SAS Fraud Framework for Health Care Evolution and Learnings

Transcription

1 SAS Fraud Framework for Health Care Evolution and Learnings Julie Malida, Principal for Health Care Fraud, SAS Jay King, Manager, Advanced Analytics Lab, SAS Copyright 2009, SAS Institute Inc. All rights reserved.

2 Current Health Care Environment Increasing Fraud, Waste & Abuse The Market Needs Fraud, Waste & Abuse Perpetrators Far more sophisticated organized, patient, sharing of rules Leveraging multiple channels (providers & facilities) at the same time Continuously evolving fraud strategies Current Health Care Fraud Systems Most current detection systems act on claim level data alone Investigations limited to individual members, providers and facilities Focus on rules based approaches (linear and limited to known schemes) Current Health Care Fraud Operations Limited to 3 rd party systems and rules No real proactive steps taken to combat fraud, waste, and abuse Inefficiencies driven by amount of data and disparate sources Copyright 2009, SAS Institute Inc. All rights reserved. 2

3 Advanced Analytics are Required Using a Hybrid Approach for Fraud, Waste & Abuse Detection Enterprise Data Suitable for known patterns Suitable for unknown patterns Suitable for complex patterns Suitable for associative link patterns Providers Members Rules Anomaly Detection Predictive Models Social Network Analysis Facilities Claims Rules to filter fraudulent claims and behaviors Examples: Detect individual and aggregated abnormal patterns vs. peer groups Examples: Predictive assessment against known fraud cases Examples: Knowledge discovery through associative link analysis Examples: Referrals Fraud Flags Financials 3 rd Party Data CPT upcoding / correct coding Value of charges for procedure exceeds threshold Daily provider billing exceeds possible Ratio of $ / procedure exceed norm # procedures / provider exceeds norm # patients from outside surrounding area exceeds norm Like upcoding behavior as known fraud provider Predicted diagnosis does not match actual Like provider/network growth rate (velocity) Provider association to known fraud Linked members with like suspicious behaviors Suspicious referrals to linked providers Hybrid Approach Proactively applies combination of all 4 approaches at member, provider, facility, and network levels Copyright 2009, SAS Institute Inc. All rights reserved. 3

4 Why SAS Fraud framework for Health Care? Provides the ability to apply Rules, Predictive Models, and Anomaly Detection on linked data More prioritized Fraud, Waste, & Abuse cases identified Including both previously undetected entities and networks and extensions to already identified cases Reduction in false positive rates Hybrid approach reduces false positives by up to 10+ times over traditional rules-based approaches Improved analyst / investigation efficiency Each alert takes 1/2 1/3 of the time to investigate due to data aggregation and visualization Provides alert logic and suggested path to initiate investigation Significant increase in ROI per analyst / investigator Copyright 2009, SAS Institute Inc. All rights reserved. 4

5 Provider Example Clinical Medical Lab 11,611 provider level alerts Usage of procedure codes 3 unique attending providers 7634 unique referring providers Usage spike: referring providers monthly July-December, since December 2,983,146 claim alerts 10,860 potential unbundled/up-coded claims ($15M) 31,963 claims for which the units of service exceeded normal (SAS and CMS) ($35M) 377,025 claims where procedure code charges exceeded normal ($198M) 454,720 claims where total charges exceeded normal for the primary diagnosis ($289M) Copyright 2009, SAS Institute Inc. All rights reserved. 5

8 Patient Example Dr. Shopping Patient had been prescribed a total of 12 narcotic drugs with overlapping prescription ranges 1 prescribed by 4 different physicians 1 prescribed by 5 different physicians 2 by 2 different physicians each Multiple pharmacies involved Rx Claims had physician DEA number instead of a medical ID (e.g. NPI or UPIN) Could not be tied back to an actual physician visit State Medicaid agency had paid all claims Copyright 2009, SAS Institute Inc. All rights reserved. 8

9 Provider Example Pain Management Epidural injections and spinal manipulation under Anesthesia Physician and surgical facility in collusion Multiple different tax ids used for billing Billed for multiple units of service when only one was performed and facility billed different procedure codes than the performing physician billed Facility billed for excessive dollar amounts compared to peers Husband and wife treated on over 95 different dates of service each On almost 25% of the dates both received the same treatment on the same day Both had instances of being released after treatment on the same day They were discharged under their own care Released under their own care hours after being under anesthesia? Potential false claims Independent medical review indicated spinal manipulation under general anesthesia was not warranted in either case ($500K). Copyright 2009, SAS Institute Inc. All rights reserved. 9

10 Provider Example DME Supplier DME Provider Falsification of Claims/Up-Coding ($138K) Number of patients was relatively steady (75-100/month) for first 8 months, as were number of claims and charges Number of patients jumped to a steady 125 in March Charges and services per patient began increasing by 15% per month in March as well Quantity of service per patient was flagged for several codes beginning in March Oxygen supplies, CPAP supplies, and Urological supplies Copyright 2009, SAS Institute Inc. All rights reserved. 10

12 Provider Example DME Supplier DME Provider Overutilization 202 claims alerted in a 1 year time frame for excessive units of service (>$1M) 4 alerts for total charges in month exceeding normal when compared to peers ($400K) Primarily for power wheelchairs and wheelchair accessories Suspected to be hitting rest homes in one metro area Copyright 2009, SAS Institute Inc. All rights reserved. 12

13 Provider Example Pharmacy Large number of short prescriptions Patients were returning weekly Pharmacy was writing more 7 day scripts than normal for his peer group Drugs not normally prescribed for short durations Normal for these drugs is 30, 60 or 90 day scripts Average script length for these drugs from this pharmacy was below normal when compared to peer group Pharmacist pled guilty Will repay over $1M in overcharges to CT Medicaid program Hartford Courant, March 31, 2010 Copyright 2009, SAS Institute Inc. All rights reserved. 13

15 Research Objectives & Methodology Objectives Understand fraud identification process and issues in Health Insurance industry today Find out where companies want to improve Determine top priorities of a comprehensive solution Methodology Focus group held at NHCAA Anti-Fraud Expo (November 2009) Nine directors and managers (business users) Copyright 2009, SAS Institute Inc. All rights reserved. 15

16 Identifying Leads for Investigation Formal Channels Hotline tips, call center Referrals from internal business units Task forces Law enforcement agencies Data Analysis Provider data trending ViPS STARSentinel FAMS EDIWatch Secondary Research BNA Health Care Report Internet Word of Mouth Colleagues Friends and family Copyright 2009, SAS Institute Inc. All rights reserved. 16

17 Fraud or Abuse? Question: Of the cases you investigate, what percentage would you classify as abuse and what percentage would you classify as fraud? Abuse - 20% to 50% Fraud - 5% to 10% What is a fraudulent or abusive practice? everything that comes to me we treat the same because we don t know we try to be sensitive to our relationship with the provider as soon as you get to the point where it is fraud you are subjected to what law enforcement tells you to do Copyright 2009, SAS Institute Inc. All rights reserved. 17

18 Prevention vs. Recovery Question: How much fraud and abuse do you recover vs. prevent? Payer A 0.1% 0.01% Payer B 0.2% 0.03% Payer C Payer D 0.2% 0.5% 0.3% 1.2% Recovery Prevention Payer E 0.3% 1.3% Payer F 0.5% 1.5% Percents represent approximate % of total company revenue Copyright 2009, SAS Institute Inc. All rights reserved. 18

19 Face of Fraud is Changing Recovery getting more difficult due to increased collusion between members & providers Medical records and member surveys no longer best source of information Fraud rings led by organized crime More emphasis on data analysis Threats of litigation Providers more willing to push back -- Call your attorney Reputation to litigate can be effective at settling Copyright 2009, SAS Institute Inc. All rights reserved. 19

20 The Coming Wave: Moving from Retrospective to Prospective Some uncertainty around analytics Lack of understanding Lack of faith Reluctance to enter into vendor evaluation Testing and refining approach with sample data Need to identify cases of highest likelihood for applying resources Copyright 2009, SAS Institute Inc. All rights reserved. 20

21 Impact of Technology on Process Deep pockets required More staff training required More investigators needed to follow up on targets even some of the lesser technology can overwhelm staff if you don t have the right processes and the right number of staff. Far reaching effect on organizational data Copyright 2009, SAS Institute Inc. All rights reserved. 21

22 Components of a Comprehensive Fraud Solution User friendly data analytics Reporting capabilities Prioritization (rating) tied with Intelligent rules Provider profiling Case management system Ties into claims system (seamless transition) Real time Understanding internal reimbursement policies Claims editor Routing (workflow management) Group policies Top 3 priorities Copyright 2009, SAS Institute Inc. All rights reserved. 22

23 Summary Findings Leads for investigation identified by myriad of sources Participants spend most time and effort on post-payment recovery Great interest in moving from retrospective to prospective analysis Process & staffing may become more critical with improvements in technology Most important components for a comprehensive fraud solution: User-friendly data analytics Reporting Prioritization & intelligent rules capabilities Copyright 2009, SAS Institute Inc. All rights reserved. 23