claims editing and pre-pay fraud and abuse detection and avoidance
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Claims Editing and Pre-pay Fraud and Abuse Detection and Avoidance. Tom McGraw. 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. - PowerPoint PPT PresentationTRANSCRIPT
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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
