOMICS Consensusonthemost importantPcaoutcomes Identificationofcritical evidencegapsinPCa Standardisationof outcomedefinitionand outcomemeasures Newinsightson improvedstratification Improvedstandardized carepathwayswith knownbetter predictableoutcomes ERSPC group Is big data the answer? Our model
  6. 6. Is big data the answer? Our model
  7. 7. Key elements to success of our model • Access to large numbers of clinical data sets across the different stages of Prostate Cancer and across different European and non-European geographies. • A standardised, open-access, collaborative data platform. • Novel informatics and computational approaches. • Involvement of key opinion leaders in Prostate Cancer and Big Data. • Involvement of all relevant stakeholders including patients.
  8. 8. Encouraging social action • Encouraging widespread adoption of data donation faces significant hurdles due to understandable concerns around privacy and security. • It is possible to anonymise data but many of the most transformative uses of big data in healthcare don’t allow for the data to be anonymised e.g. there’s no point in identifying the perfect clinical trial participant if you can’t contact them.
  9. 9. Encouraging social action • Key to overcoming the hurdle of patient consent is education (opt-out rather than opt-in). • The big data industry must engage with patients and explain the benefits to them and to others of sharing their data. Take home message: Our data can drive healthcare innovation and save lives, but we must be willing to share it.
  10. 10. Thank you for your attention

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