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Outcomes-Based Contracting: Empowering Providers to Create Tailwinds during Paradigm Shift

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This presentation throws light on:
• Key considerations to know and understand during the current outcomes-based paradigm shift
• Importance of reliable, pristine data to risk-based outcomes
• How to use advanced analytics to mitigate clinical and financial risk
• Importance of having the right data to create a patient level view
• Key areas in which predictive and prescriptive analytics can help providers focus on high value opportunities that improve patient outcomes
For more information on our Care Management and Network Management solutions, please visit:
http://www.sciohealthanalytics.com/offerings/solutions/care-optimization
http://www.sciohealthanalytics.com/offerings/solutions/network-optimization

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Outcomes-Based Contracting: Empowering Providers to Create Tailwinds during Paradigm Shift

  1. 1. |1©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved.©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. DR. KEVIN KECK Chief Evangelist SCIO Health Analytics DAVE HOM Chief Medical Officer SCIO Health Analytics
  2. 2. |2©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. MARKET HEADWINDS Why should a plan reimburse for the product and how much should a patient pay for these services Plans and Providers will require outcomes based risk strategy to align the financial and clinical risk Compliance- how do you plan to manage this across the patient care pathway Understanding the effect of the intervention to improve patient risk Pay for outcomes- how will you show the value to determine causality
  3. 3. |3©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. KEY CONSIDERATIONS FOR VALUE-BASED CONTRACT DESIGN
  4. 4. |4©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. KEEP IN MIND… • Transition to risk-sharing contracts will be gradual • Significant compensation plan realignment is needed for employed physicians and owned practices • Compensation models should be adaptable over time, valuable to the providers, include easy to understand methodology, etc. Compensation Plans to Support the Triple Aim
  5. 5. |5©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. KEEP IN MIND… • Simple, understandable, doable • Determine and agree on methods of measurement • Increase goals, year over year Contract Strategies
  6. 6. |6©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. KEEP IN MIND… • Provider trust • All data points will be questioned – From Claims to EMR to Social Economic • Must deliver insights to providers on: – Managing risk, Impactability, Gaps in care Pristine Data is a Must
  7. 7. |7©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. MEANINGFUL DATA FOR VALUE-BASED CONTRACT SUCCESS
  8. 8. |8©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. ALL HEALTHCARE ORGANIZATIONS STRIVE FOR… HOLISTIC 360o VIEW
  9. 9. |9©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. USE OF DATA TO CREATE AN ENHANCED VIEW OF THE PATIENT/MEMBER FOR ENGAGEMENT DATA MART CLAIMS RXELIGIBILITY SOCIAL ECONOMIC LAB EMRFITNESS DEVICES DATA MART USES CASES What is The Total Cost of Care By Patient and Provider What Risk Will A Member Have Without Claims Data What Lab Data Will Enhance The Prediction of Financial Risk With Claims, Data What is The Financial Risk of The Member Ability to Predict Which Members Will Be Compliant What New Data Can Be Used To Understand Compliance What Data in The EMR Enhances The Prediction of Risk Data Intuitiveness To Reduce False Positives How Often Should The Data Be Updated 1 2 3 4 5 6 7 8 9
  10. 10. |10©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. PROFILES AS AN ANALYTIC FOUNDATION- DATA LAYERING SEQUENCING Behaviors & Attitudes Demographic & Attribution Clinical Factors Cost & Quality Utilization Risk
  11. 11. |11©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. THE NEW WORLD OF ANALYTICS Eligibility Data Zip Code Data RISK Health Care Services CLAIMS DATA EMR Data Lab Values Medical Data Rx Data Impactabilty Model Surgical Models • Internet of Things (IOT) • Wearables • Weather patterns • Gross Domestic Product (GDP) • Provider clinical data from devices • etc., NEW DATA SETS Intevenability Models of Do–ability Propensity to Consume
  12. 12. |12©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. ADVANCED ANALYTICS MITIGATE CLINICAL & FINANCIAL RISK POPULATION HEALTH NETWORK OPTIMIZATION
  13. 13. |13©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. THE INDUSTRY JOURNEY FROM DESCRIPTIVE TO PREDICTIVE TO PRESCRIPTIVE ANALYTICS –ONE COMPANY’S EXPERIENCE Predictive Pop Health • Claims data for commercial populations • Care/Case management High Value Services- Behavioral Economics models • Define high value services for 17 conditions • Measured reductions in member out of pocket expenses • Measured health improvements • Reductions in hospital events Low Value services Behavioral Economics –Grey Zone Med • Identify the specific low value- Increase member out of pocket expense • Reduced utilization Preference Sensitive Surgical procedures • Identify 3 conditions frameworks-Knee, low back and hips Enhanced Commercial predictive models to be more prescriptive at the member level • Adjusted the risk models for patient gaps in care • Define which care gap had the greatest impact on avoidable hospital events • Developed HCC Coding / Risk Adjustment Models • Developed Medicaid Con current and Prospective Risk model • Linked member risk to Fraud / Waste / Abuse to identify more dollars 2005 2007 2009 2012 2015 2016 2017
  14. 14. |14©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. MITIGATE CLINICAL & FINANCIAL RISK BEYOND TRADITIONAL RISK ANALYSIS Where can I have the greatest impact?1 Who is at risk to undergo an avoidable surgery?3 What conditions and subsequent interventions represent the highest opportunity value? 2 Which consumer types comprise my membership? How do they compare? Which programs are best? 4 Can I drill down to the member/patient level?5
  15. 15. |15©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. WHERE CAN I HAVE THE GREATEST IMPACT? IMPACTABLE POPULATION ANALYSIS 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. |16©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. WHAT CONDITIONS & INTERVENTIONS WILL DELIVER GREATEST VALUE? Which conditions can we impact, with the least complex interventions?
  17. 17. |17©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. WHO IS AT RISK TO UNDERGO AVOIDABLE PROCEDURES? Foresight to Reduce Invasive Surgeries and Deliver Valuable Care PREFERENCE SENSITIVE CONDITIONS: Ailments in which there are no definitive clinical guidelines and multiple treatment options exist EXAMPLE TREATMENT PHASES FOR KNEE - OSTEOARTHRITIS Identifying members in phases 1 or 2 and informing them and their providers of other treatment options reduces the likelihood of a member progressing to a high cost phase 3 Procedure THE PST MODEL IDENTIFIES AND STRATIFIES MEMBERS WITH PREFERENCE SENSITIVE CONDITIONS ACCORDING TO THEIR: • Risk of avoidable surgery in the next 12 months • Episode cost • Remaining time to utilize a less invasive option Phase 1 – Severity 1 Selected Procedures Phase 2 – Severity 2 Selected Procedures Phase 3 – Severity 3 Selected Procedures • X-ray of the Knee • CAT Scan of the Knee • MRI of the Knee • Physical Therapy • Knee Brace … • Diagnostic knee Arthroscopy • Arthroscopic Drainage of knee • Arthroscopic knee Surgery – Chondroplasty • Arthroscopic knee Surgery – Synovectomy … • Partial knee Replacement • Total knee Replacement • Total knee Replacement Revision … Patients with preference sensitive conditions often display a selection bias towards surgery based on the assumption that it provides the highest quality of care. These invasive surgeries are expensive and in many cases the less-invasive treatment provides equal or greater outcomes with fewer complications.CHALLENGE
  18. 18. |18©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. WHO IS AT RISK TO UNDERGO AVOIDABLE PROCEDURES?
  19. 19. |19©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. SCIO’s consumer segmentation analytics blends client data with supplemental data from SCIO® to map and analyze members according to seven distinct consumer types.* Viewing membership this way helps organizations develop, evaluate, and market care management programs to the most appropriate members. CONSUMER TYPES • Healthy and Affluent • Balanced Adults • High Utilizers • Quality Driven • Cost Conscious • Chronic Older Adults • High Cost Baby Boomers CONSUMER SEGMENTATION ANALYTICS Profiling Populations to Better Understand Propensity for Care Utilization Predictive analytical models require robust data set to yield accurate insights. Common issues from relying on medical and pharmacy claims data alone: • Using less than 12 months of claims data (e.g., new members) • Missing claim data (e.g., members who receive out-of-network care) • Insufficient volume of claims data (e.g., members with low utilization rates)CHALLENGE * Data from SCIO® can also be used by clients on its own, with client data added at a later date.
  20. 20. |20©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. WHICH CONSUMER TYPES COMPRISE MY POPULATION? 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
  21. 21. |21©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. ILLUSTRATIVE SEGMENT PROFILE HIGH UTILIZERS 23% 58% 12% 7% 65+ 55-64 35-54 16-34 Age Group DESCRIPTION Low-risk adults, mostly college level education, white collar employees with moderate income. They happen to be high utilizers of healthcare services given that they are low-risk and the average chronic conditions is less than one. INTERVENTION: Low risk with high utilization, so need education & steerage Demographic Attributes % Above Poverty Level 88% % Blue Collar Employed 16% % Single Family Dwelling 33% % Married 38% % Household with children 30% Clinical Attributes IP Utilization 0.10 ER Utilization 0.34 # Average Chronic Conditions 0.85 Paid Amount PMPM ER $14 Paid Amount PMPM IP $147 Gender Education School College Individuals with Income Level > $50K Median Age 43 $216K 47% Channel Preference Frequent Spending Median Home Value Socio-Economic Score 82 Spending Pattern TextE-mail 70 100 53% 33% 12% 2% $200K+ $100K - $200K $50K - $100K Less than $50K Estimated Income Low Risk Median Risk Prospective Score 0.71
  22. 22. |22©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. CAN I DRILL DOWN TO THE MEMBER/PATIENT LEVEL? Member ID In Last 12 Months Cost Incurred in Last 12 Months IP prob IP prob IF all the gaps are closed IP Difference ER prob ER prob IF all the gaps are closed ER Difference Impactability Score # Hospitaliz ation # ER Visits InPatient (PMPM) ER (PMPM) OutPatient (PMPM) Pharmacy (PMPM) 1 2 3 $15,124 $836 $3,866 $95 39.67% 35.75% 3.92% 71.86% 62.96% 8.90% 7.12 2 1 0 $762.4 $1,233.6 $18.3 0.26% 0.17% 0.09% 8.11% 4.93% 3.18% 0.22 3 0 0 $42.0 $12.7 0.35% 0.23% 0.11% 6.42% 2.98% 3.44% 0.21 4 1 0 $7,620 $4,020 $1 0.94% 0.94% 0.00% 13.85% 12.79% 1.06% 0.09 5 3 0 $10,563 $3,866 $50 26.66% 26.66% 0.00% 37.45% 13.09% 24.35% 0.48 6 0 1 $81.5 $636.0 $82.1 0.58% 0.39% 0.19% 12.17% 7.53% 4.64% 0.48 Member ID Risk Score Impactability Score Gap1 Gap2 Gap3 1 9.87 7.12 HTN_Beta_Block_PDC80 CHF_Beta_Blocker_PDC80 Depression_Antidepressant_PDC80 2 1.91 0.22 Antihypothyroid_Med_PDC80 Hypothyroid_Follow180d Diabetes_Medical_Attention_to_Nephropathy 3 1.38 0.21 HTN_RASA_PDC80 HTN_Diuretics_PDC80 Diabetes_Eye_exam 4 3.62 0.09 Hyperlipidemia_PCP_180d RA_Rheumatologist_Visit Diabetes_RASA_PDC80 5 1.37 0.48 Antihypothyroid_Med_PDC80 Hypothyroid_Follow180d HTN_RASA_PDC80 6 5.45 2.81 Diabetes_Creatinine_clear Diabetes_Oral_Antidia_PDC80 Diabetes_Eye_exam
  23. 23. |23©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. USE CASE HOW ARE POTENTIAL SAVINGS CALCULATED? Sample Member Scenario LIKELIHOOD OF IP ADMISSION • Before gap closures 99.04% • After gap closures 87.94% – Reduction in probability 11.10% • Potential cost savings in the next 12 months – Patient is in IP strata 20 – Predicted average cost for future IP Admission: Multiplied by the Inpatient Probability Reduction: $54,920*11.10% = $6,093.76 LIKELIHOOD OF ER • Before gap closures 95.81% • After gap closures 58.98% – Reduction in probability 36.83% • Potential cost savings in the next 12 months – Patient is in ER strata 20 – Predicted average cost for future ER visits : $5,581 – Multiplied by the ER Probability Reduction is $5,581*36.83% = $2,055.53 LIKELIHOOD OF IP ADMISSION LIKELIHOOD OF ER • 56 Year old male • Had IP utilization in history; no ER utilization in history • Risk Score 17.86 MEMBER BEFORE GAP CLOSURE • Conditions-CAD, CHF, Cancer, Diabetes and Hypertension • List of open care gaps • Gap_CAD_Beta_PDC80 • Gap_CHF_Beta_Blocker_PDC80 • Gap_Cncr_Patnt_Opoid_Mntrd • Gap_Diab_Beta_Bloc_PDC80 • Gap_Diab_Creatinine_clear • Gap_Diab_Oral_Antidia_PDC80 • Gap_Diab_RASA_PDC80 • Gap_Diab_Sulfonyl_PDC80 • Gap_HTN_Beta_Block_PDC80 • Gap_HTN_RASA_PDC80 $54,920 $5,581
  24. 24. |24©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. • Strong headwinds facing providers • Push toward outcomes-based contracting • Need to structure value-based contracts to incentivize interested parties • Heightened need to manage clinical & financial risk • Pristine data is required • Predictive and prescriptive analytics deliver insight into high value- opportunities SUMMARY

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