Improved Outcomes from Data and Knowledge Driven Insights Anil Jain, MD, FACP

Transcription

1 Improved Outcomes from Data and Knowledge Driven Insights Anil Jain, MD, FACP April 20, 2016

2 Housekeeping 1. Using the control panel - Use the control panel on the right side of your screen to minimize and expand this panel by clicking on the arrow in the upper right corner. 2. Ask Questions - You can submit questions using the Question section located near the bottom of the control panel. We will take time to answer as many questions as we can during Q&A at the end of the presentation. If your question was not answered, we will respond to you individually after the event. 3. After the webinar - We want your feedback! Please take the short survey at the completion of the webinar. Also, all registrants will receive a copy of the presentation, and the recording for on-demand replay. 2

3 About Me Dr. Anil Jain is Senior VP and Chief Medical Officer of Explorys, an IBM Company, a Cleveland-based BIG DATA healthcare analytics company formed in 2009 based on innovations that he developed while at the Cleveland Clinic. Anil Jain, MD, FACP VP & Explorys Chief Medical Officer Dr. Jain began his career at the Cleveland Clinic in 1995, most recently as Senior Executive Director of IT until July 2011 where he led several Health IT innovations, including programs to support research and quality informatics and created interactive dashboards to monitor the meaningful use of the Electronic Health Record. He continues to practice medicine and teach medical residents as Consulting Staff at Cleveland Clinic s Department of Internal Medicine. He was formerly co-chair of the Information Management Committee of Better Health Greater Cleveland (BHGC), a Robert Wood Johnson Foundation Aligning Forces for Quality (AF4Q) Community. In addition, Dr. Jain had previously served as co- Director of the Biomedical Research Informatics core of the Clinical & Translational Research Collaborative (CTSC) at the Case Western School of Medicine and Instructor at Cleveland Clinic Lerner College of Medicine. Dr. Jain also serves on several advisory boards of Health IT companies in California and New York. He has authored more than 100 publications and abstracts and has given numerous talks at national and international meetings on the benefits of Health IT and how BIG DATA analytics can support quality improvement and biomedical research. He is a Diplomat of the American Board of Internal Medicine (ABIM), a Fellow of the American College of Physicians (ACP), and an active member of both the Health Information Management and Systems Society (HIMSS) and the American Medical Informatics Association (AMIA). Finally, he serves as a reviewer for several biomedical journals and national meetings. 3

4 Objectives Provide an update on Watson Health Discuss the opportunities and challenges of analytics in today s healthcare transformation Describe how Watson Health is creating a ecosystem for big datadriven and knowledge-driven insights Illustrate how knowledge driven insights through cognitive computing can enhance outcomes 4

5 Watson Health Update 5

6 IBM Watson Health Closes Acquisition of Truven Health Analytics April 8, 2016 Truven Health Analytics enables Watson Health to deliver previously unavailable capabilities across the health continuum to help improve the customer experience. Expands leadership as a high-value industry solutions provider of cognitive cloud-based healthcare data, analytics and insights Enables new offerings for valuebased care solutions, life sciences and government service organizations in domestic and international markets Expands market coverage by adding over 8,500 clients and expertise across government agencies, employers, health plans, hospitals, clinicians and life sciences Enables IBM s health cloud to build one of the world s largest and most diverse repository of health-related data, enabling an understanding of the true cost and value of care 6 6

7 Multiple Dynamics Pressing on Providers Today Payment Reform Reimbursement is no longer based on how many but on how good in terms of cost, quality and outcomes. Consolidation Payers and delivery systems are consolidating to gain market share. Care Coordination Integrated networks are required to deliver comprehensive care to populations under one payment umbrella. 7

8 Waste: Nearly a third of health spend 8

9 Beyond the Triple-Aim in Healthcare 9 9

10 MACRA MIPS-and-APMs/Timeline.PDF 10

11 For Success, It is Critical to Balance Between the Two Worlds 11

12 Importance of Analytics in Healthcare

13 Primary Obstacles to Widespread Analytics Adoption,

14 Results of the 2014 National Survey of ACOs* More than 50% of respondents on the recent survey listed the following as some challenges to leveraging HIT Infrastructure: More than 50% of respondents on the recent survey* listed the following as some challenges to leveraging HIT data and analytics: Interoperability Cost/Funding/ROI Workflow Integration Lack of Provider Engagement Training Lack of consensus on quality measures & benchmarks Access to external data Integration of disparate data Workflow Integration Data liquidity & quality Applying analytics to actual practice Training *Premier Inc. & ehealth Initiative; 14

15 Barriers to Adoption, 2015 Out of 50 responders rated as high influencers Culture & Politics (31) Fragmented Ownership (29) Access to Skilled Resources (27) Funding Process (26) Data Quality (23) Sponsorship (20) Tools and Technology (18) Data Access (15) Deloitte Center for Health Solutions 2015 US Hospital and Health System Analytics Survey 15

16 Driving Efficiency starts with taming healthcare Big Data 16

17 Watson Health Cloud 17

18 Solutions to power analytics and insights within the Ecosystem Life Sciences and Discovery Population Health Analytics Patient Engagement Care Management Services Layer Data Curation Data Lake Risk & Prediction Patient Engagement Person Matching Measure and Gap Care Management Index & Search Registry & Work-List Data Governance Acquisition & Storage Layer Explorys HDG Health Data Gateway Ambulatory EHRs Inpatient EHRs PMS Private CMS CCDA/ PHR 835/837 EDI Rx EDW Claims Claims HL7 Laboratory 18

19 Characterizing the Population Population Assessment Know their past and future utilization, their risks, and which are can be mitigated given your network s capabilities. Population Profile Demographic distribution Disease profile Geographic footprint Historical Utilization Utilization break-down High cost claimants E&M distribution Risk and Projected Utilization PMPM by condition and type Drivers of projected utilization Clinical Performance Overview of program performance (key measures and benchmarks) 19

20 Explorys Risk Model Scenario Prospective Utilization How is the Explorys risk weight used? The normalized risk weight of 2.80 can be multiplied by an average $400 PMPM (example) to get a predicted utilization of $1120. This is equivalent to saying a member will cost 2.8 x average cost. Risk Stratification The risk weight of 2.80 would put a patient in a specific bin. For example, a low risk bin may be formed by grouping patients with a risk of 2 or less into GROUP 1 and between 2 and 4 into GROUP 2 and above 4 into GROUP 3 to allocate scarce resources. Acuity adjusted panels The risk weight of 2.80 can be added to the risk weights of all other members of a specific provider to obtain an average (e.g., 1.5) this average risk score of 1.5 can be used to adjust the actual panel size of 1,600 patients to create an acuity adjusted panel of 2,400 (1,600 x 1.5) so that work load can be more accurately compared. Benchmark performance The risk score can be used to adjust actual costs and quality metrics to control for severity. Actual costs of $1350 PMPM can be compared to a predicted of $1300 PMPM. 20

21 Training Dataset Risk Factors Risk Models Performance CMS Medicare 5% Sample Demographics Age/Gender Diagnoses ICD9/10/CCS Concurrent Medicare Prospective Medicare R 2 = 74.2% MAPE = 49.3% R 2 = 30.2% MAPE = 88.5% Truven MarketScan Procedures HCPCS/CPT/BETOS Medications RxNorm Concurrent Commercial Prospective Commercial R 2 = 60.2% MAPE = 63.4% R 2 = 31.1% MAPE = 88.3% ~1100 factors 1. Not all models use all available factors 2. Predict more accurate current/future utilization/costs in commercial & Medicare populations 3. Benchmark performance against industry standards 4. Risk Stratify populations for better care coordination 5. Measure acuity of a population with more fidelity Yi R. Evaluation of Explorys Risk Adjustment and Predictive Models. Milliman Client Report, 2014, p4, Table 1. Note that higher R 2 and lower Mean Absolute Percent Error (MAPE) represent better performance 21

22 Summarizing Utilization and Clinical Risk Analytics 22

23 Actionable Insights within the Care Workflow Performance Measurement Up-to-date network-wide reporting and measures relative to performance targets, program return-on-investment, and pinpointed opportunities for continued improvement. Role Specific Dashboards for Leadership Care coordinators Providers Program Specific Measure Libraries & Scorecards ACO (commercial and Medicare) Medicare Advantage Employee Health plan Physician-based HEDIS Inpatient quality and efficiency Utilization Pre-built Reports & Data Marts Support for GPRO reporting Provider scorecards and performance plans HCC and proper coding opportunities Contract performance 23

24 Intelligently Engage the Population Risk Stratification with advanced predictive utilization models combined with care gaps from Clinical + Claims data drive Smarter Engagement Patient Engagement Simulated patient data for demonstration purposes only Data Platform Shared Superstructure + Match Curate Calculate Prioritize Report Supporting HIPAA & Complex Data Governance Care Management Big data derived smart lists aligning the most appropriate messaging to patients based upon predictive models 24

25 Clinical Factors only represent 10% of a person s overall health Environment Socioeconomic Standing Healthcare Access & Experience Determinants of Health Outcomes* Behavior & Habits Genetics & Family History Social Influences *The Relative Contribution of Multiple Determinants to Health Outcomes, Lauren McGover et al., Health Affairs, 33, no.2 (2014). 25

26 Data Rich, Information Poor? 26

27 Era of Cognitive Computing 27

28 Knowledge and Data Driven Insights Medical Records Medical literature Clinical guidelines Key textbooks Published Knowledge Wearable/monitoring data Claims data Community-based data Exogenous data Observational Data WATSON WATSON Closing the translational knowledge gap Discovering new real world evidence 28

29 Augmented Intelligence 29

30 30

31 Other Examples of Data and Knowledge Driven Insights Apple/Twitter/Facebook/UnderArmor Merging social-media, mobile applications, and activity trackers to collect and analyze exogenous data to inform and empower better health through a digital consumer experience. American Heart Association (AHA) Combining the cognitive computing power of IBM Watson with AHA s employer workplace and consumer health assessments to help reduce the risk of cardiovascular disease. CVS Health Employing new data sources and cognitive insights to more accurately identify at risk individuals and to coordinate the provider ecosystem around closing key care gaps through activities such as prescription refill and testing reminders. Medtronic Leveraging powerful analytics and cognitive computing with diabetes medical devices and health data to develop a new generation of personalized diabetes management solutions to more appropriately manage complex patients. 31

32 Future of Cognitive Computing in Healthcare IBM Institute for Business Value 32

33 Concluding Thoughts Healthcare is undergoing significant transformation in the manner in which care is delivered and reimbursed Health systems are undergoing consolidation and alignment across communities bringing disparate information systems together Advances in digital engagement, personalized medicine and deployment of health IT is generating big data Actionable insights into changing provider and patient behavior will require the use of both data driven and knowledge driven analytics Cognitive computing will be critical to achieving the outcome of the triple-aim while promoting the best patient and provider experience 33

34 Questions and Contact Information Anil Jain, MD, FACP Senior VP & Chief Medical Officer Explorys, an IBM Company/Watson Health 34

35 THANK YOU! The recording and handouts will be sent to you via within 2 business days 35