Expanding frontiers the state of payment integrity in 2018

1. |1 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved.©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved.

3. |3 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. Questions to Answer Before Any Expedition • Where are we going? • Where are we departing from? • What is our path? Are there obstacles? • Who is coming with us? • When are we leaving/arriving? • How are we getting there? Why are we going?

4. |4 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. DEFINING A “WHY” Lewis and Clark’s ‘Why’ = Expand U.S. Economic Opportunity Via a Route to the Pacific Ocean

7. |7 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. HEALTHCARE WASTE IS AT EPIDEMIC PROPORTIONS Waste is a $1 Trillion Problem Driven by the Way US Healthcare is Organized, Delivered, and Paid For Clinical Waste* ($500B) • Duplicate tests & procedures • 55 % of antibiotic Rx are medically unnecessary • Use of ER for non-emergent conditions= $21B/yr. Administrative Complexity* ($150B) • US hospitals spend ¼ of budget on billing and administration • Complexity related to administering claims=~$91B in waste alone Price Variation* ($50B) • Skyrocketing drug costs- generic vs. brand • Medicare dominant payer for DME- 20% higher • Consolidation drives decreased competition Inaccurate Payments* ($175B) • Overpayments due to clinical appropriateness and contract compliance • Fraudulent billings~3-10% of total health spend • Increased coding complexity “Waste = healthcare spending that can be eliminated without reducing the quality of care” - Network for Excellence in Health Innovation (NEHI) * Industry References Provided $1Trillion Market Opportunity

8. |8 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. Headwinds PAYMENT INTEGRITY HEADWINDS Diminishing Returns from “Business as Usual” Provider Consolidation • Increases Provider Negotiating Power • Increased Sensitivity Around Perceived Provider Abrasion • Creates Opportunities for Cost Shifting Increased Complexity of Overbilling Schemes Aging Member Populations with Multiple Chronic Conditions Rising Healthcare Prices • High Cost Advanced Treatments • Prescription Drug Costs Size and Complexity of Data • Variation of Data • Mergers and Acquisitions • Government and Regulatory Changes

9. |9 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. Tailwinds PAYMENT INTEGRITY TAILWINDS Technology Enabled Capabilities • Speed: Daily Reviews • Throughput: Much higher volumes • Accuracy: Analytics- Assisted Selection Third-Party Support • Strategic Partner to Support a Health Plan’s Core Competencies • Data Science and Algorithm Development • Blended Data Sets with Socioeconomic Information

11. |11 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. High Dollar Claim Review • Internal review of claims above a certain cost threshold • Generates significant ROI but limited by amount of staff and not scalable Fraud, Waste, and Abuse Detection • Overuse may be abrasive to providers • Staff must be credentialed • Fraud can be difficult to collect on Coordination of Benefits • Can be applied prospectively and retrospectively • Unlike eligibility, benefit coverage can fluctuate throughout the year (e.g. newly eligible for Medicare, new job, etc.) Subrogation • Recovery dollars are very predictable with ability to prevent (if allowed) • Whereas COB is more immediate, Other Party Liability can take years to resolve TRIED-AND-TRUE PAYMENT INTEGRITY TOOLS

12. |12 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. Claim Editing • As claims are received, a set of rules (industry standard or custom to health plan/LOB) flags incorrectly billed claims that need edits and additional manual review • Can also be completed retrospectively • Must consider the cost/benefit of manual review • Individual claim focus limits ability to address root causes Data Mining • Expands beyond claim editing to look across claims using industry and business rules to uncover patterns • Identifies systemic problems across providers, claims, policies, etc. Retrospective Medical Record Reviews • A deeper analysis beyond the data presented on the claim (e.g. dates, services, and diagnoses) to include documentation from the provider • Can determine level of care, billing, and payment errors • Requires more effort, time, and expertise • Can be abrasive to providers and often carries a request for reconsideration/appeals. TRUSTWORTHY TOOLS RIPE FOR INNOVATION

13. |13 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. Prepayment Auditing • Use profiles to identify high risk claims (not just high dollar) before they are paid to avoid overpayments • Since it’s costly to re-adjudicate claims, it’s beneficial to ensure claim payment is accurate the first time around • Limitations include staffing concerns and prompt payment regulations Provider Behavior Modification • Most cutting edge (“frontier”) solution. • Identify risky providers using analytics, predictive modeling, and risk scoring • Change their billing behaviors by engaging with education, audits, and support. • Benefits: Addresses the root causes of billing errors. Highly effective on lower dollar, higher volume claims that are not typically audited. • Concerns include possible provider abrasion and it can be harder to quantify ROI (requires a longer period of time, need to trend provider’s utilization, and must take into consideration other factors that may not be related to the program) RECENT INNOVATIONS THAT UNLOCK NEW CAPABILITIES AND INCREASE ROI

14. |14 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. A TREASURE MAP FOR MOST HEALTH PLANS GOAL: Eliminate Payment Errors with Proactive Approaches Supported by Advanced Reactive Tactics 1Longitudinal Data Mining 2 Post-Payment Outpatient/ Ancillary Audits 3 Prepayment Clinical Auditing 4 Provider Behavior Modification 2.5Update Systems & Policies based on Overpayment IdentificationCommon Starting Point: The “Cat-and-Mouse” Game of Collecting Overpayments After the Fact

15. |15 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. LONGITUDINAL DATA MINING GOAL: Eliminate Payment Errors with Proactive Approaches Supported by Advanced Reactive Tactics 1Longitudinal Data Mining

16. |16 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. USE A HOLISTIC DATA MINING PHILOSOPHY Thorough Client Assessment, Planning, Consultation and Support Industry Standard Client-Specific Analytics/ Automation Customized Solution Design Visualization, Insights, Workflow, & Reporting

17. |17 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. PRECISE SELECTION REDUCES FALSE POSITIVES GOAL: Use New Data Sources & Novel Analytic Models to Precisely Select Inaccurate Claims

18. |18 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. DATA MINING SUPPORTED BY A HYBRID ANALYTICS APPROACH Providers Recipients Facilities Claims Referrals Audit Flags Intra-Agency 3rd Party Data Enterprise Data Suitable for KNOWN patterns Suitable for UNKNOWN patterns Suitable for ASSOCIATIVE LINK patterns Suitable for COMPLEX patterns Flag improper claims based on known issues Rules Anomaly Detection Link Analysis Predictive Models Detect Individual and aggregated abnormal behaviors Knowledge discovery through associative link analysis Predictive assessment HYBRID APPROACH Proactively applies all 4 techniques at provider, recipient, facility and network levels Examples: • Same Provider Affiliation with par/npar providers • Benefits administered differently for same plans Example: • Member Risk for Long Term Disability Examples: • Abnormal Allowed Amounts on Same Services for Same Provider • Provider Billing Patterns • Keying Errors Examples*: • Duplicates • Overlapping Services • Unbundling Contract Terms Payment Policies Eligibility Selection Based on Four Profiles PROVIDER PROFILES MEMBER PROFILES PAYER PROFILES CLAIM PROFILES

19. |19 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. LONGITUDINAL DATA MINING A wider view across claims over time enables pattern identification that is missed during one-by-one snapshots PATTERNS FOUND VIA LONGITUDINAL DATA MINING • Repeated issues identified for the same provider • Mistakes at the same point in the adjudication process • Analysis by provider: • Overriding adjudications • Inappropriate authorizations • Questionable coding and billing patterns • Double billing Home Health Per Visit Payment Above Allowed Amounts Payment Within Allowed Amounts JAN. FEB. MAR. APR. MAY JUN. $500 $200 $200$200$200 $200 $200 $200$200$200 $200 $200 $200$500$200 $200 $500 $200$200$200 $500 $200 $200$200$200 $200 $200 $500$200$200 $200 $200 $200$200$200 $500 $500 $500$500$500

20. |20 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. DATA MINING CONCEPT APPROVAL PROCESS A Little Extra Effort Up Front Goes a Long Way Towards Optimizing Data Mining Programs Concept Identification • Concepts generated after a review of claims processing policies, provider contracts, and CMS guidance • 20% review on 2-3 months of data • Extrapolate anticipated outcomes Sampling Shared with Client • Client provides feedback on any provider exclusions, timeframe limitations, or other important parameters Concepts Refined • Changes based on client feedback ensures: − High accuracy (98%) − Reduced provider abrasion − Maximum ROI − Alignment with client so explanations to providers are consistent

21. |21 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. DATA MINING AUTOMATION Auditors Validate Results of Concepts • Review key information to confirm overpayments − Data − Claims Processing Systems − Pricing Systems − Etc. After 6 Months, We Transition Oversight to an Automated Process Automation Techniques Used • Robotic Process Automation • Screen Scraping • Macros Quality Control • A specific % is reviewed with each production run before it is deployed • This gate ensures nothing has changed before the entire set is processed

22. |22 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. OPTIMIZING POST-PAYMENT AUDIT PROGRAMS GOAL: Eliminate Payment Errors with Proactive Approaches Supported by Advanced Reactive Tactics 2Post-Payment Outpatient/ Ancillary Audits

23. |23 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. THE RISE OF OUTPATIENT CLAIMS Increased Outpatient Claims Payers push outpatient care as cost effective site of service Industry focus on value-based reimbursement and episodes of care Patient Satisfaction Advances in Treatments

24. |24 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. FRAUD/WASTE/ABUSE GROWTH INCREASINGLY OCCURS OUTSIDE OF THE ACUTE CARE SETTING Chronic Condition Management RAC Audits Readmission Penalties Inpatient Care Outpatient & Ancillary Care

25. |25 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. THE OUTPATIENT CHALLENGE Ambulatory Payment Classification (APC) Infusion Radiation/Oncology Trauma Interventional Radiology Wound Care • Outpatient settings viewed in two ways: the future of care and also a path of less resistance to revenue • Converting buildings & acquiring ancillary providers Providers • Overpayments are more difficult to identify for a variety of reasons, including: high volume of claim, diversity of treatments covered, and multiple payment methodologies involved • Two common obstacles are: − Lack of internal audit team bandwidth to review outpatient claims − Overreliance on policy design, pre-authorization, and/or data mining Health Plans Leading Areas of Error:

26. |26 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. FINDING ERRORS IN OUTPATIENT CLAIMS Review medical records to ensure coding is documented and billed appropriately Modifiers (Abuse of 59 Modifier) Units CPT/HCPCS Code Charge to Payment Multiple Surgical Units Medically Unlikely Edits High Dollar Claims (Causing Claim to Pay Outlier) Trauma Activation Code Upcoding to Complex Procedures Inconsistency Between Surgeon and Hospital Coding Cancelled Procedures Observation Time

27. |27 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. PREPAYMENT MEDICAL RECORD REVIEWS GOAL: Eliminate Payment Errors with Proactive Approaches Supported by Advanced Reactive Tactics 3 Prepayment Clinical Auditing

29. |29 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. PREPAYMENT MEDICAL RECORD REVIEWS Prevent overpayments before they occur  Reduce excessive reliance on any one payment integrity tactic  Lessen provider abrasion  Increase savings on both medical and administrative costs  B E N E F I T S CHALLENGE: Apply the rigor of clinical review typically reserved for post-payment audits before payments are sent

30. |30 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. ROBUST CLINICAL REVIEW MAXIMIZES OVER-BILLING FINDINGS Four Levels of Clinical Review Completed by Clinicians Compliance/ Documentation Level of Care Site of Service Comprehensive/ Medical Necessity 1 2 3 4

31. |31 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. PROVIDER BEHAVIOR MODIFICATION GOAL: Eliminate Payment Errors with Proactive Approaches Supported by Advanced Reactive Tactics 4 Provider Behavior Modification

32. |32 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. PROVIDER BEHAVIOR MODIFICATION Reduce Over Utilization and Prevent Billing Errors of High Volume / Low Cost Claims by Addressing Root Causes SIX-STEP PROCESS Validate Suspects Based on Stratified Audits • Upcoding • Utilization • Billed Amount • Institutional and Professional Claims Identify Outliers Based on Cost and Utilization • Volume • Findings rate • $/Claim • Disputes and Overturn rate Engage Providers to Promote Desired Behaviors • Benchmark Reports & Scorecards • Incentives • Education • Penalties Create Suspect List Based on Peer to Peer Analysis • Specialty and Subspecialty • Geographic • Member risk • Payment Methodology Measure and Rank Providers • Provider Score • Provider Rank Monitoring • Partnering with the Payer and Provider • Monthly Scorecard • Measure Results and Outcomes

33. |33 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. PROVIDER BEHAVIOR MODIFICATION Reduce Over Utilization and Prevent Billing Errors of High Volume / Low Cost Claims by Addressing Root Causes Captures Errors that are Currently “Under the Radar” of Existing Payment Integrity Tactics  Cements Positive Lasting Changes to Provider Billing Practices  Reduces Overall Provider Abrasion  Effective for Non-Par Providers  B E N E F I T S

34. |34 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. PROVIDER ENGAGEMENT VIA A SCORECARD & BENCHMARKING Claims Selected for Audit Provider Name & NPI Claims with Findings Total & Average Findings Total & Average $ Collections Disputes Overturns Total & Average $ Overturned Total & Average Selections Total Claims Count Member Risk Comparison to Benchmark

35. |35 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. GOLD-CARDING AND RED-FLAGGING • Work with payers on a clear definition (e.g. errors occur in less than 5% of claims submitted) • Provide benefits like reduced audits for one year and/or waive requirements to obtain pre- authorization • Work with payers on a clear definition (e.g. errors occur in greater than 30% of claims submitted) • Increase scrutiny like number of audits, requiring claims to go through prepayment, renegotiate contracts, assign education, or extrapolate errors across all claims Low-Risk Provider High-Risk Provider

36. |36 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. COMPLEMENTARY TACTICS ENSURE COMPLETE COVERAGE 1. Coordination of Benefits • Eligibility Errors • Excluding Medicare Plans 2. High Dollar Claims • High Cost / Low Volume • Adjudication System Errors 3. Claim Editing • Provider/Procedure Payment Policy Limitations • Adjudication System Errors 4. Longitudinal Data Mining • Inappropriate Billing Trends • Adjudication System Errors 5. Post-Payment Auditing (Clinical Record Review) • Higher Cost / Lower Volume • Adjudication System Errors 6. Prepayment Auditing (Clinical Review) • High Risk Claims • Adjudication System Errors 7. Subrogation • Claims with Specific Diagnoses 8. Provider Behavior Modification • High Volume / Low Cost • Overutilization/Upcoding 9. Fraud/Waste/ Abuse Detection • Suspicious Billing/Utilization Patterns Tried-and-True Solutions Ready for Upgrade Solutions New Innovation Solutions

37. |37 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. A TREASURE MAP FOR MOST HEALTH PLANS GOAL: Eliminate Payment Errors with Proactive Approaches Supported by Advanced Reactive Tactics 1Longitudinal Data Mining 2 Post-Payment Outpatient/ Ancillary Audits 3 Prepayment Clinical Auditing 4 Provider Behavior Modification 2.5Update Systems & Policies based on Overpayment Identification

38. |38 ©2018 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. Questions to Answer Before Any Expedition • Where are we going? • Where are we departing from? • What is our path? Are there obstacles? • Who is coming with us? • When are we leaving/arriving? • How are we getting there? Why are we going?