Readers Write: Engineering Prior Authorization for WISeR: Six Ways Providers Can Prepare for AI-Assisted Prior Authorization Under the WISeR Model

Engineering Prior Authorization for WISeR: Six Ways Providers Can Prepare for AI-Assisted Prior Authorization Under the WISeR Model
By Ryan Redman, JD

Ryan Redman, JD is product manager at Onspring.

The Wasteful and Inappropriate Service Reduction (WISeR) model introduces AI-assisted reviews into Medicare Fee-for-Service (FFS) prior authorization across six pilot states is now live, as of January 2026. That may expedite cost control, but it also raises high-stakes governance questions that are already being discussed in public debate.

Some critics have warned of an “AI death panel” dynamic in payer decisions, a fear that is now echoing into Medicare’s orbit as automation expands. For providers participating in original Medicare, the operating problem changes. Decisions must be made quickly, consistently, and defensibly, with evidence trails that withstand audits and appeals.

While the program is framed around reducing waste, it creates immediate governance, risk, and compliance challenges for providers who are deciding whether and how to submit services through the WISeR prior authorization pathway.

What changes most under WISeR is not clinical care, but the expectation that decisions are traceable, reviewable, and defensible as they move through provider ordering, scheduling, and revenue cycle workflows and into AI-assisted review on the payer side.

How should providers respond? The focus should be on preparing ordering, intake, and revenue cycle workflows first, then tuning for throughput.

Where the friction really is for providers

Before designing solutions, providers must understand where WISeR introduces operational and governance risk into existing workflows. Providers will still deliver care and submit claims, but WISeR introduces new intermediaries, AI technology vendors, between the provider and the Medicare Administrative Contractor.

With tech vendors now in the mix, incentives to curb waste cannot influence clinical judgment. Provider documentation and workflow controls must support medical necessity without introducing financial bias into clinical decision-making.

Teams will have to juggle prior authorization and pre-payment reviews. If a provider chooses not to submit a required prior authorization, the claim will be scrutinized pre-payment, delaying reimbursement by 45 days or more and potentially affecting cash flow. If prior authorization is skipped, post-service reviews can stall cash and increase appeals, so routing, timers, and evidence capture must be precise.

The baseline requirement: transparency is non-negotiable. Prior authorization status, approval and denial patterns, turnaround times, and appeals must be visible across provider clinical, scheduling, and revenue cycle teams, not in stitched spreadsheets, with human review and audit trails for any AI-assisted step.

Build a WISeR-ready architecture

With the friction points defined, the build becomes clearer. From a provider perspective, a WISeR-capable pipeline consists of six moving parts that function as a single system and support governance, risk monitoring, and compliance reporting.

1. Data discipline at intake. Ensure that your intake teams or software are capturing the specific clinical evidence that is required for WISeR codes before the order is signed. Don’t let the order proceed without the “evidence packet” attached. For providers, this starts with ensuring that required clinical documentation is captured at the point of order for WISeR-targeted services.
2. Pre-submission logic checks. Configure clearinghouse or revenue cycle management (RCM) practices to check claims before submission. If an issue arises, stop the claim internally before the AI vendor sees it.
3. Clinical review queue (human in the loop). For providers, this includes ensuring that claims do not drop until a prior authorization number is on file. Use selectable reason codes for consistent reporting and notices. Human oversight remains a documented control, not an informal checkpoint.
4. Evidence and disclosure bundles. Automatically generate a complete packet for each determination: inputs, rationale, attachments, timestamps, communications, and notices aligned to reason codes.
5. Appeals and learning loop. Segregate appeals (different reviewers, fresh rationale). Track overturns and feed them into rule refinement, reviewer coaching, and documentation retraining where gaps are identified.
6. Observability in the system of record. Instrument the same system that makes decisions: latency distributions, approval to denial ratios, appeal rates and outcomes, reviewer variance, and any AI usage or overrides. Providers should monitor denial trends closely to identify whether specific diagnosis codes or documentation patterns are triggering automated review.

Controls that make speed defensible

Role-based access should determine who can view PHI, who can finalize a determination, and who can modify provider-controlled workflow rules and documentation requirements. When those rules or configurations change, record who reviewed them and maintain a versioned history of the changes. Logs should be append-only and time-stamped, with retention aligned to records schedules. Controls should also prevent WISeR-targeted claims from being submitted without a prior authorization number on file.

Because AI-supported reviews occur on the WISeR technical vendor side, providers are not tuning models, but monitoring outcomes. Pattern and variance checks should run continuously, monitoring approval and denial rates by category and population slices, tracking overturns on appeal, and flagging outliers for the governance group. Provider compliance, legal, security, and operations teams should review findings together to protect both reimbursement and regulatory posture.

Proving it with metrics and turning plans into operations

Where providers use AI internally, such as limited adoption of AI-enabled claims review or denial prediction, those tools should be governed as part of existing clinical and revenue cycle controls rather than treated as core to the WISeR model itself.

Treat WISeR as an engineering problem: set up the core path, prove it on one service line, and then extend it with guardrails. Four phases keep providers moving without losing control.

• Phase 1: foundation. Intake queues, evidence and disclosure bundles, and tamper-evident logs. Run one high-volume service line end to end. Ensure schedulers do not book WISeR-targeted procedures for original Medicare patients without a prior authorization number on file.
• Phase 2: pilot and prove. Add audited versioning for rules and, where used by a limited set of providers, any AI-enabled claims review configurations. Require documented clinician sign-off for adverse determinations and keep clinical review independent from financial reporting in access controls and logs. Validate that claims for targeted codes cannot drop without prior authorization.
• Phase 3: find gaps and retrain. Use denial and pre-payment review data to retrain physicians when documentation gaps emerge.
• Phase 4: institutionalize and monitor. Run a standing governance cadence (compliance, legal, security, operations, clinical). Track a small, trusted set of metrics: time to decision (median and tail), backlog age, first-pass yield, appeal and overturn rates, reviewer variance, and cash flow impact from pre-payment review delays.

WISeR raises the bar on speed, transparency, and defensibility. For providers, success depends on preparing workflows and documentation before claims are submitted. Done well, this approach protects reimbursement, limits disruption, and may support future eligibility for CMS “Gold Card” exemptions as performance is evaluated during the pilot, ensuring that provider organizations can participate in WISeR without unnecessary risk. Getting data, documentation, and workflows right now puts providers in a position to earn flexibility later.