Medical debt is climbing toward a new inflection point, and artificial intelligence is moving into the machinery that creates, prices, and collects it. As Republican-led health reforms take force this winter, companies using AI for medical coding, billing, and debt collection are finding a bigger market at the exact moment more patients may struggle to pay.
A report released last Tuesday found that more than one in four US adults reported owing medical debt in 2025. Even among people with health insurance, the proportion stood at about one in four, showing how coverage does not prevent patients from facing large bills, deductibles, co-pays, and other out-of-pocket costs.
Health cuts could push medical debt higher
Donald Trump’s HR1, known as the One Big Beautiful Bill Act, cut roughly $1tn in federal spending from the healthcare industry. The law is expected to result in about 17 million newly uninsured individuals through 2034, while the Urban Institute estimates that it will cause $278bn in uncompensated care by 2034.
Those figures point to a major shift in the medical billing landscape. As coverage shrinks and unpaid care grows, hospitals, billing companies, debt buyers, and collectors will face more accounts that require decisions about payment, discounts, and recovery.
April Kuehnhoff, senior attorney at the National Consumer Law Center, described the pressure facing patients: “It’s people who lose their coverage because they can’t afford it, or they can’t meet these new stringent requirements. It’s people who maybe they can afford it but the plan they get isn’t as good, and they are asked to have a higher deductible, higher co-pay, so they just have a higher out-of-pocket [cost]”.
Artificial intelligence now helps medical debt collectors determine personalized discounts for settlements. It also helps medical debt-buying companies decide the price they should offer for debt portfolios, bringing automated predictions into choices that can shape what patients owe and how creditors pursue repayment.
Billing platforms turn patient data into predictions
Cedar is an AI-driven billing platform backed by venture capital firms such as Andreessen Horowitz and JP Morgan. Its system uses data provided by hospitals and doctors to train a machine learning model capable of predicting a patient’s propensity to pay.
That prediction can influence how billing operations handle an account, from the payment process to settlement decisions. Cedar also deploys an AI chatbot named Kora to deflect calls to billing departments, adding an automated front door between patients and the people managing their bills.
The promise is clear: AI can help billing systems process more information and make more targeted decisions. The risk is just as direct. When a model predicts who can pay, the result can affect how a patient experiences a bill, a discount offer, or a collection effort.
Mehmet Oz, Centers for Medicare and Medicaid Services administrator, said AI will not save money in health care in the short run, but might over the long term. His warning is blunt: “Short-term, AI is going to be inflationary, because it’s going to turbocharge the ability of the current billing systems to work more effectively”.
AI can also help patients find financial aid
The same technology driving billing and collections is being aimed at a different problem: helping patients receive assistance before debt takes hold. In LA County, about 800,000 residents have medical bills they can’t pay, while an estimated 100 million adults in the US have some form of healthcare debt.
Naman Shah, a physician and epidemiologist at the Los Angeles County Department of Public Health, estimates that a new system to screen patients for financial aid could prevent several hundred million dollars of medical debt every year. The need exists because most people walk into hospitals and walk out without knowledge of financial assistance.
Presumptive eligibility systems offer a way to identify patients who may qualify for aid without waiting for them to navigate the process alone. Hospitals that have deployed these systems have reported as much as a 50% increase in the amount of financial aid they give patients.
Under a California law passed last year, all hospitals will have to start doing presumptive eligibility screening by next July. Los Angeles County partnered with the Hospital Association of Southern California to procure a digital presumptive eligibility system, with the partnership aiming to get it up and running by January.
Adena Tessler, regional vice president for the Hospital Association of Southern California, and Paul Young, senior vice president with the association, are part of that effort. Melanie Fontes Rainer, strategic planning leader at LA Care, is also named among the leaders connected to the work.
Shah said the public health department recognizes that a better screening system alone won’t eliminate medical debt. That point may define the next stage of healthcare AI: better tools can find assistance, price debt, and speed billing, but they cannot erase the financial pressure created when people lose coverage or face higher costs.
The contest ahead is over what these systems optimize. AI may make collections more precise, yet it can also help hospitals identify support before an unpaid bill becomes a lasting burden. As medical debt grows, that difference will shape whether automation adds force to the billing system or opens a path toward relief.
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