THE MACHINIFY APPROACH
Machine learning models flag behavioral health claims anomalies like upcoding patterns, session-length outliers, telehealth modifier misuse, and impossible-day billing.
Every flagged claim is reviewed by a certified coder with behavioral health expertise. Each determination is documented and defensible.
Machinify supports both pre-payment and post-payment review environments. Pre-pay review is especially important for out-of-network behavioral health claims, where post-pay audit leverage may be limited.
The program documents audit processes, actions, and decisions for behavioral health in the same way it captures medical-surgical claims decisions.
THE EVIDENCE
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Machinify’s behavioral health payment integrity program is already deployed in one of the most stringent parity enforcement environments in the U.S. It’s ready for your plan today.
Payer leaders evaluating behavioral health payment integrity programs often raise similar questions. Here’s how Machinify addresses each one.
No—failing to apply consistent review across behavioral and medical claims is the greater risk.
Machinify aligns audit methodology across both domains and produces the comparative documentation required to demonstrate parity compliance.
No. The program focuses strictly on coding accuracy and documentation—not clinical decision-making.
It does not involve prior authorization or medical necessity determinations.
Earlier approaches relied on broad, non-specialized review.
Machinify uses targeted claim selection, behavioral health expertise, and clear rationale—reducing unnecessary abrasion while addressing real issues.
Manual review doesn’t scale.
Machine learning triage identifies the claims most likely to warrant review, making even low-dollar claims viable at scale.
Federal timelines may shift, but enforcement hasn’t stopped.
States like California, New York, and Pennsylvania continue active oversight, and private litigation remains ongoing.
The 2024 Final MHPAEA Rule placed an affirmative burden on plans to conduct and document NQTL comparative analyses. Lisa Pincher breaks down what comparable oversight means operationally—including audit selection logic, pre-payment design considerations, and how to build a program that is defensible to regulators without restricting member access.
Documentation-to-code misalignment is a recurring finding in behavioral health claims — with roughly half of reviewed claims containing some form of variance. Lisa Pincher, VP of Operations at Machinify, explains why episodic audit sweeps aren't enough and how a control-system approach stabilizes payment accuracy across the claims lifecycle.
Out-of-network utilization in behavioral health can approach 25% of claims—far above med/surg norms. With limited post-payment leverage, the economics of traditional recovery break down. Lisa Pincher examines why timing matters and how pre-payment validation changes the equation for health plans.
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