Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation's express consent. © 2011 Fair Isaac Corporation. 1 Insurance Fraud Manager User Group February 8-9, 2011 San Diego, CA Facility Model Claim level Mike Tyler Sr Director of Analytic Science FICO Robin P Analytic Science-Scientist II FICO Feb, 2011

© 2011 Fair Isaac Corporation. Confidential. 2 © 2009 Fair Isaac Corporation. Confidential. 2 Agenda »Facility Model overview »Our solutions »In patient claims analyses »Out-patient claims analyses »Consistency analysis

© 2011 Fair Isaac Corporation. Confidential. 3 © 2009 Fair Isaac Corporation. Confidential. 3 Facility Model overview

© 2011 Fair Isaac Corporation. Confidential. 4 Facility Model Approach »IFM Medical currently focuses on physician claims »At last User’s Group, we heard a strong call for the ability to score Facility claims »Leverage much of the same technology we use in physician scoring »Focused scores with relatively small contexts to facilitate prepay scoring »More complex scores to detect complex patterns

© 2011 Fair Isaac Corporation. Confidential. 5 Facility Model Approach »Scoring of both outpatient and inpatient cases. »Scoring done on individual claims or small batches of claims. »Reason for score immediately obvious »Scoring happen quickly »Models are data driven which adapt to change easily »Same unsupervised approach used in physician claims scoring »Models continue to learn in production (fraud amoeba)

© 2011 Fair Isaac Corporation. Confidential. 6 © 2009 Fair Isaac Corporation. Confidential. 6 Agenda »Facility Model overview »Our solutions »Inpatient claims analyses »Out-patient claims analyses »Consistency analysis

© 2011 Fair Isaac Corporation. Confidential. 7 © 2009 Fair Isaac Corporation. Confidential. 7 Our solutions

© 2011 Fair Isaac Corporation. Confidential. 8 Claim centric »Facility scores based on a suite of simple scores »Immediately apparent why a claim scores high »Review is very focused and efficient »Scores are data driven based on your own data »Takes into account the wide variety of payment policies in healthcare »Scores are calibrated to match the score distribution of other IFM claims scores

© 2011 Fair Isaac Corporation. Confidential. 9 Patient centric »Claim level scores based on some small batch of claims »Generally small batch is designed around a patient »Patient-day »Patient-procedure »Poor quality of care »Patient focus eliminates any fragmentation »Following the patient is a fruitful way to uncover inconsistencies »Looking across both inpatient and outpatient claims for fraud indicators is powerful

© 2011 Fair Isaac Corporation. Confidential. 10 © 2009 Fair Isaac Corporation. Confidential. 10 Agenda »Facility Model overview »Our solutions »In-patient claims analyses »Out-patient claims analyses »Consistency analysis

© 2011 Fair Isaac Corporation. Confidential. 11 © 2009 Fair Isaac Corporation. Confidential. 11 In-patient claims analyses

© 2011 Fair Isaac Corporation. Confidential. 12 Inpatient claims analyses In- patient claims Claim centric DRG high paid RUG LOS Patient centric MDC rate Poor Quality of Care »Inpatient claims scored assuming a PPS payment system »Scores focused on a single PPS claim, utilizing other related claims for context

© 2011 Fair Isaac Corporation. Confidential. 13 Patient centric analyses »Poor quality of care »Tries to capture cases of readmission by a beneficiary resulting out of poor quality of care. »A batch of claims for a patient is analyzed »Looks at the gap between the two inpatient admits. »Takes into account provider behavior too. »MDC rate »Analyzes readmission rate of a patient for an MDC in a given time frame. »Norms are created on MDC. »High rate may include » Services that were reported but not rendered. » Services performed unnecessarily or poor quality of care.

© 2011 Fair Isaac Corporation. Confidential. 14 Claim centric analyses »High dollars for a DRG »Finds claims with unusually high dollars for DRG »Creates norms on dollar amount for DRG. »Also looks at the length of stay (LOS) for the patient. »LOS for a RUG »Payment is on per diem basis for RUG. »Examines norms on LOS for RUG. »Finds claims with high LOS for RUG.

© 2011 Fair Isaac Corporation. Confidential. 15 © 2009 Fair Isaac Corporation. Confidential. 15 Agenda »Facility Model overview »Our solutions »In-patient claims analyses »Out-patient claims analyses »Consistency analysis

© 2011 Fair Isaac Corporation. Confidential. 16 © 2009 Fair Isaac Corporation. Confidential. 16 Out-patient claims analyses

© 2011 Fair Isaac Corporation. Confidential. 17 Outpatient Scoring »Goal was to take our standard IFM physician claim scores and apply them to outpatient claims »Some of those scores work well, others did not Out-patient claims Claim centric High paid Patient centric RateRepeat High dollar day

© 2011 Fair Isaac Corporation. Confidential. 18 Claim centric »Procedure high paid »Finds claims with unusual high dollar amount for a procedure. »Norms are based on procedures. »Detects » Unusual high payment » Data errors

© 2011 Fair Isaac Corporation. Confidential. 19 Patient centric »High dollars on a patient – day »Examines the mix of procedures on a patient-day. »Finds patients with unusually high dollars paid for their procedure mix »Finds a variety of problems » Unbundling » Overbilling » Data errors »Patient – procedure rate »Focuses on the rate of a procedure performed on a patient in a time frame. »Norms are based on procedures. »Detects » Duplicate billing » Unusual patterns of care

© 2011 Fair Isaac Corporation. Confidential. 20 Patient centric »Patient – procedure repetition »Focuses on how quickly a procedure is repeated on a patient »Targets cases where a procedure is performed too soon »Norms are based on procedure in question »Detects » Duplicate billing » Unusual patterns of care

© 2011 Fair Isaac Corporation. Confidential. 21 © 2009 Fair Isaac Corporation. Confidential. 21 Agenda »Facility Model overview »Our solutions »In-patient claims analyses »Out-patient claims analyses »Consistency analysis

© 2011 Fair Isaac Corporation. Confidential. 22 Consistency analysis »Focuses on the consistency between each beneficiary's inpatient and outpatient claims. »A batch of claims around an inpatient visit for a patient is examined. »Profiles are created on each such batches and looked for outliers » Inconsistencies may include »Services that were reported but not rendered »Inappropriate services that were performed.

Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation's express consent. © 2011 Fair Isaac Corporation. 23 Insurance Fraud Manager User Group February 8-9, 2011 San Diego, CA THANK YOU Feb 8, 2011 Mike Tyler Sr Director of Analytic Science FICO Robin P Analytic Science-Scientist II FICO