Using Big Data to Advance Healthcare Gregory J. Moore MD, PhD February 4, 2014

Transcription

1 Using Big Data to Advance Healthcare Gregory J. Moore MD, PhD February 4, 2014

2 Sequencing Technology - Hype Cycle (Gartner) Gartner - Hype Cycle for Healthcare Provider Applications, Analytics and Systems, 2013 Analyst: Vi Shaffer 2 All-digital workflow

3 Provider Time and Big Data: a caution and an opportunity Hours per day needed by a family practitioner to provide all acute, preventive and chronic disease care to a panel of 2500 patients: hours per day Am J Public Health April; 93(4): Ann Fam Med May 1, 2005 vol. 3 no

4 Big data has the potential to overload both providers and patients 4

5 Geisinger Institute for Advanced Application National Laboratory For Healthcare Transformation: Leading through continual innovation 3 Centers, 9 Labs, Informatics and Technology Trials Office, High Performance Computing Core Novel data streams and big data analytics Re-engineering Emerging technology and Informatics Rapid clinical innovation Remote assessment/diagnostics/treatment Transforming patient care Engaging patients Improving the healthcare value proposition Center for Clinical Innovation Center for Emerging Technology and Informatics Center for Re-engineering Healthcare Organized for Synergy: Predictive Analytics Human Factors Engineering Diagnostics 5

6 Big Data Value Chain (baseline) Data Generation Data Aggregation Performance Measurement Insight and Action Clinical Transformation and Reengineering Knowledge Generation Care byproduct Transactional Required for performance measurement Establishes current state Deployed as visualization, alerts and reminders Basis for new models of care delivery What to do in a patient like mine?

7 EHR and Clinical Analytics Information Exchange EPIC - Medical Transactions Core EHR Clinical Documentation Clinical Data Repository Clinical Systems Decision Support Data Collection CPOE egate Lab Cardiology Results Review Scheduling Communication Clinical Analytics Radiology GI Cardiology Clarity CDIS Data Warehouse Enterprise Data Repository Operational Reporting Complex Analytics Dashboards Communication

8 Care Gaps: Data to Action Populations 100% Bundles Action Arms Prevention Chronic Diseases Mammogram every year A1c 7-8 Automation of Orders Anticipatory Management Unclosed Loops Abnormal Pap Follow up Care Gaps Office-Based Decision Support Medication Safety Renal function monitoring Automated Outreach Regular care failures Heart Failure exacerbation Patient Engagement

9 Enhancing the Big Data Value Chain Data Generation Data Aggregation Performance Measurement Insight and Action Clinical Transformation and Reengineering Knowledge Generation Care byproduct Transactional Required for performance measurement Establishes current state Deployed as visualization, alerts and reminders Basis for new models of care delivery What to do in a patient like mine? Natural Language Processing Data Warehousing Dashboards Anticipate and Mitigate Automation Machine Learning

10 Data Enabled Emergency Department Simulation Modeling 10

11 Geisinger Genomics for Health Initiative Implementation of clinically useful genomic data to improve health and well-being for individuals, families and communities MyCode Community Large, stable population >95% participation Ethically valid consent Permission to recontact e-recruitment, consenting (MyGeisinger) Funded collaborations emerge Network (NIH) ClinGen Resource (NIH) PCORI Clinically Relevant Genomic Data Return to patients, physicians, EMR by expert team (genetic counseling, clinical genetics, bioethics) Clinical research, health outcomes and economic analyses Dissemination of new genomicsinformed care delivery models GHS-Regeneron Collaboration >100,000 pts consented >25,000 WES first 5 years

12 Ethical Framework For Clinical Genomics Research An Ethical Framework of Three Principles Principle of Trust Geisinger will educate and inform MyCode participants, engage them in dialogue and seek their perspectives on research Geisinger will utilize ethically sound practices in seeking the consent of prospective participants in MyCode Principle of Respect Consent to & Participation in MyCode + Return of Results Principle of Care Geisinger will care for MyCode participants by returning clinically actionable genomic results to them and to the health professionals who care for them

13 Clinical and Research Opportunities Unique Resources Expanded biobank Genomic data Clinical database Phenotype algorithms Opportunities Genomic discovery Predictive risk modeling Clinical implementation Patient and provider education Ethical, legal, social implications Health services research Health economics

14 Key Enablers for Using Big Data to Advance Healthcare Talent, Tools, Process and Patients Talent Clinical Leadership Role of the Data Scientist Tools Data architecture Clinical rules User interface Process Clinical agreements Scrubbing data Validation Patients Engagement Activation 14

15 Thank You! Questions? 15