When Health Insurers Turn to New AI to Decide Care

Health insurance companies, especially in the U.S., are increasingly deploying artificial intelligence (AI) and algorithmic systems to automate prior authorizations, claim approvals, and care denials. The goal: reduce cost, speed up decisions, cut administrative burden. But the shift has raised serious concerns about transparency, fairness, medical necessity, and patient rights.

Some developments to note:

• Several major Medicare Advantage insurers are already reported to use AI and predictive tools to deny or limit post-acute care (rehabilitation stays, home health care) for older patients.
• Private insurers also use AI in claims review, health utilization forecasting, and prior authorization.
• Legal complaints and class actions are emerging, alleging that AI systems are being misused to deny needed care.
• States are proposing or passing laws to regulate insurer use of AI in coverage decisions.
• Some startup efforts are using AI to help patients appeal denials more effectively.

Let’s unpack the technology, benefits, risks, and what’s next.

How AI Is Being Used in Insurance Decisions

Here’s how AI enters into the health care coverage process:

1. Prior Authorization Automation
   Insurers require prior approval for expensive or specialty care. AI tools evaluate requests, compare to internal rules or predictive models, and often issue an approval, denial, or flag for further review.
2. Predictive and Risk Models
   AI looks for patterns in claims, diagnoses, patient history, and demographics to forecast which procedures or patients present high “cost risk.” That can influence whether care is approved or denied.
3. Claim Review & Scoring
   After services are delivered, claims are checked via AI to decide if they match insurer criteria before payment — deciding which claims to pay, contest, or deny.
4. Network & Utilization Controls
   AI may suggest limiting use of certain facilities or services, or encouraging more conservative care. It can be used to steer patients to lower-cost options or disallow more expensive settings.
5. Denial Rate Optimization (“Denial Bots”)
   Some systems use AI to batch-process denials more aggressively, with minimal human involvement, aiming to reduce payouts and administrative costs.
6. Appeal Assistance Tools
   In response, patient-oriented AI tools are emerging to help generate appeal letters, navigate insurer policies, or counteract denials. Some companies offer these as advocacy tools.

What Benefits Are Promised

Proponents argue AI in coverage decisions can:

• Speed Up Decisions: Faster turnarounds, reduced waiting time for patients and providers.
• Cut Administrative Costs: Less manual review, fewer personnel, and streamlined workflows.
• Improve Consistency: Algorithms don’t tire, have consistent rules (if well designed) — reducing human error or variability.
• Detect Fraud, Waste, Overuse: AI can spot anomalous billing patterns or suspicious claims.
• Optimize Resource Use: Insurers can better allocate limited funds, avoid wasteful treatments, and steer care to cost-effective settings.

But these advantages depend heavily on how well the models are designed, validated, monitored, and constrained by oversight.

What’s Going Wrong — Risks, Evidence & Backlash

As AI systems are rolled out, a variety of concerns and failures have surfaced:

Overuse of Denials & Scope Creep

• Investigations show that some insurers have increased denial rates for post-acute care (rehab, skilled nursing) after adopting AI tools. In some cases, denial rates rose from ~10% to over 20% within years.
• Some insurers began subjecting far more cases to automated review, even for patients who would previously have been fast-approved.

Lack of Transparency & Explainability

• Many AI models operate as black boxes: patients, providers, or regulators cannot see or understand how the AI is making decisions.
• Insurers sometimes do not clearly disclose that AI was used or the basis of denials.

False Positives, Improper Denials & Harm

• Hospitals and clinicians report elevated rates of denials (or requests for more documentation) for legitimate care.
• In some lawsuits, patients allege that AI denials substituted for medical judgments, delaying needed care or pushing patients to pay out-of-pocket.

Bias and Discrimination

• If training data reflects past biases (age, race, socioeconomic status, diagnosis categories), AI may replicate or amplify those inequities.
• Some patient groups may suffer more from oversight, under-treatment, or misclassification.

Weak Oversight or Audit Controls

• Human review is often a fallback, but in many systems, appeals or manual override is infrequent or difficult.
• Regulators are just catching up; laws and policies may lag behind adoption of powerful AI tools.

Legal & Liability Issues

• Once AI is used, accountability is murky: who is responsible if a patient is harmed by an AI denial — the insurer, the model designer, or the reviewer?
• Lawsuits are already emerging targeting insurers using AI in claim denials without adequate safeguards or explanation.

Patient & Provider Pushback

• Medical providers complain of opaque denials, extra documentation burdens, and disrupted care pathways.
• Patients may not even realize denials were AI-based or know how to appeal effectively.

What’s Being Done — Policy, Regulation & Pushback

To mitigate risks, governments, state legislatures, and advocacy groups are pushing oversight:

• Federal Restrictions on AI Use in Medicare Advantage: In some cases, officials have stated insurers should not rely solely on algorithms to deny care without human oversight.
• State Laws: At least a half-dozen states are working on legislation requiring disclosures, audit rights, physician oversight, or limits on insurer AI decision-making.
• Senate & Congressional Investigations: E.g. Senate subcommittee reports have criticized top Medicare Advantage insurers for using AI in denying care for older adults.
• Lawsuits: Several class actions allege misuse of AI to wrongfully deny coverage, especially for seniors.
• AI Tools for Appeals: Startups like Counterforce Health are leveraging AI to help patients appeal insurer denials more effectively.
• Professional Ethics & Standards: Medical associations, bioethics groups, and health policy researchers advocate rules around explainability, patient rights, and oversight of automated decisions.

What Remains Unclear / Major Challenges

Here are key gaps and uncertainties we still need clarity on:

• Model Validation & Error Rates: How accurate are these AI systems? What is their false-positive rate (denying care that should be approved)?
• Oversight Structures: Who audits and regulates AI decisions? Are there independent third-party audits?
• Appeal and Dispute Mechanisms: When AI denies care, do patients have meaningful recourse or effective channels to challenge?
• Transparency to Patients: Are patients told that AI systems made decisions, and are they given rationale?
• Evolution of Fraud & Adversarial Behavior: As insurers get smarter, so will fraudsters — how will the arms race evolve?
• Ethical Boundaries: Which medical decisions should never be automated? What safeguards ensure AI does not override critical clinical discretion?
• Scalability & Costs: Running AI at scale (data, model hosting, updates) is expensive — will insurers invest in robust monitoring and safeguards?
• Impact on Clinical Practice: Will physicians change ordering behavior, adapt tests, or over-document to avoid AI denials? What burden does this create?

FAQs — Common Questions & Answers

Question	Answer
1. Can insurers legally use AI to deny care?	In many cases, yes — but with restrictions. Some federal guidelines forbid AI being the sole decision-maker for Medicare Advantage denials without human oversight. State laws may also impose limits.
2. Will human doctors still be involved in decisions?	Often yes — AI may provide a first pass or recommendation, but physicians or clinical reviewers typically have to review and override when needed. The degree of human involvement varies.
3. Can AI improve fairness or reduce inequities?	It has potential — if designed carefully. But if models use biased training data or opaque rules, AI may worsen inequalities. Oversight and fairness checks are critical.
4. What can patients do if care is denied by AI?	Patients should request the reasoning, appeal denials, seek independent review, engage patient advocacy groups, and use legal channels if necessary. AI-based appeal tools may help.
5. Will this reduce health care costs?	Possibly — insurers argue AI helps cut waste, overuse, or fraudulent claims. But cost reductions must be balanced against harm from denied but necessary care.
6. Can AI decisions be audited or reversed?	Auditing is possible if decision logs, rationales, and data are stored. But whether insurers or regulators will enforce audits, allow review, or reverse decisions is still evolving.
7. How fast is legislation catching up?	Slowly. Some states are advancing laws; federal policy is still playing catch-up. The technology is moving faster than regulatory structures in many cases.
8. Will this change the role of doctors?	Yes. Physicians may need to adapt by writing more detailed justifications, understanding insurer AI logic, negotiating denials, and overseeing AI decisions.

Conclusion

The use of AI by health insurers to approve or deny care is not a distant prospect — it’s already happening. The promise of faster decisions, cost-savings, and streamlined workflows is real. But so are the risks: opaque decision-making, wrongful denials, lack of recourse, bias, and ethical dilemmas.

Whether this shift leads to better health outcomes or more frustration and harm depends on how regulators, insurers, clinicians, and patients navigate the trade-offs — emphasizing transparency, fairness, accountability, and the preservation of human judgment in medical care.

Sources NBC News