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Big Data in Healthcare: Hype and Hope on the Path to Personalized Medicine

As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.

• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health

New York eHealth Collaborative Digital Health Conference
November 18, 2014

Big Data in Healthcare: Hype and Hope on the Path to Personalized Medicine

  1. 1. Big Data in Healthcare: Hype and Hope How can we find the path to precision medicine? Bonnie Feldman, DDS, MBA | www.drbonnie360.com | @DrBonnie360 | drbonnie360@gmail.com
  2. 2. © 2014 - All rights reserved.
  3. 3. Medical Data Owners: Consumers, caretakers Sources: Patients, providers Users: Patients, providers, R&D, payers Examples: vitals, fitness, history © 2014 - All rights reserved. Patient Clinical Financial R&D Owners: Providers, patients Sources: Patients, providers Users: R&D, patients, providers, payers Examples: EMRs, images, Dx, Tx Owners: Payers, Sources: Providers Users: Payers, providers, regulators Examples: claims, cost, payment, utilization Owners: Academics, pharma Sources: Providers, patients Users: Researchers, developers Examples: trials, screening libraries
  4. 4. © 2014 - All rights reserved.
  5. 5. Different Perspectives from: •Andrew Kasarskis Co-director, Icahn Institute for © 2014 - All rights reserved. Genomics and Multiscale Biology •Colin Hill CEO of GNS Healthcare •William King CEO Zephyr Health •Jonathan Hirsch Founder and President Syapse
  6. 6. Icahn Institute GNS Healthcare Zephyr Health Syapse What Integrate Big Data to build models of biology and thus better diagnose, treat + prevent disease © 2014 - All rights reserved. Value based Big data analytics for personalized interventions that deliver better population health Organizes health information that makes it useful and accessible for anyone Precision medicine platform that enables healthcare providers How Aggregation and mining of clinical, preclinical + basic research data, molecular + other profiling tech, EMR and other data sources Value- based analytics that combine economic and clinical models to predict the right interventions targets for best outcomes Integrates health data from thousands of disparate source lets users find insights by viewing data in context Semantic computing based Precision Medicine Platform aggregates genomic, molecular, outcomes and cost data For Whom Patients Providers Health Care Innovators Payers Life Science Companies Commercial team Medical affairs team Providers Oncology Cardiovascular Genomic Medicine
  7. 7. Open Questions •What has worked? •What has not worked? •How is your business model evolving? •Dreams for the future? © 2014 - All rights reserved.
  8. 8. +1.310.666.5312 drbonnie360@gmail.com www.drbonnie360.com @DrBonnie360 Bonnie Feldman DDS, MBA Business Development for Digital Health
  9. 9. Accelerating Intelligent Interventions Colin Hill, CEO & Founder November, 2014 www.gnshealthcare.com
  10. 10. Big Data Analytics Accelerating Intelligent Interventions • Team of 50 (25 PhD’s) • Physicists •Health & Computer scientists •Health Epidemiologists •Health Actuaries •Mathematicians •Statisticians • Founded in 2000 • Cambridge, MA • Solutions for • Payers • Providers • Pharma 10
  11. 11. GNS Healthcare Emerging Data HRA, Labs, Geography EMR Data Consumer Data Pharmacy & Medical Claims GNS REFS™ Platform Individual Characteristics Intervention Economic Outcomes Clinical Outcomes Large & Diverse Data Sets Value-Based Inference Engine Personalized Interventions Value-Based vs. Rules-Based Approach 11
  12. 12. Poor Medication Adherence 12
  13. 13. Value-Based vs. Rules-Based Selection Value based selection precisely matches individuals and maximizes overall ROI Lucy Nora Ethel Age 46 24 66 Drugs of Interest (DOIs) Cardio + Diabetes Cardio + Diabetes Cardio Cardio, Diabetes (oral), Chronic Respiratory Current PDC to DOIs 44% 29% 82% # Unique Pharmacies 2 1 2 Prior Condition-Related Events? Yes No No Event Costs That Could ‘ve Been > $14,000 < $200 Avoided with Increase in PCD 25% Increase 45% Increase > $10,000 10% Increase 13
  14. 14. Meaningful Adherence™ Rules-based Value-based 41,114 Selected individuals 42,856 $ 2.3M Eliminated events $ 3.1M $ 1.6 M Additional Rx costs $ 0.5M $ -13.03 Net savings/participant $ 96.75 (0.7) ROI 2.7 • Rapid Time to Value – Personalized interventions on just the right targets – Optimizing cost savings – Improving clinical results • Revolutionizing Population Health Mgt. 14
  15. 15. Accelerating Intelligent Interventions Colin Hill, CEO & Founder Colin@gnshealthcare.com GNS Healthcare 1 Charles Park Cambridge, MA 02141 www.gnshealthcare.com
  16. 16. Big Data, the Icahn Institute, and the Mount Sinai Health System Andrew Kasarskis NYeC Digital Health Conference November 17, 2014 @IcahnInstitute
  17. 17. Building and Using Realistic Predictive Models of Biology 18
  18. 18. Using the Big Data: Benefits for Patients, Providers, and Research at Mount Sinai BioBank Patient EMR (EPIC) Clinical Labs Sequencing Facility Data Warehouse Traffic Clinical Data Primary Data High-Performance Computing Research and Clinical Queries; Experiment Creation; etc. Actionable Feedback Disease Model Construction and Prediction Generation
  19. 19. Data Science Adds Value Across Constituencies Icahn Institute New Target and Biomarker Discovery Pathogen Surveillance Molecular Epidemiology
  20. 20. Closing Thought Population Sample acquisition Electronic Medical Record Clinical Care & Research Personal Environmental and Social Data Predictive Network Model 21
  21. 21. Enabling Precision Medicine for Healthcare Providers Jonathan Hirsch Founder & President Syapse
  22. 22. Legacy oncology practice “Nuclear bomb” therapies
  23. 23. Precision cancer care “Smart bomb” therapies
  24. 24. Health System’s Challenge Providing rich genetic data and actionable information to physicians while overcoming legacy software infrastructure
  25. 25. Legacy software Best of the 1980s: EMR, PACS, LIS, CPOE, eMAR
  26. 26. Electronic Medical Record Can’t handle complex genomic data No data mining, visualization Built for billing & compliance
  27. 27. The precision medicine workflow… …and barriers to adoption. Clinical workup & Review clinical history Order test Lab generates MDx test report View clinical & MDx data Receive decision support based on guidelines, clinical, molecular data Order therapy or enroll patient in clinical trial Process drug procurement Monitor patient outcome & revise care strategy Track cost & adherence Obtain pre-authorization Molecular Tumor Board reviews clinical & MDx data; delivers guidance to physician Obtain off-label reimbursement authorization Assess health outcomes & modify care pathways data integration No and visualization decision support for MDx test orders pre-authorization support systematic decision support for therapy or clinical trials mechanism for sharing patient records systematic capture of physician decisions & patient outcomes systematic capture of treatment costs systematic update of care pathways No No No No No No No
  28. 28. The precision medicine workflow… …and barriers to adoption. Clinical workup & Review clinical history Order test Lab generates MDx test report View clinical & MDx data Receive decision support based on guidelines, clinical, molecular data Order therapy or enroll patient in clinical trial Process drug procurement Monitor patient outcome & revise care strategy Track cost & adherence Obtain pre-authorization EMR tabs EMR records Paper reports Emails Phone calls XLS, PPT, DOC files Mental steps Molecular Tumor Board reviews clinical & MDx data; delivers guidance to physician Obtain off-label reimbursement authorization Assess health outcomes & modify care pathways data integration No and visualization decision support for MDx test orders pre-authorization support systematic decision support for therapy or clinical trials mechanism for sharing patient records systematic capture of physician decisions & patient outcomes systematic capture of treatment costs systematic update of care pathways No No No No No No No 8 ~50 9 4 5 12 4 Conservative estimate by users
  29. 29. Genomic data: EMR “Import”
  30. 30. A modern-day Tower of Babel No standard schemas No standard terminology Unstructured or semi-structured Thousands of record types Millions of property types
  31. 31. Introducing Syapse: Enterprise software to enable precision medicine Integrate molecular data into clinical workflow Tailor decision support to organization best practices Extend expertise to affiliate network
  32. 32. Data integration Physician Data Ingestion Sequencing & Analytics Sendout Labs PDF Excel PowerPoint Filemaker Pro One-Time Migration Interfaced Systems PACS EMR Data Warehouse LIS CPOE Drug Administration
  33. 33. Oncologist dashboard 5 4 3 2 1 1 Structured clinical data 2 Omics data 3 Drug procurement 4 Longitudinal data 5 Imaging metadata * All data included in this chart is for informational purposes only and does not include actual patient data
  34. 34. Cancer genomics workflow enabled by Syapse Clinical Workup Patient Consent Test Order in EMR Specimen Procurement Sequencing & Processing Filtering Searchable Database Report Delivery Clinical Data Review Molecular Tumor Board Syapse Clinical Decision

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