1

• Claims Editing and Pre-pay Fraud and Abuse Detection and Avoidance Defined
• Detection Methods
• Comparison of Pre-pay and Post-pay Approaches
• Results
• Questions
2

3

• “Avoidance” means the actual savings from direct claims denied or reduced
• Net of amounts paid on the original claim
• Net of amounts paid on re-filed claims
• “Avoidance” does not mean additional savings from the provider from change in behavior, or change in behavior of other providers
• “Fraud” is used to mean any claim that is filed by the provider or provider billing agent improperly and really should be “fraud, waste, abuse or other improperly filed claim”
• Except where specified, I am not referring to the legal definition of “fraud” which includes “intent”
4

• There are several claims editors on the market and
Ingenix provides the Ingenix Claims Editing System
(iCES)
• Claims editors are focused on claims that are inherently incorrect or that are incorrect given other claims
• They are based on industry standards, coding requirements or payer specific requirements
• Generally they auto-deny claims pre-payment
• Some claims editors also have “rules” that are not autodeny
5

• Prior authorization of services
• Biometric or other technology that validates that the proper patient and provider were present at the point-ofservice
• 100% pre-pay review of claims over a certain dollar threshold
6

• Identification of “suspect” claims or claim lines
• One or more of various models rules, or prepayment flags have identified the claim as likely to be incorrectly coded, not performed, or not performed as coded
• Stopping those claims for human review
• Almost always requires stopping (suspend or “pend”) a claim and requesting a medical record
• In some claims processing systems/approaches, claims are denied when they are stopped and the medical record is requested
• Performing an in-depth investigation on the claim
7

8

9

• This space represents the universe of claims
• Manual clinical review is impossible for entire space
• Goal: Stop as many reds (improper) for review as possible while keeping the number of blues (proper) identified to a minimum
10

Predictive Models &
Analytical Targets
• Codes that can be used to bypass conventional claims edits
• Provider’s historical prevalence of upcoding
• Hours of work
• Changes in provider behavior particularly involving increasing of claims filed such as:
• Likelihood that certain claims should have been grouped
• Scores based on multiple factors
Dimensional Modeling
Anomalies Flag Claim as High
Risk for “Overpayment ”
• Unlikely or infrequent relationships
• Between diagnosis and procedures within a claim
• Between procedures from different claims for the same patient
Peer Comparison Approach
• Outlier within specialty/region for performing high cost procedures based on synthetic (data-driven) specialty groupings
• Outlier for ordering certain tests or treatment
These are some examples of the issues identified in pre-pay analytics
11

Provider Flags – a list of known providers with issues is compiled and all or a subset of claims are stopped for review
Challenger Analytics – outlier analysis & soft rules create dynamic provider flagging
Observable
Patterns
INFERENC
E
Aberrant Billing Pattern
(ABP) Algorithms – clinical expertise crystallized into coding logic, patterns are identified at the claim level
Predictive Model – detecting more advanced improper billing patterns using interactions among many variables
12

13
• The output of traditional rules or flags is binary: either a claim is flagged or it is allowed
• With the Predictive Model, the output is in the form of a continuous score
• The scores range from 1 to 1,000 - with higher scores indicating the larger deviation from typical behavior
• Once each claim line is scored, the final score for the claims is assigned as the maximum of the line scores
• The purpose of the score is to rank-order the claims in order of descending suspicion of fraud and abuse
• A score threshold is set to stop only those claims where
Predictive Model score exceeds the threshold to ensure the most anomalous claims are stopped for review
13

• Multiple anomaly factors used to identify suspect claims
• Uses a weighted approach and a deviation from expected mean approach
• Continually updated by payer experience
• Most core variable/equations unchanged
• Unbundling different based on Medicare rules
• “Peer” Grouping is Critical
• Does not use declared specialty
• Data-driven peer groups determined through advanced analytical techniques and novel use of data
• Start by looking for approximately 300 peer groups
• Work down to 100 to 200 groups to ensure each has sufficient size
• Has been and remains a core component of P2.0
14

15
• The threshold determines which claims scored by the
Predictive Model ultimately get flagged for review
• The threshold is composed of the following parameters:
• Predictive Model Score
• Claim Charged Amount
• An analysis of sample data runs via the Predictive Model is performed to determine the initial threshold setting
• Striking balance between maximizing potential savings and minimizing false positives
• This analysis is presented to the client for review and approval
• Ingenix reviews and recommends threshold changes to clients on a regular basis
15

16
• Provider filters can be set up to ensure that no claims for a given provider are flagged by P2.0
• Provider filters can based on either TIN or NPI, and are created to avoid flagging claims for providers that over time have proven to have a high false-positive rate
• These filters are set as time limited to ensure the providers are reviewed on a regular basis
16

Provider
Flags
ABPs Challenger
Processing Order
Analytics
Predictive
Model
• Feedback improves models to the left which become more accurate over time for each client
• Over time, improvements to models to the left reduce the measured “accuracy” of models to the right
• Feedback for model improvement is also received from reviewers of claims and medical records
17

18

• Model development and maintenance —over 70 staff
• Advanced statistical modelers
• Data analysts
• Software developers
• Clinicians
• Coding and billing experts
• Payment policy experts
• Claims and medical record review —100s of staff
• Clinicians
• Coders
• Investigators
19

20

• Providers complain less when their money is not paid than when their money is taken back
• Claim specific review
• Fast turn-around needed because payment of some correct claims is being held up
• Claim-by-claim review limits referral to law enforcement for suspected criminal fraud
• Opportunity to stop payments to providers that would never pay back improperly paid amounts
• Claim specific or provider reviews
• Provider review can lead to increased recoveries through extrapolation
• More referrals to law enforcement for suspected criminal fraud when providers are reviewed
• Needed for identifying certain activities such as network fraud and improper billing of low dollar claims (E&M up-coding)
• Feedback to pre-pay process
Both are components of an effective, comprehensive Program Integrity program
21

22

• Ingenix does pre-pay and post-pay fraud and abuse detection, avoidance and collection work for governments, commercial plans and over 10 government-focused health plans
• Ingenix saved clients approximately $500,000,000 from pre-pay fraud and abuse detection and avoidance services in 2010
• This is the direct savings numbers from claims stopped and reduced or denied that would have otherwise been improperly paid
23

Receive Claims and Apply Ingenix Predictive Analytics
& Modeling to Score Claim
Per 1,000,000 Claims
Pend Suspect Claims and
Request Medical Records
1,709 Claims Pended and
Medical Records Requested
(0.17% of Total Claim Volume)
Receive Medical Records 854 Records Received
Deny Based on
Medical Records Review
401 Denials Based On Records Review
+ 855 Denials for Records Not Received
Review Provider
Appeals
Appeals = 167
Accepted
Appeals
Overturned Denials (Based on Appeal) = 50
Total Net Pre-Pay
Denials:
*
*401+855-50=1,206
Claims
Savings:
** 1.5%
**Savings of Professional Claims
Dollars
24

Tom McGraw thomas.mcgraw@ingenix.com
(804) 357-7739 www.ingenix.com
25