At the start of 2026, the Utah Department of Commerce’s Office of Artificial Intelligence Policy announced a partnership with Doctronic to allow an AI system to legally renew certain prescriptions for patients with chronic health conditions.
The pilot presents a ground-breaking use case for AI in healthcare that can deliver potential benefits – assuming the right guardrails are in place. Utah’s approach reflects a proactive effort to explore how emerging technologies can help address access gaps, reduce friction in care, and test new models under controlled conditions. The pilot program also raises some questions – which is why we sat down with Sandy Shtab, Healthesystems VP of Industry and State Affairs, and Silvia Sacalis, VP of Clinical Services, to discuss the role of AI in treatment decisions and what needs to be considered in its governance.
How does this prescription renewal program work exactly?
Shtab: At the Doctronic prescription renewal portal, patients must confirm that they are located in Utah, enter the medication they want refilled, and then select an in-state pharmacy for fulfillment. Users must then upload their ID, along with a verification selfie and proof of an old prescription and then pay a $4 service fee.
The AI system reviews the information to ensure a prescription history exists, after which a health assessment is given, where patients must answer certain questions before the program issues a refill. If the AI is uncertain if a prescription should be renewed, it refers the patient to a Utah-licensed human physician.
Sacalis: It should be noted that Doctronic has not publicly detailed which specific clinical factors are evaluated. According to the contract between Doctronic and the state of Utah, after the patient completes their health assessment, Doctronic applies evidence-based clinical guidelines to determine renewal appropriateness.
There are some parameters in place that limit the scope of where AI is being applied. Doctronic can only renew prescriptions from a list of 192 drugs, none of which are controlled substances. The list does include many drugs for the treatment of chronic conditions including hypertension, diabetes, pain, inflammation, muscle spasms, asthma, COPD, anxiety and depression, erectile dysfunction, migraine/headache, and more.
What are the strengths and limitations of relying on patient‑reported data in an AI‑supported renewal process?
Sacalis: A system like this requires patients to effectively monitor and report their symptoms when interacting with AI. And it’s worth noting that the pilot is intentionally focused on renewal scenarios where patients are already familiar with their therapy and symptoms, which may make structured self-reporting more feasible than in an initial diagnostic or treatment decision.
However, there may be scenarios where self-related data is unreliable. Barriers such as low patient engagement, limited health literacy, cognitive impairment, or difficulty self-monitoring can all affect the quality and completeness of patient-reported information. As stewards of medication safety and outcomes, we must thoughtfully account for these variables.
Continue reading this article in full at the Healthesystems blog.
