TL;DR
Since January 2026, US Medicare has piloted WISeR, an AI-assisted prior-authorization model, across six states. On June 23, 2026, KFF Health News (via CBS) reported that doctors attribute some denials to AI hallucinations garbling clinical facts — a patient needing a neck injection denied because “the thoracic region” was ineligible; a patient whose records stated four times that there was no numbness denied on the grounds of numbness. CMS requires a licensed clinician, not a machine, to make the final non-coverage call. The layer that makes the call was there, on paper. What was missing is the layer that establishes, before a denial is issued, which patient file that determination was checked against.
What happened
- The subject is WISeR (Wasteful and Inappropriate Service Reduction), introduced into fee-for-service (Original) Medicare. It covers 13 services deemed vulnerable to fraud or misuse — skin and tissue substitutes, electrical nerve stimulator implants, knee arthroscopy for osteoarthritis, and kyphoplasty for spinal fractures — across Arizona, New Jersey, Ohio, Oklahoma, Texas, and Washington. Inpatient-only services, emergency services, and services for which delay would pose a substantial risk are excluded.
- Doctors and their staff say some denials look like AI hallucinations that garble or invent clinical information. An April report from a US senator’s office, drawing on hospital association data, said that at the University of Washington’s medical system alone nearly 100 patients had been waiting this year for epidural injections because of WISeR-related delays.
- Per CMS’s own announcement, model participants are paid based on their ability to reduce inappropriate utilization and lower Original Medicare spending, adjusted for quality and process measures covering decision speed and stakeholder experience. The CEO of one vendor, Humata Health, says he is not aware of any such hallucinations.
A denial reaches the patient through the following chain.
- A physician submits the supporting medical records to an online portal ahead of one of the 13 covered procedures.
- AI and machine learning grant an “immediate yes” to requests meeting the program’s criteria — one vendor puts this at about 88% of cases where the clinical data supports approval.
- The rest go to review, and some come back denied on garbled clinical facts.
- The patient faces delay, extra visits, or the loss of the treatment altogether; appeals rise, and review costs accrue on the government’s side as well.
Timeline — disclosure and response
- 2025-06-27: CMS announces the WISeR model, stating explicitly that final determinations that a request does not meet coverage requirements will be made by licensed clinicians, not machines.
- 2026-01 (mid-month): the pilot launches in six states. The outgoing CEO of the Ohio State Medical Association calls the rollout “quicker than normal” for the federal government; the policy director of the Washington State Medical Association says doctors “just sort of had to figure it out.” A vendor acknowledges an “aggressive rollout from the time of being notified to going live.”
- 2026-04: Sen. Maria Cantwell’s office publishes a snapshot report on WISeR drawing on hospital association data, including the roughly 100 patients waiting for epidural injections at the University of Washington.
- 2026-06-23: KFF Health News (Darius Tahir, published by CBS News) reports the confusion, errors, and delays, along with denials that doctors suspect originated in AI hallucinations.
This Brief does not adjudicate whether any individual case was an AI hallucination. CMS and the vendors state that humans make the final determination, and one vendor says he is not aware of any hallucinations. Individual physicians’ statements are treated as statements from parties with a direct interest. The senator’s office report could not be retrieved directly at the time of writing, so its figures are cited via KFF Health News’s reporting.
The response and industry movement after disclosure:
- CMS says the goal is “to reduce inappropriate care without delaying appropriate care,” with decisions returned within 72 hours.
- That is not what clinicians on the ground describe. Clean claims are supposed to be paid within 15 days, physicians say, but a Tulsa radiologist reports seeing delays of six to eight weeks. At an April briefing, the vendor holding the Arizona contract also acknowledged a large payment backlog stretching back to January.
- Appeals are reported to be rising, and CMS says it has factored in changes in the volume of appeals and the associated costs. It says there are “currently no changes” contemplated for the list of covered services, but that it continues to assess whether changes are warranted.
- The director of the CMS Innovation Center also acknowledged that “the percentage of providers committing waste, fraud, and abuse is small.”
Why it wasn’t stopped
The failure here is neither that an AI can be wrong nor that human review was absent from the design. There was no record, checkable before the action becomes final, of which patient file — which entry in it — each determination was checked against.
Human final judgment is written into the design. CMS requires a licensed clinician to make the non-coverage determination, and vendors describe it the same way. On paper, that layer existed. What was missing sits earlier — a form in which a third party could establish, before the action becomes final, which record the determination was actually checked against. When a physician states the same fact four times in the record and the misreading is not corrected, nothing remains to substantiate the claim that the file was consulted at all.
A denial is an action. Without a record of which file that determination was checked against, the claim that it was checked stays a claim — and the error takes effect first, as an adverse action already imposed.
The payment design can widen this gap. When payment is tied to reduced utilization and lower spending, the record of which file a determination rests on has to be carved out independently of the paying party, or no material is left with which to audit how determinations were issued. This runs in a direct line from Brief 078, where erroneous automated eligibility determinations cut off Medicaid coverage, and it shares the structure of public-sector AI judgments that go unverified before action. The issue is not the “fairness” of the determination but the one point turned on by Brief 012 (facial recognition and a wrongful arrest): that a determination is not independently verified and authorized before a coercive action follows.
What proof would have changed
Proof-as-auth inserts one step into the path ahead of the moment an adverse determination becomes final: it fixes what the determination was checked against. It is not that a machine judges whether the check itself was right. It puts “which patient, and which entry in the record, was this determination checked against” into a form in which the person on the receiving end can establish it — before the action becomes final, and without querying the issuer.
Lemma’s design against this gap:
- The record it was checked against, fixed. Before an adverse action is finalized, tie the determination to the patient record actually consulted, and keep which record it rests on in a form that can be checked before the action.
- A record of the check. Keep when that binding was made, under whose issuance, and that it has not been altered since — tamper-evident, in a form that cannot be overturned later. The claim that the file was consulted stops being merely a claim.
- Provenance of the decision path. Record the path from the AI's recommendation to the human's final call in a form that cannot be altered afterward. Once the time of approval is on record, review time per case becomes a checkable fact.
- An audit trail independent of the payer. Even where payment tracks reductions, carve out the record of which file a determination rests on so it can be audited independently of the paying party.
What this layer does not carry is worth stating as well.
- Whether a misreading occurred is judged by a person, on the basis of that binding.
- Provenance shows when an approval was performed, not whether the reviewer read the record.
- The gate sits with the payer; this layer supplies the material for that decision, no more.
This is also where it differs from an operator’s own audit log. A log is something a party produces for itself; the party on the receiving end of the action cannot check it independently.
The two reported cases differ in kind. Take the denial said to have cited the thoracic region against a request for a neck injection. The physician who submitted it can reconcile that with their own filing, so once the binding is fixed, the denial can be contested before it takes effect. A determination that contradicted findings stated repeatedly in the record is a different matter. That is about the reading of the record itself, which falls outside the scope of pre-execution proof: a layer that depends on the same reading only reproduces the same error. What carries that case is improving the model’s accuracy, human review, and appeals.
Lemma is not a product that determines medical necessity, nor one that detects AI errors. Improving the model’s accuracy and human final review are complementary to this layer, not alternatives to it. The first corrects errors afterward; the second closes one point before the action becomes final.
Sources
- KFF Health News / CBS News (independent reporting): Darius Tahir, “Medicare’s AI push snarls patients and doctors in errors and delays” (2026-06-23) — https://www.cbsnews.com/news/medicare-ai-program-wiser-prior-authorization-errors-delays/
- CMS (primary, announcement): “CMS Launches New Model to Target Wasteful, Inappropriate Services in Original Medicare” (2025-06-27) — https://www.cms.gov/newsroom/press-releases/cms-launches-new-model-target-wasteful-inappropriate-services-original-medicare
- CMS (primary, operational guide): “WISeR Model Provider and Supplier Operational Guide” — https://www.cms.gov/priorities/innovation/files/wiser-provider-supplier-guide.pdf