AI, Healthcare Subrogation, and Prudent Recovery Administration

By: Stewart P. Miller, Esq.

Every healthcare subrogation professional knows the file. Medical claims have been paying for months, and nothing initially distinguishes them from thousands of other claims in the system. Then a questionnaire, diagnosis code, provider note, or other piece of information reveals that the treatment followed an accident and another source of recovery may exist.

Sometimes that discovery happens early. Sometimes it happens after counsel is involved, litigation is underway, or settlement is approaching. That difference in timing can matter.

When a self-funded health plan pays medical claims arising from an injury for which a third party may be responsible, the Plan may have contractual rights to reimbursement or subrogation. Properly administering those rights can help restore Plan assets for the benefit of the Plan and its participants.

The challenge is not simply finding a potential recovery. It is identifying the right file early, developing the recovery accurately, applying the governing Plan terms, and pursuing the matter with sound legal and practical judgment.

Artificial intelligence can strengthen that process. Healthcare subrogation has always depended on information. Claims data, diagnosis codes, questionnaires, treatment patterns, accident indicators, provider information, and high-dollar claim review can all help identify matters involving potential third-party responsibility. But modern health plans process enormous volumes of claims, and a legitimate recovery opportunity may initially look like an ordinary medical claim among thousands of others.

AI and advanced analytics can help in this regard. Used appropriately, these tools can review large claim populations, identify patterns that may warrant investigation, and prioritize matters for human review. In practical terms, technology can help put the right file on the right desk sooner.

But finding the file is only the beginning. Once it reaches a recovery professional, the questions change. What actually happened? Which claims are related? What do the Plan terms provide? Is there potential for meaningful third-party recovery? What strategy makes sense under the circumstances?

No algorithm answers all of these questions simply because it identified an accident indicator. That is where the distinction between technology and capability becomes important.

AI can help surface opportunities, organize information, and reduce repetitive administrative work. It may also assist with claim histories, treatment timelines, potentially related charges, and information requiring follow-up. These efficiencies can give recovery professionals more time to focus on the work that ultimately determines whether a recovery is identified, preserved, negotiated, and collected.

But technology does not convert a flagged claim into money returned to the Plan. Experienced professionals do that by understanding Plan language, investigating the recovery environment, evaluating relatedness, recognizing legal and practical issues, communicating effectively, and determining the appropriate recovery strategy for the circumstances. A strong recovery process combines these functions rather than treating claim identification as the end product.

That combination matters from a fiduciary perspective as well and timing is important. In 2026, employer-sponsored health plan fiduciary administration remains under heightened scrutiny, while governance surrounding AI use continues to receive regulatory attention across healthcare and insurance. Neither development creates a new requirement for Plans to use AI. Together, however, they reinforce a familiar principle: Tools and service providers should support reasonable, informed, and disciplined Plan administration.

Healthcare subrogation fits comfortably within that principle. A Plan need not pursue every conceivable recovery opportunity, nor should it adopt technology simply because the technology exists. The objective is to have a recovery process that identifies appropriate opportunities, develops legitimate recovery interests, deploys resources intelligently, and protects Plan assets without sacrificing accuracy or consistency. AI can make this process more targeted, but human judgment remains essential.

Anyone who has worked on a complex recovery file knows that one wrong fact can send the discussion in the wrong direction. The wrong date of loss can pull in unrelated claims. An inaccurate treatment summary can blur an important distinction. A seemingly minor factual error can quickly become the focus of a negotiation that should have been about something else. AI-generated work product is therefore most valuable when it supports, rather than replaces, professional review.

The same principle applies to privacy and data governance. Healthcare subrogation necessarily involves claims information and protected health information. Any technology used in the recovery process should operate within the safeguards already required for sensitive Plan data. Efficiency is valuable, but it does not displace existing confidentiality and security obligations.

For Plan sponsors, the practical question should be broader than whether a recovery vendor uses AI. The more important question is whether the overall recovery program combines technology with the expertise necessary to turn identified opportunities into appropriate recoveries. That requires more than data analytics. It requires claims intelligence, knowledge of Plan language, legal resources, negotiation experience, disciplined workflows, and the ability to recognize when a matter requires a different level of attention.

Also, technology is becoming increasingly accessible. Specialized healthcare subrogation judgment is not. That distinction matters because AI can make a strong recovery process more efficient, but it can also make a weak process move faster. Better identification has limited value if potentially recoverable claims are not developed correctly. Better organization accomplishes little if Plan terms are misunderstood. Faster communication is not an advantage if the underlying position is inaccurate.

The strongest recovery programs use technology to amplify expertise rather than replace it.

The best technology does not make the recovery professional disappear. It gives that professional a better starting point. It helps surface the right matter sooner, organize information more efficiently, and direct attention where experienced judgment can add the most value.

What happens next still depends on people who understand healthcare subrogation. Healthcare subrogation is not simply a collections function. It is the administration of contractual recovery rights involving Plan assets, participant claims, third-party recoveries, and legal obligations.

Technology can help find the opportunity. Specialized expertise is what develops, protects, negotiates, and ultimately converts that opportunity into recovery for the Plan. AI can strengthen the process but prudence still requires people who know what to do with the information it provides.