Charlie Schick, PhD Big Data, Healthcare and Life Sciences The predictive power of Big Data in healthcare 2012 IBM Corporation
Market Forces Driving Health Care Transformation Increasing incidence and cost of chronic and re-emerging infectious diseases Empowered consumers expect better value, quality, and outcomes Growing costs for new, revolutionary technologies and treatments - including shrinking reimbursements Industry challenges and opportunities Changing demographics and lifestyles drive associated costs New market entrants and new approaches to health and care delivery increase complexity and competition Source: IBM HCLS, IBM GBS Institute for Business Value 2 Primary Care and Nursing shortages demand workforce productivity and efficiency Health care is shifting from local to state and national contexts
Data trends - volume, velocity, and variety 44x as much Data and Content Over Coming Decade 2020 35 zettabytes 1in3 Business leaders frequently make decisions based on information they don t trust, or don t have Business leaders say they don t have access to the information 1in2 they need to do their jobs 2009 800,000 petabytes 90% Of world s data is unstructured 83% of CIOs cited Business intelligence and analytics as part of their visionary plans to enhance competitiveness 60% of CEOs need to do a better job capturing and understanding information rapidly in order to make swift business decisions 3
Healthcare data assets Claims Health Plans Pharmacy Providers Patient Supply Chain Charts Lab Tests Adverse Event Finance EMR Research Devices Digital Hosp. Social 4
Information Management 2012 IBM Corporation
The future of healthcare transformation will be DATA-CENTRIC paraphrasing Ray Campbell, Exec Dir, CEO Mass. Health Data Consortium. 6
Analytics-Driven Healthcare Enterprise Optimization Predictive Analytics BI Reporting and Ad Hoc Analysis What is the best choice? What will happen? What will the impact be? What happened? When and where? How much? Transaction reporting Basic reporting Spreadsheets 7 Current analytics level Data integration Data warehouse Dashboards Clinical data repositories Departmental data marts Decision support analytics Enterprise analytics Evidence-based medicine Clinical outcomes analytics Predictive analytics Personalized healthcare Dynamic fraud detection Patient, member behavior
Who needs help? Providers Health Insurer/Health Plans Hospitals nonprofit, for-profit, government Academic medical centers Medical practices Retail clinics Ambulatory surgery centers Long-term care facilities Home health agencies 8 Life Sciences Payers Health maintenance organizations (e.g., Kaiser Permanente) Blue Cross Blue Shield plans in the U.S. Commercial insurers (e.g., Aetna, BUPA, Groupe Mutuel, Wellpoint) Provider-sponsored plans (e.g., Geisinger Health Care) Government-run insurance programs Governments National healthcare systems (e.g., England s National Health Service) Government health insurance programs (e.g., U.S. Medicare and Medicaid) Government healthcare agencies (e.g., U.S. HHS, Health Ministries, Public Health) Government-run hospitals Policy-makers (e.g., the European Union) Biotech / Pharmaceuticals (BioPharma) Academic & Government Research Labs Contract Research Organizations (CROs) Medical Device & Diagnostics (MD&D) Medical Distributors, Life Sciences Support Services, Tools & Tech
Big Data solutions accelerate healthcare transformation Evidence Based Medicine Evidence based Healthcare Models driven by Health outcomes. Analyzing Care experience requires inspecting structured and unstructured data Health Outcomes Provider & Staff shortage demands workforce productivity & Efficiency improvements Knowing patients 360 implies looking at all their data to provide optimal care Patient Centered Care Knowing patient s lifestyle and habits helps drive optimal outcomes and is part of providing comprehensive care pre and post visit. Disease Management Conducting disease management and surveillance requires exhaustive processing of structured and unstructured data to identify chronic and reemerging infectious diseases 9
Big Data in action Clinical Analytics Unifying clinical, financial, and operations data for clinical decision support systems, transparency of medical data, aggregating and synthesizing patient, create the reports they are required to create, analyzing their operational and financial data for efficiency gains. Drug discovery analytics Integration of clinical, healthcare, patents, medical journals, compound info, public research data, safety data to enable contextual, integrated access to correlated information around disease, target, and compound to provide key insights into decisionmaking for target selection, compound selection, safety vs. efficacy issue discovery, lead optimization, clinical issue discovery, and so forth. Outcomes Analytics Unifying all patient related data (structured and unstructured) to get a 360-degree view of patient to measure and predict outcomes, manage patient population. And for payer, provider scoring and outcomes-based incentive calculation. Genomics Analytics Combining patient genomic data with clinical data. Genomic data is becoming critical to the complete patient record, rather than an isolated self-sufficient data set. Fraud Waste and Abuse Analytics Analyzing claims and benefits of OEF/OIF veterans benefits and education fraud. Potential to do this real-time using Streams and Big Insights Device analytics Capturing the vital signs from babies to detect advanced warning of the onset of complications. 10
And now for a deeper dive 11
OUTCOMES ANALYTICS 12
Who s Analyzing Outcomes? 13
Who s Analyzing Outcomes? 14
Who s Analyzing Outcomes? 15
Who s Analyzing Outcomes? 16
Who s Analyzing Outcomes? 17
Who uses what data? Federal Pharma Payers End Users All The Blues! Data 18 Payers have their own claims data.
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Disease surveillance 21
Data baby 22
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Personal fitness monitoring - devices 25
Personal fitness monitoring - visualization 26
Personal healthcare analytics? 27
Some of the images are from 28 http://www.plosone.org/article/info:doi%2f10.1371%2fjournal.pone.0019467 http://asthmapolis.com/ dpstyles http://www.flickr.com/photos/dpstyles/6862564508/sizes/m/in/photostream/ http://www.flickr.com/photos/dpstyles/6976377616/sizes/m/in/photostream/ http://www.wired.com/gadgetlab/2012/02/first-look-nike-fuelband-exercise-monitor/ Illustir http://www.flickr.com/photos/alper/5669501266/sizes/m/in/photostream/ dionehinchcliffe http://www.flickr.com/photos/dionhinchcliffe/6247139118/sizes/m/in/photostream/ http://www.bodymedia.com/ https://mybasis.com/ https://www.insidetracker.com/ http://www.myzeo.com/ http://jawbone.com/up/ http://www.hydracoach.com/ http://practicefusion.com http://patientslikeme.com
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