US health insurance agency authorizing patient claims with AI

US health insurance agency authorizing patient claims with AI and enable quicker approvals and reduced expenditures. Opponents caution that AI may feed false dismissals, stall life-saving interventions, and lower decision-making openness.

 

What’s Going On?

An American health insurance agency has also indicated that it is going to use the AI to deploy a system to handle prior authorization and patient claim approvals.

The aim is to accelerate the process, eliminate mistakes and enhance customer experience. Nevertheless, this move has been seriously questioned by experts and other stakeholders.

Related Developments:

• Medicare Pilot Program (WISeR)

This will be introduced as WISeR (Wasteful and Inappropriate Services Reduction) in 6 states, starting January 2026 (Arizona, New Jersey, Ohio, Oklahoma, Texas and Washington). This program will apply AI to help in the approval of 17 outpatient procedures.

It is important to note that, the final decisions made will not be made by AI but licensed clinicians. Critics have fears that this would continue to extend wait times or denials, particularly detrimental to the elderly who used to enjoy the benefit of minimal prior-authorization of Medicare.

• Artificial Intelligence Tools Make Patients attractive.

Several new AI-based services, such as Claimable (paid: ~$40) and Fight Health Insurance (free), help patients write appeal letters in response to claim denials. These tools make what has long been an overwhelmingly complicated process easier.

Some examples of insurer reforms are as follows.

In response to media scrutiny, such as the much-publicized death of the CEO of UnitedHealthcare, large insurance companies (Cigna, Aetna, Humana, UnitedHealthcare) have committed to reforming prior authorization practices: minimising the number of approvals required, accelerating decisions, enhancing transparency and permitting continued treatment when the plan changes.

AI will contribute, and they aim at 80 percent approvals in real time by 2027. But, numerous are concerned that there is no enforcement and a lack of clarity about patient protections.

Why This Is Troubling

1. Higher Denial Rates & Risk of Harm

The medical practitioners have sounded alarms that AI-mediated systems can automatically reject more than 16 times as many cases as their human-reviewed counterparts. Such algorithms usually do not have the subtlety of complicated or unusual cases.

According to physicians, such denial procedures are some of the reasons that lead to delayed care, higher out-of-pocket expenses, lack of treatment, and even serious consequences such as hospitalization or death.

2. Bias & Lack of Transparency

There is mounting anxiety around the bias in algorithms and black box decision-making that patients or providers can poorly understand or oppose.

3. Insufficient Human Oversight

There is a stress among critics, such as the American Medical Association, that AI should be viewed as an augmented intelligence, not a substitute. Clinician review and accountability, should always be part of final decisions on medical necessity.

4. Regulatory Gaps & Evolving Laws

o According to the NAIC, 84 percent of U.S. health insurers are already working with AI/ML, though regulators continue to work on the right models of governance.

o California had SB 1120 to mandate that all treatment denial decisions be under human provider supervision- a precedent of stricter safeguards.

 

Bottom Line

Although AI offers efficiency and cost-control, its use in healthcare claims decision-making, where life and death are at stake, brings into question deep ethical, logistical, and legal issues. Key risks include:

• Automated denials for complex medical cases
• Escalation in patient harm due to delays or denials
• Reduced transparency and accountability
• Insufficient regulation and oversight
• Disparities in who benefits or suffers from such automation

 

state laws, WISeR program of Medicare and uses of AI in appeals?

1) State and regulatory reactions — California as the standard bearer.

What California did

SB 1120 (in effect January 1, 2025) specifies that health plans can scan the utilization of AI/algorithms to support utilization review and utilization management.

It demands plans that have AI tools utilization reviewed to fulfill outlined transparency, fairness, and human-oversight criteria (such as: documentation of inputs, human-clinician oversight of adverse decisions, and equitable use across populations).

Why this matters nationally

California law deserves to be mentioned as the most concrete illustration of a state-level effort to make sure that AI has a human component in coverage decisions.

The other states and regulators are taking note; NAIC and state insurance authorities are considering the standards of governance, and there is no yet a standard nationwide that matches what California has accomplished.

Key gaps to watch for

• Scope and enforcement: Laws may demand staffing, audits and punishment.
• Definition creep: AI is broad- rules should include simple rule-based automation and also ML models.
• Transparency vs. proprietary models: Trade secrets are frequently mentioned in plans, and it is difficult to understand among patients/physicians reasons why an algorithm rejected a request.

2) The pilot of WISeR in Medicare – its nature and fundamental issues.

What WISeR is designed to do

WISeR (Wasteful and Inappropriate Service Reduction): This CMS pilot will examine whether improved technologies (including AI/ML) and human clinical review can help curb waste, fraud, and inappropriate Medicare services in Original Medicare on a list of targeted outpatient procedures.

CMS issued an RFA and press materials about the model and solicited participation. The pilot will begin January 1, 2026 in selected states and will first address a set of outpatient items/services that CMS has deemed vulnerable to inappropriate use.

Operational specifics & guardrails CMS will use.

CMS positions WISeR as AI + clinician review (AI to indicate cases, licensed clinicians to implement final decisions) and as a means to expedite prior authorization of certain services. The RFA presents reporting rules and the obligations of the participants.

Significant issues brought up by clinicians and policy analysts.

• Traditional Medicare winds down: Traditional Medicare has traditionally lacked extensive prior authorization; any pilot that creates prior authorization will create an addition of delay and harm to the seniors.
• Vested interest mismatch: A contractor might have a reason to deny or under-deliver services; when an algorithm is optimised to achieve low utilisation, denials will reach a spike.
• Scale and expansion risk: CMS might increase scale, in case the pilot works (e.g., by cutting spending) and increase the stakes of any systemic bias or error inherent in the models.

3) What AI can contribute to appeals, actual tools, assets, and constraints.

What these AI appeal tools do

• Startups and nonprofits (examples: Claimable, Fight Health Insurance, and other new tools) apply generative AI and structured questionnaires to automatically generate appeal letters, build clinical arguments, and offer relevant citations (clinical guidelines, FDA approvals, plan language, etc.).
• There are some that are free and there are those that charge low fees. These tools have the potential to reduce a time consuming, skill based task that once took hours, to 10-30 minutes of workflow.

 

Why they help

• Keep the barrier to appeal low: Historically, less than 1 percent of denials are appealed, automation cuts down on time and knowledge barriers so that a larger number of patients can appeal.
• Regularity and evidence connection: AI can present guideline quotes, previous-authority precedents, and bundle the medical record plus explanation in a more comprehensible manner than in most instances by the patient alone.

Important limitations & risks

• Garbage-in, garbage-out: The quality of the input (medical records, denial letter) must be high to guarantee high quality of appeal. Unless AI outputs are verified by a clinician or patient advocate, the models may hallucinate or over-claim.
• Not a systemic solution: Automation of appeals benefits people, but does not prevent mass automated rejections or correct defective underlying models/policies. Numerous AI-issued denials are subsequently reversed on appeal, although that delays/harms the patients who do not appeal.

4) Real world protection and design considerations that could minimise harm.

These would ensure the risks would have significantly decreased (Providing that the policymakers or plans actually put them into effect).

Minimum protections to mandate in any AI-assisted system of prior authorization:

1. Documented, mandatory human clinician review of all adverse decisions, prior to final denial (not just audit afterwards). (SB 1120 direction).
2. Entitlement to an intelligible explanation of refusals (which model was applied, which are the guidelines, and who was the human reviewer who made the conclusion).
3. Denial rates, overturn rates on appeal, and demographic breakdowns so bias or disparate impacts are visible and monitored and publicly reported.
4. Independent validation and external audits of the model (accuracy, fairness, sensitivity for atypical cases).
5. Pre-litigation assistance (routing to free legal/advocacy assistance, and reimbursement of expedited reviews in case of emergency).

5) Rapid clinician, patient, and advocate recommendations.

• Clinicians: record medical necessity in a clear and proactive way, include evidence; mark urgent cases with a human review bypass.
• Patients: denial, appeal – AI tools can be used to write appeals in seconds, but the appeal must be reviewed by a clinician. Claimable, Fight Health Insurance and the likes can assist in first drafts.
• Activists and regulators: demand policies such as SB 1120 (human control, transparency), demand outcome reporting and invest in audits of pilot programs such as WISeR before a national rollout.

 

Conclusion:

The move by the U.S. health insurance market toward the use of AI in the claims approvals is a two-sided sword. On the one hand, it will speed up the processing, reduce the administrative expenditures and even provide new tools that will enable patients to combat denials.

On the other, it will put in jeopardy the automation of nuance, more unjust denials, and the traditional accessibility of Medicare. SB 1120 of California demonstrates how state regulations can impose human control and openness, whereas the future WISeR program of CMS will help to find out whether prior authorization under artificial intelligence may be carried out in a responsible manner.

The true protection is in balance: AI must be a helper, not a censor- it must support clinicians and patients, not become their judgment. Devoid of stringent control, responsibility and equitable appeal mechanisms, the same technology that is supposed to facilitate care delivery may actually procrastinate or withhold it at the time when the patient needs it the most.

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