North Dakota hospitals are deploying AI tools built for different patients
More than $18 billion flowed into healthcare AI in 2025, accounting for 46 cents of every dollar invested in American healthcare. That money is not staying on the coasts - it's reaching hospitals and clinics across the country, including North Dakota.
If you've been treated at a major health system recently, AI likely played a role somewhere in your care. It may have analyzed your imaging before a radiologist reviewed it. It may have flagged a risk in your chart. It may have determined whether your insurance approved a procedure.
Prior authorization and the first guardrails
Until this year, North Dakota had no rules governing how AI could be used in prior authorization - the process insurers use to approve or deny care. Physicians here spent up to 14 hours a week fighting those decisions, according to a 2023 survey by the American Medical Association.
In April 2025, Governor Kelly Armstrong signed Senate Bill 2280, establishing the first guardrails on AI's role in that process. The law was a concrete step forward. But prior authorization is only one way AI is entering healthcare.
Clinical decision support is the larger shift
AI systems are now embedded in emergency rooms to flag stroke risk, in ICUs to predict sepsis, and in primary care to identify patients who may need follow-up before they recognize the need themselves. These are deployed tools making real recommendations that affect patient care - not experiments.
The systems are also largely untested on the populations they serve. Most healthcare AI is built on data from large urban academic hospitals. North Dakota is not that.
Nearly 75% of rural counties in the state face primary care shortages. Patients drive further to appointments. They arrive later. They carry different combinations of chronic conditions shaped by North Dakota's demographics - including its Native American population, aging rural communities, and veterans.
When an AI tool built on data from a Boston hospital gets deployed at a clinic in Minot or a tribal health facility on the reservation, it is operating on a population it has never seen. The gap between how a tool performs in testing and how it performs on actual patients can be significant.
Testing tools on the right populations
One approach to closing that gap uses simulated patient populations calibrated to actual state demographics. These simulations test how clinical AI tools behave when the population shifts - without using real patient data or creating privacy risks.
The work matters because healthcare AI is accelerating. Nationally, 85% of healthcare organizations say they are increasing their AI budgets this year, according to NVIDIA's 2026 trends report. The federal government is actively pushing AI into clinical care.
North Dakota took a real step with SB 2280. The next step - making sure tools coming into clinics were tested on populations like ours - remains ahead.
For healthcare professionals managing these tools and their implications, understanding how AI systems perform across different patient populations is becoming essential. This includes staying current on AI for Healthcare and how to validate systems before deployment.
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