Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Leveraging analytics in a value based reimbursement world

457 views

Published on

This presentation throws light on:

• Impact of regulations in the new world of ACA and value-based reimbursement in context of the growth of integrated delivery networks and shifting risk
• Common areas providers might use to make up for lost reimbursement
• How payers can use analytics to detect areas meriting review and action, such as patterns of referrals that appear to be financially driven rather than in the members’ best interests.

Published in: Healthcare
  • Be the first to comment

Leveraging analytics in a value based reimbursement world

  1. 1. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |1 SCIO Health Analytics® Leveraging Analytics in a Value- Based Reimbursement World October 17, 2016
  2. 2. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |5 MARKET DRIVERS
  3. 3. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |6 Provider Consolidation and Shifting Care Traditional Fee for Service Patient PCP Hospital Lab Specialist SNF
  4. 4. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |7 Provider Consolidation and Shifting Care Traditional Fee for Service Patient PCP Hospital Lab Specialist SNF RegulationsTechnology Needs PenaltiesComplex Patients
  5. 5. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |8 Provider Consolidation and Shifting Care New Types of Organizations: ACOs, IDNs, CINs, PCMHs, and Others Hospital Primary Care Provider Aftercare Provider Clinic PayviderSNF Testing Center Provider Groups Home Care Patient
  6. 6. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |9 How Plans Have Thought about Providers So Far Analytics for Value Based Care • New types of analytics are important under Value Based Care: – Quality: How is the provider doing at closing gaps? – Care Pathways/Bundles: Ensure providers render integrated care – Enhanced Disease Management: Wellness Programs, Incentive Optimization – Referral Patterns: Are providers referring to most efficient other providers (specialists, facilities, etc.) Historic Analytics Key Components of legacy thinking • Unit Cost – Metrics focus on how a provider compares to others on various services – Strategies to Control increases include : Contracting with OON providers, Payment Methodologies, Medical Management (Step Therapy, Lower Levels of Care) • Utilization – Focus on items increasing faster than average and how to manage – Medical Management, Pre-Certification, Formularies, Member Cost Shares
  7. 7. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |10 Impact of These Shifts Customers Consumers become money managers Increased focus on wellness & prevention Aging Populations with Chronic Conditions Providers New reimbursement models that shift risk & favor value over volume Changing organizational structures (Hospitals employing physicians) New Services (Personalized Medicine) Shift care to lower intensity settings: outpatient, nursing facilities and home Changes create new risks for payment errors, abuse, and fraud Efficiency Quality Satisfaction Employers Members
  8. 8. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |11 MANAGING COST & CARE ACROSS THE CONTINUUM
  9. 9. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |12 Thriving in a Value-Based Reimbursement World • Understanding How Individuals Vary in Impactability and Population Health Needs Identifying Highest Risk Individuals (Illness and Cost) • Utilizing Assessment and Targeting Analytics in Managing Finite Resources Leveraging the Right Resources to Optimize Clinical and Financial Results • Engaging Providers in Managing and Optimizing Network Performance Delivering Care in the Optimal Site of Service and Intensity of Service • Establishing Data Sharing and Transparency Collaborating with Key Stakeholders: Payers, Providers, & Patients Challenge Analytic Needs
  10. 10. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |13 Using Analytics to Better Understand 1. Identify Outliers 2. Engage 3. Measure Results
  11. 11. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |14 The Foundation of Actionable Analytics: Robust Profiles Behaviors & Attitudes Demographic & Attribution Clinical Factors Cost & Quality Utilization Risk
  12. 12. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |15 Example of Personas – Member / Patient Analysis 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 MedianRisk 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
  13. 13. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |16 POPULATION HEALTH ANALYTICS: FINDING MEMBERS YOU CAN IMPACT
  14. 14. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |17 Precise Care: Higher Quality and Lower Costs TOTAL POPULATION ACTIONABLE MEMBERS
  15. 15. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |18 View Members 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
  16. 16. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |19 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
  17. 17. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |20 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 Interventionsfor Diabetes 1 Eye Exam 2 HbA1c 3 Lipid Test 4 Medication Regimen Compliance
  18. 18. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |21 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
  19. 19. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |22 Understanding Patient Risk with Analytics 1 WHERE ARE HIGH RISK PATIENTS? 2 WHAT ARE THE PATIENT PERSONA TRENDS, AND WHAT IS DRIVING THE RESULT? 3 WHAT GAPS IN CARE ARE DRIVING PATIENT COSTS? 4 WHAT ARE MY QUALITY MEASURE GAPS? WHERE DO WE AGREE ON RISK?
  20. 20. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |23 ANALYTICS TO STANDARDIZE CARE AROUND TOP PERFORMERS & WATCH FOR ABERRANCIES
  21. 21. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |24 Network Analytics – The Need Focus resources on areas to improve risk- adjusted utilization against benchmarks in key areas Insight into performance on quality measures Drive improved efficiency (both utilization & unit cost) Demonstrate opportunities to improve network leakage (based on hypothesis of why)
  22. 22. ©2014 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |25 Case Study Evaluation of Provider Quality and Efficiency – Advanced Analysis EFFICIENCY RESULTS QUALITY RESULTS OVERALL RESULTS Provider Analytics: Attribution, Performance Physician ID Total Members Observed Cost/ Member Expected Cost/ Member Efficiency (Observed/ Expected) 00001 53 $11,939 $10,450 1.14 00002 46 $7,987 $9,052 0.88 00003 42 $7,460 $9,925 0.75 00004 39 $14,218 $12,150 1.17 Physician ID Total Members Observed Gap Expected Gap Quality (Observed/ Expected) 00001 53 42% 47% 0.89 00002 46 47% 45% 1.04 00003 42 45% 48% 0.94 00004 39 45% 50% 0.90 Physician ID Total Members Efficiency (Observed/ Expected) Quality (Observed/ Expected) 00001 53 1.14 0.89 00002 46 0.88 1.04 00003 42 0.75 0.94 00004 39 1.17 0.90 Best Combination of Efficiency & Quality
  23. 23. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |26 The Squeeze is On: Increase in Outpatient Care Chronic Condition Management RAC Audits Readmission Penalties Inpatient Care Outpatient Care
  24. 24. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |27 The Outpatient Challenge Various Forces are Driving Treatment to Lower Levels of Care Target • Good/Better Outcomes for Patients • Typically Lower Cost • Increased patient satisfaction COMBINE TO DRIVE EXCESSIVE OUTPATIENT UTILIZATION External Forces Coordinated Care Management, Re-Admission Penalties RAC Short Stay Audits. Internal Strategies Converting Buildings Buying Ancillary Providers Moving Care from Office to OP As Inpatient services are shrinking, Hospitals are Providing more Services in Outpatient Settings. They are also adjusting to Balance Revenue lost from reduced inpatient stays
  25. 25. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |28 • “Improved” efficiency • Unclear if this was a net improvement for the system as a whole • Things not included in bonus might get more expensive • Possible incentive to misrepresent the risk of their population Unexpected Consequences of Value-Based Programs Before Incentive Par Doctor Facility 1 Facility 2 $$$ $ After Incentive Par Doctor Facility 1 Facility 2 $$$ $ Concerns:
  26. 26. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |29 PAYER-PROVIDER COLLABORATION: WORKING TOGETHER TOWARDS COMMON GOALS
  27. 27. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |30 Payer, Provider, and Patient Engagement Am I compensated correctly? Do I have all the data I need? Am I receiving the best care? Transparent Data Sharing Supports Care & Revenue Optimization
  28. 28. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |31 Analytics Driving Engagement Outreach
  29. 29. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |32 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
  30. 30. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |33 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
  31. 31. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |34 Our Vision for Analytics Use new types of data and analytics to build robust views and models based on enhanced member & provider profiles in order to: • Better identify patterns of behavior that merit review • Prioritize limited resources • Enhance provider collaboration to improve shared results • Improve performance overall Goal

×