Predictive Care Models to Improve Outcomes Brendan Fowkes Sr. Healthcare Solution Executive May 14, 2013

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1 Predictive Care Models to Improve Outcomes Brendan Fowkes Sr. Healthcare Solution Executive May 14, 2013 Technology Insights Performance Outcomes IBM Corporation

2 Healthcare Transformation: A Work in Progress 1st 8.0 US rank in Healthcare spending 1 Hours each day an average family physician spends on direct patient care 3 $750B 37th % US rank in quality of care delivered 2 90% Hours required to meet the patient care guidelines each day 3 of the world s data was created in the last two years % (Billion) Wasted on missed opportunities along with unnecessary, error-prone and inefficiently delivered services 3 US physicians still reliant on paper based medical records systems 4 An Ocean of Unused Data of the world s data is unstructured World Health Statistics 2011 from World Health Organization The World Health Report 2000 Health Systems: Improving Performance from World Health Organization Best Care at Lower Cost: The Path to Continuously Learning Health Care in America from Institute of Medicine / National Academy of Sciences Physician Adoption of Electronic Health Record Systems: United States, 2011 from National Center for Health Statistics

3 Disease and Cost of Care Progression 20% of People Generate 80% of Costs Early Intervention Opportunities Identification Health Status Healthy Low Risk At Risk High Risk Early Clinical Symptoms Active Disease Health Care Spending Time Early Intervention Opportunities Identification 70% of US Deaths from Chronic Diseases

4 Strategic Dilemma: How to migrate the institution to a new business model while still managing the model you re in Potential Future State Business Model Business Model Disruption Today s Business Model Core competencies, processes and revenue model designed on fee for service chassis Capabilities and revenue model designed around value demonstration and management of patient populations New Competencies Required Care planning for individuals and populations, including predictive modeling and disease management Insight for reimbursement models focusing on pay-for-outcomes Measurement and transparency of performance data to drive best practices on outcomes and value Improved risk management capabilities Health Information technology for population management and care management / coordination

5 Patient Centered Care Models Work Growing and Accelerating Industry Adoption WellPoint (34M), Aetna (18M), Humana (12M), UHG (34M) and BSBC plans 1 Over 300% growth in PCMH sites and clinicians since Jan Better Care Outcomes 60% better access to care % long-term improvement of cholesterol control % improvement in HbA1c levels 1.3 Lower Care Costs 40% fewer inpatient hospital days % fewer visits to specialists and 70% fewer visits to ER 1.5 9% overall lower costs resulting in savings of $32 per month per member Benefits of Implementing the Primary Care Patient-Centered Medical Home: Cost and Quality Results, 2012 from PCPCC 1. BCBS of Michigan (Physician Group Incentive Program) (2011) 2. UPMC (Pittsburgh, PA) Independence Blue Cross Pennsylvania Chronic Care Initiative (Southeast PA) 2012(51) 4. Capital Health Plan, (Tallahassee, FL) Blue Quality Physician s Program (BCBSNC) Capital District Physicians Health Plan (Albany, NY)

6 IBM s involvement in Healthcare Buyer/Consumer of Healthcare 450,000 lives; $1.6B in investment Partner to promote innovation and value Change agent Advocate for medical homes and founder of Patient Centered Primary Care Collaboration Healthcare standards leadership and participation in Continua, CDISC, DICOM, ISO, OHT and CAQH-CORE Influencer to national level reform and ehealth IBM Research healthcare patents, systems thinking Solution provider across the continuum 6 Intersection of business and technology for Payers / Providers / Life Sciences / Devices Business services, process and workflow integration Big Data, information management and analytics Secure, optimized transaction processing Infrastructure build and optimization

7 Information Should Aid Us, Not Lie Hidden and Dormant If we could only activate the relevant information to bring insights to the point of care when needed most Knowledge, Guidelines and Best Practice Measures Longitudinal Data Driven Insights Confirm what I think or suspect? How many are being missed? Identify Intervention Opportunities Show me something new or unexpected? Adapt Care to Changing Conditions and New Information How do we move faster and anticipate change? Time once spent manually interpreting data becomes time spent healing patients Aggregate, activate and enrich relevant patient information beyond what is known Surface new data driven insights that enable new intervention opportunities earlier Adapt to changes and proactively deliver individualized patient centered care 7

8 Seton Healthcare Family Reducing CHF readmission to improve care IBM Content and Predictive Analytics for Healthcare uses the same type of natural language processing as IBM Watson, enabling us to leverage information in new ways not possible before. We can access an integrated view of relevant clinical and operational information to drive more informed decision making and optimize patient and operational outcomes. Charles J. Barnett, FACHE, President/Chief Executive Officer, Seton Healthcare Family Business Challenge Seton Healthcare strives to reduce the occurrence of high cost Congestive Heart Failure (CHF) readmissions by proactively identifying patients likely to be readmitted on an emergent basis. What s Smart? IBM Content and Predictive Analytics for Healthcare solution will help to better target and understand high-risk CHF patients for care management programs by: Utilizing natural language processing to extract key elements from unstructured History and Physical, Discharge Summaries, Echocardiogram Reports, and Consult Notes Leveraging predictive models that have demonstrated high positive predictive value against extracted elements of structured and unstructured data Providing an interface through which providers can intuitively navigate, interpret and take action Featured on Smarter Business Outcomes Seton will be able to proactively target care management and reduce re-admission of CHF patients. Teaming unstructured content with predictive analytics, Seton will be able to identify patients likely for readmission and introduce early interventions to reduce cost, mortality rates, and improved patient quality of life. IBM solution IBM Content and Predictive Analytics for Healthcare IBM Cognos Business Intelligence IBM BAO solution services

9 What Really Causes Readmissions at Seton Key Findings from Seton s Data The Data We Thought Would Be Useful Wasn t Structured data not available, not accurate enough, without the unstructured data - which was more trustworthy What We Thought Was Causing 30 Day Readmissions Wasn t 113 possible candidate predictors expanded and changed after mining the data for hidden insights New Hidden Indicators Emerged Readmissions is a Highly Predictive Model 18 accurate indicators or predictors (see next slide) 97% at 80th percentile 49% at 20th percentile 9 Predictor Analysis % Encounters Structured Data % Encounters Unstructured Data Ejection Fraction (LVEF) 2% 74% Smoking Indicator 35% (65% Accurate) 81% (95% Accurate) Living Arrangements <1% 73% (100% Accurate) Drug and Alcohol Abuse 16% 81% Assisted Living 0% 13%

10 What Really Causes Readmissions at Seton Top 18 Indicators Ranking of Strength of Model Variable New Insights Uncovered by Combining Content and Predictive Analytics Top indicator JVDI not on the original list of as well as several others Assisted Living and Drug and Alcohol Abuse emerged as key predictors - only found in unstructured data LVEF and Smoking are significant indicators of CHF but not readmissions A combination of actionable and non-actionable factors cause readmissions Projected Odds Ra o Jugular Venous Distention Indicator 2. Paid by Medicaid Indicator 3. Immunity Disorder Disease Indicator 4. Cardiac Rehab Admit Diagnosis with CHF Indicator 5. Lack of Emotion Support Indicator 6. Self COPD Moderate Limit Health History Indicator 7. With Genitourinary System and Endocrine Disorders 8. Heart Failure History 9. High BNP Indicator 10. Low Hemoglobin Indicator 11. Low Sodium Level Indicator 12. Assisted Living 13. High Cholesterol History 14. Presence of Blood Diseases in Diagnosis History 15. High Blood Pressure Health History 16. Self Alcohol / Drug Use Indicator 17. Heart Attack History 18. Heart Disease History

11 The Impact of Readmissions at Seton CHF Patient X What Happened? Admit / Readmission 30-Day Readmission Patient X was hospitalized 6 times over an 8 month period. The same basic information was available at each encounter and Patient X s readmission prediction score never dropped below 95% (out of possible 100%) 98% 24 days Apr % 98% 8 days May May % 95% 144 days 26 days 44 days Oct % Nov Dec Individual Patient Data at Each Encounter (Patient Dec 20, 2009) Patient Population Monitoring Clinical and Operational Data

12 Combining Knowledge and Data Driven Insights for Feature Selection1,2 [1] Dijun Luo, Fie Wang, Jimeng Sun, Marianthi Markatou, Jianying Hu,Shahram Ebadollahi, SOR: ScalableOrthogonal Regression for Low-Redundancy Feature Selection and its Healthcare Applications. SDM 12 [2] Jimeng Sun, Jianying Hu, Dijun Luo, Marianthi Markatou, Fei Wang, Shahram Edabollahi, Steven E. Steinhubl, Zahra Daar, Walter F. Stewart. Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records. AMIA 12 (to appear)

13 Prediction Results of Knowledge-driven Features plus Data-driven Features AUC Hypertension 0.6 +diabetes all knowledge features CAD Number of features AUC significantly improves as complementary data driven risk factors are added into existing knowledge based risk factors. A significant AUC increase occurs when we add first 50 data driven features

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19 What Have We Learned So Far? Structured Data is Not Enough Unstructured data significantly increases the richness and accuracy of analysis and decision making including paper / faxes Prediction Results of Knowledge-driven Features plus Data-driven Features #=@$! %&#$! "##$ #=?&$ Data Driven Insights! %##$! &#$ #=?$! " #$ Today s Care Guidelines Only Get You So Far Not granular enough to deliver on the promise of personalized medicine with data driven insights 1, 2 #=>&$! *45+*$ #=>$!. 5' $ ' (($) *+, (-. /- $ 0- ' 123-4$ Knowledge and Guidelines : ; <$ #=&&$ #=&$ Manual Processes and Traditional Workflow!AUC significantly improves as complementary data driven risk factors are Approaches Don t Work added into existing knowledge based risk factors.!a significant AUC increase occurs when we add first 50 data driven features Process complexity increases with disease complexity changing conditions require process adaptability 3 #$ %##$ "##$ A##$ B##$ %&' ( ) *$+,$,) -. &*) / $ &##$ >##$ 2012 IBM Corporation Dijun Luo, Fie Wang, Jimeng Sun, Marianthi Markatou, Jianying Hu,Shahram Ebadollahi, SOR: ScalableOrthogonal Regression for LowRedundancy Feature Selection and its Healthcare Applications. SDM 12 Jimeng Sun, Jianying Hu, Dijun Luo, Marianthi Markatou, Fei Wang, Shahram Edabollahi, Steven E. Steinhubl, Zahra Daar, Walter F. Stewart. Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records. Under submission at AMIA 12 Blind Surgeon Metaphor Problem - W.M.P. van der Aalst, M. Weske, and D. Grünbauer. Case Handling: A New Paradigm for Business Process Support. Data and Knowledge Engineering, 53(2): , 2005

20 Working Together to Beat Cancer Cancer is an insidious disease and the second highest cause of death 1 in 4 individuals will die from cancer 3X rate cancer cost climbs vs. std. health costs or 15-18% / yr. X 20% of cancer cases receive the wrong diagnosis initially with some as high as 44% B overall costs of cancer in the US in 2010 $$$$$$$$$$ $$$$$$$$$$ $$$$$$$$$$ Source: American Cancer Society, National Health Institute IBM + + IBM W o rk ing T o g ethe r to B e a t C a nc e r

21 Thank you for your time