Medical Debt in America in the AI Era: Can It Solve the Payout Problem?

This is Danika Kimball’s third contribution to the 21st Century Tech Blog. Danika lives in Boise, Idaho and is an SEO guru. When not writing, she likes to play video games and do podcasts about the TV reality show, The Bachelor.

When I received her article, I wrote back stating that I was unfamiliar with this problem, as I live in Canada, where there is universal healthcare coverage.

I shared an example. My daughter was born with complex heart disease. She has had three heart operations and long hospitalizations. My wife and I never received a bill, although we were aware of the cost of the procedures because the hospital showed us the billing submitted to the government for our daughter’s treatments. The numbers were well beyond our ability to pay, and if we had been living in the United States, they would have bankrupted us. Instead, we only had to pay for parking at the hospital’s lot, and even that was discounted because our daughter was a patient.

As much as Americans talk about examples of shortages and wait times in the Canadian health system, no one with a government-issued health card gets denied coverage or a bill. Canada may not have the same quantity of MRIs and CT scanners per population as the U.S., and we still suffer from a shortage of qualified physicians, but these issues are being remedied. 

America’s healthcare system is very different, and Danika describes just how much so. American healthcare is a for-profit system. What this means is that the cost to Americans for many lifesaving procedures without government or private health insurance may be out of reach even to the middle class. 

In a world where algorithms can detect disease, chatbots assist with triage, and robots perform surgeries with micrometre precision, it’s ironic that many Americans still face bankruptcy simply because they get hurt.

As America’s medical technologies and data systems leap forward, the financial safety nets meant to catch us after injury or illness remain decades behind. This growing disconnect is most evident when patients confront a harsh truth: even a successful injury claim might not cover the spiralling costs of care.

The Price of Progress

Modern healthcare has become increasingly digitized and efficient, but it hasn’t become cheaper. From artificial intelligence (AI)-powered diagnostics to robotic surgery and remote rehabilitation platforms, the cost of staying alive has, paradoxically, never been higher.

When an accident occurs, on the road, at work, or in a public space, victims often pursue legal action to recover costs. But even when the outcome favours the patient, a settlement may not come close to covering medical bills, lost wages, and long-term therapy. The gap between technological sophistication and financial support is widening.

Algorithms Can Predict Risk, But Can They Predict Equity?

AI has transformed risk assessment in healthcare and insurance. Machine learning models can now flag high-risk patients, simulate accident scenarios, and even estimate likely litigation outcomes. In theory, this should help courts, insurers, and lawyers arrive at fair and comprehensive settlements.

But predictive tools aren’t the same as protective ones. AI can map outcomes; it cannot make structural change. And if the inputs into an algorithm, like past verdicts, biased data sets, or incomplete billing, are flawed, the outputs reinforce injustice. For example, if previous settlements systematically undervalued certain types of injuries or patient demographics, new models will repeat those patterns under the guise of objectivity.

American Legal System is Stuck in Analog

While telemedicine apps may offer instant access to care, many legal processes remain frustratingly analog. Paperwork, delays, and outdated evaluation systems bog down claims. Courts often rely on historical precedent instead of real-time data. Attorneys often find themselves arguing over costs that have already doubled by the time a judge looks at a case.

And then comes the most painful part: the cheque. After fees, negotiations, and insurance adjustments, patients have to face the reality of underpaid settlements that barely touch their actual costs.

Smart Tools, Smarter Policies

If a body scan using LIDAR detects microscopic tumours using AI, we should be able to modernize how injury compensation works. Some promising innovations are starting to surface, including:

• Automated billing transparency tools provide a means to help patients and attorneys decode medical charges and flag inflated line items before they balloon into litigation obstacles.
• Decentralized blockchain-based claims processing provides time-stamping for each step in a claim, reducing fraud and speeding up approvals.
• AI-powered legal support helps lawyers build stronger cases by analyzing vast databases of similar claims, verdicts, and medical pricing data.

Yet these tools need to be paired with policy change. It’s not enough for tech to optimize the system. The system itself must evolve to prioritize patient outcomes over institutional convenience.

Moreover, insurance providers must be held accountable and adapt to modern medical economics. If AI enables real-time underwriting and adaptive pricing for premiums, the same innovation must be used to adjust coverage levels post-injury. A one-size-fits-all approach to payout structures is incompatible with a landscape where treatment plans vary greatly depending on access to technology.

Education also plays a key role. Many Americans don’t understand their rights, the legal process, or how to document medical and financial losses effectively. Community-based programs that partner with hospitals, clinics, and legal aid groups could bridge this knowledge gap, especially for low-income populations disproportionately affected by medical debt.

The Human Cost of Technological Gaps

Behind every data point is a person: a single parent hit by a distracted driver, a factory worker who has slipped on a neglected floor, a child injured by a defective product. These are not just line items in a spreadsheet; they are lives derailed. When compensation doesn’t cover real recovery, the future becomes a financial burden. Patients defer rehab, skip medications, or fall into debt spirals that impact housing, education, and mental health.

Using AI can help to calculate probabilities. It can forecast outcomes. But it can’t feel pain, or weigh fairness, or understand what it’s like to choose between another MRI or keeping the lights on.

Closing the Loop

To bridge the gap between medical innovation and financial justice, Americans need more than smart systems. A shift in how we value injury recovery is needed. Technology should serve patients not just at the point of diagnosis, but long after the discharge papers are signed.

Legal and medical professionals, insurers, and policymakers in the United States need to work together to create a new model, one that integrates AI’s capabilities with a deep, human-centred understanding of recovery. Because no matter how advanced our machines become, justice is still a profoundly human goal.