The predictive power of Big Data in healthcare

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1 Charlie Schick, PhD Big Data, Healthcare and Life Sciences The predictive power of Big Data in healthcare 2012 IBM Corporation

2 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

3 Data trends - volume, velocity, and variety 44x as much Data and Content Over Coming Decade 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 ,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

4 Healthcare data assets Claims Health Plans Pharmacy Providers Patient Supply Chain Charts Lab Tests Adverse Event Finance EMR Research Devices Digital Hosp. Social 4

5 Information Management 2012 IBM Corporation

6 The future of healthcare transformation will be DATA-CENTRIC paraphrasing Ray Campbell, Exec Dir, CEO Mass. Health Data Consortium. 6

7 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

8 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

9 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

10 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

11 And now for a deeper dive 11

12 OUTCOMES ANALYTICS 12

13 Who s Analyzing Outcomes? 13

14 Who s Analyzing Outcomes? 14

15 Who s Analyzing Outcomes? 15

16 Who s Analyzing Outcomes? 16

17 Who s Analyzing Outcomes? 17

18 Who uses what data? Federal Pharma Payers End Users All The Blues! Data 18 Payers have their own claims data.

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21 Disease surveillance 21

22 Data baby 22

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25 Personal fitness monitoring - devices 25

26 Personal fitness monitoring - visualization 26

27 Personal healthcare analytics? 27

28 Some of the images are from 28 dpstyles Illustir dionehinchcliffe https://mybasis.com/ https://www.insidetracker.com/

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30 Legal Disclaimer IBM Corporation All Rights Reserved. The information contained in this publication is provided for informational purposes only. While efforts were made to verify the completeness and accuracy of the information contained in this publication, it is provided AS IS without warranty of any kind, express or implied. In addition, this information is based on IBM s current product plans and strategy, which are subject to change by IBM without notice. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this publication or any other materials. Nothing contained in this publication is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software. References in this presentation to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. Product release dates and/or capabilities referenced in this presentation may change at any time at IBM s sole discretion based on market opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. Nothing contained in these materials is intended to, nor shall have the effect of, stating or implying that any activities undertaken by you will result in any specific sales, revenue growth or other results. If the text contains performance statistics or references to benchmarks, insert the following language; otherwise delete: Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user's job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here. If the text includes any customer examples, please confirm we have prior written approval from such customer and insert the following language; otherwise delete: All customer examples described are presented as illustrations of how those customers have used IBM products and the results they may have achieved. Actual environmental costs and performance characteristics may vary by customer. Please review text for proper trademark attribution of IBM products. At first use, each product name must be the full name and include appropriate trademark symbols (e.g., IBM Lotus Sametime Unyte ). Subsequent references can drop IBM but should include the proper branding (e.g., Lotus Sametime Gateway, or WebSphere Application Server). Please refer to for guidance on which trademarks require the or symbol. Do not use abbreviations for IBM product names in your presentation. All product names must be used as adjectives rather than nouns. Please list all of the trademarks that you use in your presentation as follows; delete any not included in your presentation. IBM, the IBM logo, Lotus, Lotus Notes, Notes, Domino, Quickr, Sametime, WebSphere, UC2, PartnerWorld and Lotusphere are trademarks of International Business Machines Corporation in the United States, other countries, or both. Unyte is a trademark of WebDialogs, Inc., in the United States, other countries, or both. If you reference Adobe in the text, please mark the first use and include the following; otherwise delete: Adobe, the Adobe logo, PostScript, and the PostScript logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States, and/or other countries. If you reference Java in the text, please mark the first use and include the following; otherwise delete: Java and all Java-based trademarks are trademarks of Sun Microsystems, Inc. in the United States, other countries, or both. If you reference Microsoft and/or Windows in the text, please mark the first use and include the following, as applicable; otherwise delete: Microsoft and Windows are trademarks of Microsoft Corporation in the United States, other countries, or both. If you reference Intel and/or any of the following Intel products in the text, please mark the first use and include those that you use as follows; otherwise delete: Intel, Intel Centrino, Celeron, Intel Xeon, Intel SpeedStep, Itanium, and Pentium are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries. If you reference UNIX in the text, please mark the first use and include the following; otherwise delete: UNIX is a registered trademark of The Open Group in the United States and other countries. If you reference Linux in your presentation, please mark the first use and include the following; otherwise delete: Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both. Other company, product, or service names may be trademarks or service marks of others. 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