1 claims editing and pre-pay fraud and abuse detection and avoidance tom mcgraw

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Claims Editing and Pre-pay Fraud and Abuse Detection and Avoidance

Tom McGraw

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Agenda

• Claims Editing and Pre-pay Fraud and Abuse Detection and Avoidance Defined

• Detection Methods

• Comparison of Pre-pay and Post-pay Approaches

• Results

• Questions

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Claims Editing and Pre-pay Fraud and Abuse Detection and Avoidance Defined

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Use of Terms “Avoidance” and “Fraud” in this Presentation• “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”

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Claims Editing

• 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 auto-deny

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Pre-payment Processes That I Won’t Be Talking About

• Prior authorization of services

• Biometric or other technology that validates that the proper patient and provider were present at the point-of-service

• 100% pre-pay review of claims over a certain dollar threshold

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What is Pre-pay Fraud and Abuse Detection and Avoidance?

• 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

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Processing—Standard Implementation

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Detection Methods

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The Challenge of Improper Claim Detection

• 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

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Predictive Models &Analytical Targets

• Codes that can be used to bypass conventional claims edits

• Provider’s historical prevalence of up-coding

• 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

Identification of “Unlikely” Claims—Multiple Methods

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

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Observable

Patterns

INFERENCE

Provider Flags – a list of known providers with issues is compiled and all or a subset of claims are stopped for review

Aberrant Billing Pattern (ABP) Algorithms – clinical expertise crystallized into coding logic, patterns are identified at the claim level

Challenger Analytics – outlier analysis & soft rules create dynamic provider flagging

Predictive Model – detecting more advanced improper billing patterns using interactions among many variables

Pre-Pay Fraud and Abuse Detection Methods

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Claim Scoring

• 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

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Predictive Model• 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

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Determining Which Claims to Flag

• 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

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Provider Filters

• 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

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Detection Methods Processing and Feedback

• 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

Provider

Flags

Predictive

Model

Challenger

Analytics

ABPsProcessing Order

Model Improvement FeedbackModel Improvement Feedback

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Staffing

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Staffing

• 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

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Comparison of Pre-pay and Post-pay Approaches

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Comparison of Approaches

Pre-pay•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

Post-pay• 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 processBoth are components of an effective,

comprehensive Program Integrity program

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Results

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Overall

• 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

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Claims Savings:

1.5%**

Request Medical Records

Receive Claims and Apply Ingenix Predictive Analytics & Modeling to Score Claim

Pend Suspect Claims and

Receive Medical Records

Deny Based on Medical Records Review

Review Provider Appeals

Accepted Appeals

Total Net Pre-Pay Denials:

1,206 Claims*

Per 1,000,000 Claims

1,709 Claims Pended and Medical Records Requested (0.17% of Total Claim Volume)

854 Records Received

401 Denials Based On Records Review + 855 Denials for Records Not Received

Appeals = 167

Overturned Denials (Based on Appeal) = 50

*401+855-50=1,206

**Savings of Professional Claims Dollars

Health Plan Example

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Questions?

Tom McGraw

thomas.mcgraw@ingenix.com

(804) 357-7739

www.ingenix.com