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Medicare 101 - February 2017 Update
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A Catalyst to Drive Behavior and Engagement in a Value-Based World

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This presentation throws light on how to:
• Use personas and analytics to improve compliance and effectiveness
• Leverage actionable insights from next generation analytics to drive outcomes based programs and risk-based contracting
• Use the science behind the data to focus targeted outreach to the right populations and providers

For more information on our SCIOVantage solution, please visit: http://www.sciohealthanalytics.com/offerings/products/sciovantage

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A Catalyst to Drive Behavior and Engagement in a Value-Based World

  1. 1. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |1 Predictive Analytics A Catalyst to Drive Behavior and Engagement in a Value-based World July 22, 2016 Rose Higgins President, North America
  2. 2. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |2 Market Driver: Connecting and Coordinating Care DRIVINGFORCES HEALTHCARE REGULATIONS AFFORDABLE CARE ACT (ACA) MEDICARE Data needs are expanding beyond paid claims. Acquiring, aggregating, and managing heterogeneous data is a core competence. Utilize data for actionable business value PARADIGM SHIFT Fee for Service Fee for Value Sick Care Well Care Fragmented Episodes Coordinated Care Acute Care Focused Cross Continuum Patient Consumer
  3. 3. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |3 Increased scrutiny, audits, rate cuts, STAR ratings and Quality/Satisfaction Measures Value-based care • Rewards outcomes and effectiveness • Financial accountability for care • Requires precise resource use • Patient Satisfaction and Engagement Fee for service • Illusion of provider sovereignty • Financial conflicts for patients • Rewards increased intensity and volume 2000 2020 Intensity of Care Labor Costs Supply Costs What is Reimbursed $ Unsustainable Costs Changing Payment Models Pressure from Payers (Employers, Govt, Plans) Market Driver: Cost Pressures
  4. 4. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |4 Emerging Trends Increased mobile usage Payment tied to VBH Consumers as new $ managers Precision Medicine Predictive/Anticipatory Cloud Wearables become fully integrated Medical devices data ACO & IDNs become the standard care delivery models Willingness to participate in telehealth Payers demand evidence of “patient success” Participatory Real time Social Networks MACRA / MIPS / APS Shift to lowest level care setting- home will become where most care is delivered Pharma patient centricity Preventive Big Data Collaborative and social BI increases Rising drug cost impact Consumers choose nontraditional providers Pharma shifts to drive better pt. outcomes. Personalized Multi data sources Technology Enabled Behavior Change Volume to Value Consumer Engagement Personalized Care Delivery Actionable Analytics Data Handling
  5. 5. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |5 Increasingly Complex Patient Populations Older patients with multiple chronic conditions consume an outsized amount of resources Longer Life Span Boomer Generation Two thirds of the Medicare +beneficiaries have two or more chronic conditions. (2010 Center for Disease Control Study) There are 49,432,610 Medicare beneficiaries in the US (Kaiser Family Foundation) Most Common Chronic Conditions High Blood Pressure High Cholesterol Heart Disease Arthritis Diabetes Average health costs for someone who has one or more chronic conditions is greater than for someone without any chronic conditions 5 5X 2 2 3 4 2 3 1 0 1 50% 45% 31% 29% 28%
  6. 6. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |6 Using Analytics to Better Understand Your Populations Increasingly Complex Patient Populations Accelerating Shift to Value-Based Care ≥1 Chronic Condition = the Cost5x
  7. 7. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |7 The Foundation of Population Health: Robust Profiles Member, Patient or Provider Profile Clinical Factors Risk, Cost, Quality, Utilization, & Attribution Demographic Behaviors Attitudes
  8. 8. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |8 View Populations Through the Lens of Impactability Population 100% Impactability Prospective Risk Moderate Impactability 12% of Members Low Impactability 75% of Members High Impactability 12% of Members High Low Opportunity Goal Close Gaps and Steerage to Managed Networks Close Gaps and Steerage to Managed Networks Manage High Costs and Risk Factors Manage High Costs High Risk 10% Moderate Risk 1.5% Low Risk 0.5% High Risk 8% Moderate Risk 3% Low Risk 1% High Risk 13.5% Moderate Risk 27% Low Risk 34.5% High Cost 1% of Members
  9. 9. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |9 Allocate Resources Towards Impactable Conditions Diabetes, $326,515,914 COPD, $56,262,780 Seizures, $20,743,606 Obesity, $53,401,848 Rheumatoid Arthritis, $47,819,586 Inflammatory Bowel, $13,258,925 Back Pain, $303,270,098 Depression, $158,480,248 Hyperlipidemia, $326,077,730 Asthma, $119,587,531 CHF, $69,839,071 Maternity, $69,955,753 CKD, $48,898,470 Parkinson Disease, $1,602,486 CAD, $112,816,271 Hypertension, $497,076,823 -10 -8 -6 -4 -2 0 2 4 6 8 10 -10.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 10.0 Condition Intervention Summary Harder to Impact More Complex Interventions Less Complex Interventions Easier to Impact High Volume Silent Diseases High Volume Symptomatic Diseases Maternity Symptomatic Chronic Low Numbers Diabetes, $497,076,823 Hypertension, $326,515,914 Optimal Interventions for Diabetes 1 Eye Exam 2 HbA1c 3 Lipid Test 4 Medication Regimen Compliance
  10. 10. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |10 Track Compliance and Progress
  11. 11. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |11 Provider & Patient Engagement
  12. 12. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |12 Segmenting Patients by Consumer Type Healthy & Affluent Balanced Adults High Utilizers Quality Driven Cost Conscious Chronic older Adults High Cost Baby Boomers No.of chronic conditions ER Paid PMPM IP Paid PMPM ER Utilization IP Utilization 0.54 0.70 0.71 0.86 0.82 1.02 1.13 Median Risk Prospective Score 0.6 0.7 0.8 1.2 1.2 1.3 1.6 0.09 0.05 0.10 0.04 0.07 0.08 0.09 0.25 0.22 0.34 0.23 0.18 0.21 0.23 $75 $73 $147 $54 $75 $118 $248 $10 $9 $14 $9 $7 $10 $11 Healthy & Affluent Balanced Adults Value Driven Quality Driven Cost Conscious Chronic older Adults High Cost Baby Boomers 44% 24% 19% 29% 8% 12% 11% 67% 63% 32% 54% 14% 40% 54% 58% 30% 14% 33% 2% 13% 12% Net Worth > $100K Estimated HH Income > $100K Estimated HH Debt > $15K Median Home Value 0.54 0.70 0.71 0.86 0.82 1.02 1.13 Median Risk Prospective Score $333K $285K $216K $165K $151K $146K $175K Healthy & Affluent Balanced Adults Value Driven Quality Driven Cost Conscious Chronic older Adults High Cost Baby Boomers Habitual Frequent Frequent Frequent Occasional Sporadic Sporadic PhoneMailCell Text E-mail Web Text E-mail E-mail Channel Preference Spending Pattern No. of Automobiles 0.54 0.70 0.71 0.86 0.82 1.02 1.13 Median Risk Prospective Score
  13. 13. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |13 Prioritize Care Gap Closure at an Individual Level Member ID Risk Score Impactability Score Gap1 Gap2 Gap3 000000010506 0.89 1.68 Diabetes - Consider Foot Exam HbA1c Less Than 7 Target 000000010331 0.83 1.51 Lipid Panel Spirometry 000000010043 0.81 1.64 Consider Pulmonary Rehabilitation AST Test Physical Therapy 000000010154 0.73 1.39 Lipid Panel Spirometry Alpha-Glucosidase 000000010539 0.73 1.04 Diabetes and Macroalbuminuria - Consider Adding an ACE Inhibitor or ARB Diabetics 50 years and Older - Consider Screening for Peripheral Arterial Disease Member ID In Last 12 Months Cost Incurred in Last 12 Months Probability of ER Admission Predicted Probability of ER Admit IF all the gaps are closed Difference Impactability Score# Hospitalization # ER Visits InPatient (PMPM) ER (PMPM) OutPatient (PMPM) Professional (PMPM) Pharmacy (PMPM) 000000010506 1 1 $2,999 $302 $209 $201 $130 93% 22% 71% 1.68 000000010331 0 0 $237 $158 $147 90% 27% 64% 1.51 000000010043 0 2 $287 $231 $225 $133 91% 22% 69% 1.64 000000010154 0 0 $231 $178 $103 74% 16% 58% 1.39 000000010539 0 0 $340 $181 $96 70% 27% 44% 1.04 000000010507 0 0 $333 $208 $134 73% 24% 49% 1.15
  14. 14. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |14 Analytics Driving Engagement Outreach
  15. 15. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |15 Small Improvements Carry Significant Revenue Implications Calculation: $800 average pmpm payment x RAF A 45,000 member health plan purchases Medicare risk adjustment and HEDIS/Star/P4P monitoring analytics Within 90 days their systems are online to support new suspecting and provider collaboration programs: • Identification: A prioritized list of all patients that need to be seen by 12/31 to ensure care gaps are closed and revenue streams remain constant • Provider Collaboration: Each morning physicians receive patient-specific pre-populated forms containing previously diagnosed conditions and any outstanding Stars measure assessments needed for that member. PMPM Revenue In two months, the health plan increases their average Medicare RAF score from .95 to .98 and sees significant improvement on a number of clinical Star measures $760 $784 $0 $100 $200 $300 $400 $500 $600 $700 $800 $900 .98 RAF .95 RAF Pre-Solution Revenue Post-Solution Revenue $24 Provider Reimbursement for many providers is based on % of revenue/premium At 35% of premium, this example generates an additional provider revenue of $4,500,000 Health Plan An increase of just 0.03 to the RAF score generated an additional $24/member/month. For a 45,000 member plan, this equates to an annual revenue increase of $12,960,000
  16. 16. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |16 Case Study Diabetes Management at a Medicaid ACO Member Data Analytics Engine Actionable Insights Challenges Solution Value Analysis of the program demonstrated the following outcomes for participants: • 5% Total Cost Savings • Patient compliance to LDL & HbA1c both increased by 9% • Visit to PCP: Increased by 29% • Acute Utilization: Decreased by 25% & 20% in IP & ER respectively • Eligible population stratified as medium or high risk • Patient cohorts receiving multiple incentives identified. • Tracked ‘non-compliance’ members for 6 months for gap closure • Dashboards to measure quality outcomes & utilization • Understand the impact of Diabetes Disease Management Program: 1. Incur less cost 2. Utilize less ER and Inpatient services 3. Utilize more physician office services 4. Have higher compliance EHR Data Rx & Dx Claims Patient Medical History Cost Data  Identify  Engage  Measure
  17. 17. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |17 In Summary Leveraging Predictive Analytics as a Catalyst to Drive Behavior and Engagement Multi-source persona data including behavioral, clinical factors, demographic, attitudes Impact and receptiveness to treatment Focus on the population and individual level Optimizing incentives Measure the outcome and ongoing sustainability Improved quality and effectiveness Better care for lower cost

This presentation throws light on how to: • Use personas and analytics to improve compliance and effectiveness • Leverage actionable insights from next generation analytics to drive outcomes based programs and risk-based contracting • Use the science behind the data to focus targeted outreach to the right populations and providers For more information on our SCIOVantage solution, please visit: http://www.sciohealthanalytics.com/offerings/products/sciovantage

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