Agentic AI is entering healthcare through the work that happens between a medical decision and the care a patient eventually receives. These systems go beyond summarizing records or answering requests. They can collect information, move between software systems, initiate an action and continue working toward a defined result. Healthcare organizations are applying that capability to three persistent problems: securing insurance approval, keeping patients on track and connecting eligible people with clinical trials.
AI Agents Take On Prior Authorization’s Paper Chase
Prior authorization operates like a relay race in which medical records, coverage rules and approval forms pass among providers, payers and patients. A missing document can stop the entire process. An AI agent can start with a physician’s order, determine whether approval is required, retrieve the relevant clinical evidence and assemble the submission. It can then send the request to the insurer, monitor its status and respond when the payer asks for more information.
The regulatory calendar is making this use case especially timely. Under the CMS Interoperability and Prior Authorization Final Rule, affected payers generally must meet new application programming interface requirements by Jan. 1, 2027. Those APIs will let providers determine authorization requirements and exchange requests and decisions electronically. An agent could use those connections to manage the process from inside a provider’s existing system. It could also draft an appeal after a denial, supported by the patient’s records and the payer’s stated reason.
Clinicians would still review cases requiring medical judgment. The practical gain comes from removing the searches, status checks and repetitive data entry that consume staff time and can delay treatment.
AI Agents Keep Patients Moving After the Appointment
Healthcare often assigns patients a long list of next steps, then leaves them to coordinate the work. An agentic care navigator could turn a discharge plan or physician referral into a series of completed tasks. It could schedule a specialist visit, check insurance coverage, send preparation instructions and arrange transportation. After discharge, the agent could deliver medication reminders, ask about symptoms and confirm that the patient attended follow-up appointments.
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This model is beginning to move from concept to product. CVS Health is launching Health100, an AI-based platform developed with Google Cloud that is designed to give consumers proactive, real-time support across doctors, pharmacies and insurance providers. Research has also demonstrated the value of automating narrower steps in the care journey. An NIH-supported clinical trial found that an AI screening tool could help identify hospital patients with opioid use disorder and generate referrals to specialists.
An agent could extend that approach by tracking whether the referral led to an appointment and escalating stalled or high-risk cases. The agent wouldn’t diagnose a patient or change a prescription. Its job would be to keep the care plan moving and alert a clinician when human intervention is needed.
AI Agents Find Clinical Trials Patients Might Otherwise Miss
Finding an appropriate clinical trial can require clinicians and research coordinators to compare a patient’s diagnosis, treatment history, laboratory results and genetic markers against lengthy eligibility criteria. Much of that information sits in physician notes or other unstructured records. An AI agent can search those records, compare the findings with available trials and produce a ranked list of potential matches with an explanation for each recommendation.
A 2026 Nature Communications study of TrialMatchAI shows how far this capability has progressed. The system processes structured records and unstructured physician notes, retrieves relevant studies and conducts a criterion-by-criterion eligibility assessment. In real-world validation, it found at least one relevant trial among the top 20 recommendations for 92% of oncology patients. Experts validated more than 90% accuracy in its eligibility classifications.
An agentic version could take additional steps, including checking whether a trial is recruiting, identifying the closest participating site and preparing the patient’s information for review. It could also notify the research coordinator when new test results change a patient’s eligibility. Researchers and clinicians would retain authority over final screening, consent and enrollment. The agent’s value would come from finding opportunities that time-pressed medical teams or patients may never discover.
Agentic AI’s near-term healthcare value is strongest where coordination breaks down. Prior authorization, patient navigation and trial matching each require multiple systems, repeated follow-up and careful handling of sensitive information. With firm controls and human review, agents could reduce those administrative gaps while helping patients reach approved care, follow-up services and experimental treatments faster.
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