For years, payers have “tilted the playing field” with familiar levers—deny, delay, underpay, audit, and consolidate. What’s changed is scale. AI doesn’t just add a new lever; it turns older tactics into industrialized workflows that run faster than your billing team can react.
The result is a modern asymmetry: payers can operationalize uncertainty (about medical necessity, coding linkage, post-acute length of stay, out-of-network pricing) through algorithms—while providers inherit the downstream labor of proving what should have been obvious in the first place.
1) Predictive algorithms as “medical necessity” at scale
A key shift is the use of predictive models in utilization management—especially in Medicare Advantage post-acute care decisions. Multiple lawsuits allege that payers used algorithms to predict how much rehab a patient “should” need and then treated that prediction as the decision baseline—even when clinicians recommended more care.
Why this tilts the playing field:
- Prediction becomes policy. A forecast of expected recovery time functions like a coverage rule—even if the patient’s reality diverges.
- Humans become rubber stamps. Allegations describe operational pressure (performance targets, discipline for deviating) that makes “human in the loop” feel more like “human approving the loop.”
- The economics count on inertia. One suit explicitly claims payers rely on the fact that only a tiny fraction of members appeal denials—meaning the model “wins” by default even when wrong.
This is the denial/delay strategy rewritten as software: not a case-by-case dispute, but an automated throughput system.
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Why it matters
- Prevents missed follow-ups
- Reduces context switching
- Keeps teams proactive and organized
2) “1.2-second medicine”: automated claim denials in bulk
If utilization management is the front door, AI-assisted claim systems are the back door. Reporting on Cigna’s PxDx (procedure-to-diagnosis) system describes denials happening “in batches,” at a speed that suggests the clinical review requirement is being bypassed in practice.
Two details matter operationally:
- Denial velocity: allegations cite average denial times measured in seconds.
- Appeal outcomes: reporting describes a high overturn rate on appealed denials—suggesting the initial decisioning may be systematically over-inclusive (high false positives).
Translation for providers: your claims can be rejected not because they’re wrong, but because the model is tuned to flag aggressively—knowing the burden of correction sits with you.
3) Algorithms don’t just deny care—they can depress prices
AI isn’t only used to say “no.” It can also be used to say “less.”
A Reuters report describes the U.S. Department of Justice supporting medical providers in litigation alleging insurers used common pricing/analytics software (MultiPlan, now rebranded) in ways that could violate antitrust law—raising the concern that shared algorithms can coordinate decision-making and systematically reduce out-of-network reimbursements.
Even if you set aside the legal merits, the operational takeaway is clear:
When pricing logic becomes centralized in shared tools, underpayment can become standardized—and harder to challenge claim-by-claim.
The real advantage: AI turns “friction” into infrastructure
Historically, many payer tactics relied on administrative friction:
- serial denials,
- opaque rationale,
- slow correction loops,
- burdensome documentation demands.
AI upgrades that friction into something closer to an assembly line:
- more denials per unit time
- more consistent enforcement of payer-side heuristics
- less explainability at the point of rejection
- higher provider labor per dollar recovered
And because the system can be tuned centrally, changes propagate overnight—while providers discover the new rules weeks later in the denial queue.
What providers can do: fight systems with systems
You don’t beat industrialized denial with heroic phone calls. You counter it with operational design.
1) Instrument the denial machine
Track denial reason, model/tool implicated (when known), turnaround time, appeal rate, overturn rate, and dollars recovered. AI-based systems reveal themselves through patterns: speed, volume, repetition.
2) Shorten your appeal loop
If payers win by assuming you won’t appeal, your advantage is fast, standardized, high-throughput appeals. Build templates, evidence bundles, and deadline automation.
3) Treat documentation as “future audit defense,” not a note
When AI is used in UM and claim edits, documentation quality becomes your currency. Make the medical necessity narrative explicit and reproducible.
4) Demand transparency in contracts and processes
Where possible, push for:
- clear explanations for adverse determinations,
- right to human clinical review,
- disclosure of guidelines used,
- response-time commitments for appeals.
5) Collaborate
The most effective responses are collective: shared denial intelligence, shared playbooks, and shared evidence packets for recurring patterns.
The bottom line
AI isn’t the story. Scale is the story. AI gives payers the ability to convert policy ambiguity into automated decisions—then push the cost of correction onto providers and patients.
If providers respond with ad hoc effort, the payer’s system wins by design. If providers respond with disciplined operations—measurement, automation, faster appeals, and audit-ready documentation—they can neutralize the tilt and reclaim control of payment outcomes.
