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Predicting and Preventing Claim FWA with Advanced Profiling & Analytics

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This presentation throws light on:
• New and emerging challenges facing payers, how recent thinking can address those obstacles, and exemplar use cases
• The need to progress beyond claim level analysis for FWA
• How to apply predictive models and rich data benchmarks to better identify true outliers
• The impact of analytics and profiling on FWA
• The importance of leveraging predictive models, member/provider profiles, and rich multi-source data to identify FWA patterns, reduce errors, limit false- positives, and improve overall performance recoveries
• Innovative approaches which leverage data analytics to drive behavior modification
For more information on our FWA solutions or reimbursement solutions, please visit: http://www.sciohealthanalytics.com/offerings/solutions/reimbursement-optimization

Published in: Healthcare
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Predicting and Preventing Claim FWA with Advanced Profiling & Analytics

  1. 1. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |1 SCIO Health Analytics® Predicting and Preventing Claim FWA with Advanced Profiling & Analytics September 15, 2016
  2. 2. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |6 Background: New Market Drivers COST PRESSURE MARKET DRIVERS SIZE & COMPLEXITY OF DATA VALUE-BASED CARE MARKET STRATEGIES NEW/DIFFERENT GATEKEEPERS EPISODE & RISK BASED CONTRACTING PUSH TO LOWER LEVEL OF CARE DATA SHARING PROVIDER CONSOLIDATION VERTICAL INTEGRATION FLEXIBLE DELIVERY & ENGAGEMENT MODELS INSIGHTS & TECHNOLOGY AS A KEY ENABLER New Data Types, Integration of claims & clinical data, Lack of Standardization Shifting risk, Value not volume, Purchase of practices by IDNs, Change in POS New services (Personalized Medicine, Hep-C. etc.), Aging Population, New payment models
  3. 3. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |7 Background: U.S. Healthcare is a Huge Industry $3 Trillion IN TOTAL EXPENDITURES 17.5 Percent OF GDP Based on 2014 Data from CMS and the CDC
  4. 4. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |8 Background: Data Explosion in Healthcare • Each Group of Stakeholders – providers, payers, patients, employers, pharmaceutical and medical device companies – generate huge amounts of data • Previously, this data was in separate pools. However, with additional technology and devices in service, other stakeholders’ data is e o i g more available to each of the various players
  5. 5. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |9 Background: How We’ve Combatted FWA So Far Referrals • Various types of analysis have existed for at least 15 years, but older methods have been fairly straightforward: – Claim by Claim: Virtually every payment integrity program looks at claims one by one, usually focusing on facility claims since those pay more – Provider Patterns: SIUs tend to look at things by provider compared to others (rate of certain codes compared to peers, etc.) – Basic Member Analytics: Anecdotal at best (drug seeking behavior, VERY high utilizers, etc.) Basic Analytics • Information from members, provider offices, and other parts of company – Certainly useful, but can be very anecdotal (single claim / event)
  6. 6. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |11 THE NEED FOR ANALYTICS
  7. 7. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |12 The Need for Analytics • Most players have been looking at FWA in healthcare for decades • There are lots of people with extensive experience with healthcare claims and knowledge of ways providers might try to abuse the system • Despite that, estimates still indicate that a substantial portion of payments for healthcare in our country are incorrect. Current methods ARE NOT WORKING!! CMS’ Co prehe sive Error Rate Testi g CERT • Overall Medicare FFS Improper Payment Rate: – 12.1% in 2015 ($43.3 billion) – 12.7% in 2014 ($45.8 billion) – 10.1% in 2013 ($36.0 billion) – 8.5% in 2012 ($29.6 billion) – 8.5% in 2011 ($28.8 billion) $183.5 Billion In 5 years
  8. 8. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |13 The Need for Analytics (Cont’d) • With creation of more sophisticated schemes of abuse, continued growth of new services, and ability to receive millions in payments in short periods of time, EXPERIENCE ALONE IS NOT ENOUGH to address the complex ways FWA occurs • This is in part because of the COMPLEXITY OF THE TYPES OF SERVICES that can be required, and the CONTINUING INNOVATION IN TREATMENTS AND BILLING SCHEMES for emerging conditions • Given all of this, the capability to SIFT THROUGH HUGE DATASETS QUICKLY AND ACCURATELY AND IDENTIFY SITUATIONS THAT MERIT REVIEW is becoming more and more important • We need ongoing process/model improvements through analytics
  9. 9. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |14 Our Vision for Analytics Use new data types and analytics to build robust predictive models based on member & provider profiles in order to: Goal • Identify patterns of fraud, waste and abuse that are actionable • Reduce errors & false positives • Prioritize limited resources • Improve performance overall COMBINE TO FIND ADDITIONAL PROBLEMS PROVIDER ANALYTICS Based on Claims, Member Risk, Demographics, Linkage, Etc. MEMBER ANALYTICS Risk Based on Co-Morbidity, Propensity, Socio-Demographics
  10. 10. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |15 Our Vision for Analytics (Cont’d) HRA and Survey Data Lab & Biometric Data Activity Data Medical Claims Social Demographic Data Enrollment Data Rx Claims EHR & Clinical Data Provider Data HISTORICAL DATA ACQUISITION DATA ANALYSIS & TRANSFORMATION Patient & Provider Profiles ANALYTICS ENGINE Linkage / Referrals Patient Population Audit Outcomes Billing Behavior CLINICAL & PAYMENT OPTIMIZATION SELECTION & RECOMMENDATION   CLAIMS TO BE REVIEWED FEEDBACK LOOP
  11. 11. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |16 VERY LOW LOW MODERATE MEDIUM MEDIUM HIGH HIGH VERY HIGH Example of Provider Profile No of Providers Upheld Rate Appeal Rate Average Overpayment Average Claims 19.2% 25.1% 26.0% 27.7% 28.2% 35.6% 61.4% Error Impact 1,534 46 113 50 294 604 846 3 99 64 131 37 16 5 $270 $1,612 $1,655 $1,531 $1,662 $1,586 $2,737 13.9% 53.3% 52.2% 54.4% 55.8% 51.6% 47.8% 7.0% 27.0% 30.6% 34.1% 30.0% 27.0% 22.0%    
  12. 12. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |17 Insights from Benchmark Informs Provider Profile SIGNIFICANT VARIATION IN COST OF PCP VISIT BY STATE DISTRIBUTION OF AVERAGE BILLED AMOUNT PER PCP VISIT DISTRIBUTION OF MEMBER AGE POWERFUL INSIGHTS • National representative data – Ability to filter by age group, gender, income, zip3, state, region – Comparative benchmark to help provide insights on utilization, metrics etc. * Limited to commercial insurance PCP includes – IM, FM, Peds, and OB/GYNs
  13. 13. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |18 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 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
  14. 14. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |20 EXEMPLAR USE CASES • Opioid Challenge • Skilled Nursing Facility • Home Health
  15. 15. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |21 Comprehensive Analysis MEMBER PROFILE CLAIM PROFILE PROVIDER PROFILE 360° ANALYSIS
  16. 16. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |22 • Cost Impact – The largest number of fraud, waste and abuse cases in 2016 involve prescription drugs, with the opioid epidemic leading the way • Regulatory Reaction – The passing of H.R. 6, The 21st Century Cures Act, in May 2015, allows the “lock-in” of identified Part D members to a specific pharmacy and/or prescriber (house bill) – Senate bill S. 1431 has also been introduced: Prescription Drug Abuse Prevention and Treatment Act of 2015 The Opioid Challenge Source: https://oig.hhs.gov/testimony/docs/2016/maxwell-testimony05242016.pdf $88 Million TOTAL COST RELATED TO FWA IN U.S. (2016) 66% ATTRIBUTED TO PRESCRIPTION DRUG ABUSE
  17. 17. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |23 Opioid Challenge – Hot Spots
  18. 18. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |24 Solving the Opioid Challenge
  19. 19. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |25 Benefits of the Provider Profile MEMBER PROFILE CLAIM PROFILE PROVIDER PROFILE 360° ANALYSIS
  20. 20. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |26 Provider Risk Model – Applied to SNF Stays Provider Profile Member Clinical and Financial Risk Profile Claim Attributes PROVIDER BEHAVIOR PROFILE CLAIMS DATA Provider Segment FWA Factors Claim Score
  21. 21. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |27 Analytic Depth from Provider Profile New Analytics Components • Risk clustering • Patient Conditions & Propensities • Case Mix Adjustment • Quality and Efficiency of Providers • Facility Type, size, etc.
  22. 22. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |28 Month 1 Last Month Ongoing Scoring Model Month X Month Y INITIAL BILL FINAL BILL Claims are locked based on ranking of all episodes in Month X Episode scored based on all claims with the same Stay ID OPPORTUNITY SCORE: 0.5 Claims with the same Stay ID will be locked Episode Scored based on all claims with the same Stay ID OPPORTUNITY SCORE: 0.6 The claims are locked d given the ranking of all episodes The locked claims with final bills will be prioritized for selection Final episode scored using All claims OPPORTUNITY SCORE: 0.65 Claim not selected given ranking of episodes OPPORTUNITY SCORE: 0.2 First claim scored • An audit can be performed only after the episode is complete. • The initial claim can be scored and final score predicted to select stays for audit • As additional claims come in, the final predicted score is refined • There is a very high correlation between the initial score and the final score
  23. 23. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |29 Benefits of the Member Profile MEMBER PROFILE CLAIM PROFILE PROVIDER PROFILE 360° ANALYSIS
  24. 24. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |30 Selection in Home Health Services – Member Profile • Typical Findings Rate = 30% • Typical Overpayment = $1600 • Applied Risk Score to bucket claims • Noted that Average Overpayment tends to increase as risk goes up
  25. 25. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |31 Home Health – Analysis of Impact of Member Risk Paid Amount Outlier Claims Outlier Patients Outlier Claims Outlier Claims Audited Outlier Claims with Overpayment Audited Outlier Claims / Total Outlier Claims Hit Rate Average Overpaid Amount Total Overpaid Amount Top 3% 406 610 477 144 78.2% 30.2% $3,000 $432,000 Top 2% 275 443 353 111 79.7% 31.4% $2,916 $323,699 Top 1% 147 271 218 76 80.4% 34.9% $3,046 $231,470 • For one month of claims data, pulled the top 3% outlier claims (yielded 610 claims) • Among them, 477 (78.2%) were audited, which yielded: – 30.2% hit rate – $3,000 average overpaid amount per finding claim – $432K total overpaid amount HYPOTHESIS – Member risk can be used as an input to refine selection • Claims showing high intensity of services on members with low risk will have different audit outcomes
  26. 26. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |32 Home Health – Impact of Member Risk • Lowest risk members, for very high paid services, had higher hit rate • Analyze to identify what about those low risk members predicts a hit • Will not be all claims, but pockets of claims where providers have issues – Use Provider Profile as well
  27. 27. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |33 Predicting and Preventing Claim FWA with Advanced Profiling & Analytics • Cost Pressures • Value-Based Care • Abundance and Complexity of Data Market Drivers • Experience alone is no longer enough to keep pace • Continuing innovation in treatments & billing schemes • Sift through large & diverse data sets to identify outliers • Build member and provider profiles that improve over time Role of Analytics • Opioids – analyzing member drug seeking behavior • SNF – provider characteristics improve prediction • Home Health – member risk impacts prediction Use Cases

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