{
  "id": 7271604,
  "title": "Healthcare AI Agents Help Patients Get Care Faster",
  "url": "https://urgent.news/2026/09/14/healthcare-ai-agents-help-patients-get-care-faster",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-14T08:00:06.000Z",
  "source": {
    "name": "PYMNTS",
    "slug": "pymnts",
    "url": "https://www.pymnts.com/healthcare/2026/healthcare-ai-agents-help-patients-get-care-faster/"
  },
  "original_language": "en",
  "account": "Agentic artificial intelligence is being utilized within healthcare to streamline the processes between medical decisions and patient care. These sophisticated systems go beyond simple record summarization or request answering. They possess the ability to gather data, navigate through diverse software systems, initiate actions, and persistently work towards a predefined outcome. Healthcare institutions are leveraging this technology to tackle three recurring challenges: obtaining insurance approval, ensuring patient adherence, and connecting eligible individuals with clinical trials. AI Agents Streamline Prior Authorization Assistance In prior authorization, AI agents can mimic the functionality of a relay race, managing the flow of medical records, coverage rules, and approval forms among providers, payers, and patients. They can initiate with a physician's order, ascertain if approval is necessary, retrieve pertinent clinical evidence, and prepare the submission. Subsequently, they transmit the request to the insurer, monitor its status, and respond when the payer requests further information. The regulatory landscape is making this particular application of AI particularly relevant. Under the CMS Interoperability and Prior Authorization Final Rule, healthcare payers are expected to meet new application programming interface requirements by January 1, 2027. These APIs will enable providers to ascertain authorization prerequisites and exchange requests and decisions electronically. An AI agent could leverage these connections to manage the entire process within a provider's pre-existing system. Additionally, they could draft an appeal following a denial, utilizing the patient's records and the payer's rationale. Clinicians would still be required to review cases necessitating medical judgment. The practical benefit arises from eliminating the time-consuming searches, status checks, and repetitive data entry that can delay treatment. AI Agents Assist Patients After Their Appointments Healthcare typically provides patients with a long list of subsequent steps following their visits, leaving them to coordinate the tasks independently. An AI-powered care navigator could convert a discharge plan or physician referral into a series of accomplished tasks. It could arrange a specialist appointment, verify insurance coverage, deliver preparation instructions, and coordinate transportation. Post-discharge, the agent could provide medication reminders, inquire about symptoms, and confirm the patient's attendance at follow-up appointments. This concept is transitioning from theoretical to practical implementation. CVS Health is slated to introduce Health100, an AI-driven platform developed in collaboration with Google Cloud. This platform aims to deliver proactive, real-time assistance to consumers across various healthcare entities, such as doctors, pharmacies, and insurance providers. Research has also demonstrated the efficacy of automating narrower aspects of the care journey. An NIH-supported clinical trial revealed that an AI screening tool could identify hospital patients with opioid use disorder and generate referrals to specialists. An agent could further enhance this by tracking whether the referral resulted in an appointment and escalates stalled or high-risk cases. The agent's role would not involve diagnosing patients or altering prescriptions. Instead, its purpose would be to keep the care plan progressing and alert a clinician when human intervention is required. AI Agents Identify Clinical Trials Patients May Have Overlooked Identifying suitable clinical trials can entail clinicians and research coordinators comparing a patient's diagnosis, treatment history, laboratory results, and genetic markers against extensive eligibility criteria. A significant portion of this information resides in physician notes or other unstructured records. An AI agent can sift through these records, compare the findings with available trials, and generate a ranked list of potential matches along with explanations for each recommendation. A 2026 study published in Nature Communications, titled \"TrialMatchAI,\" exemplifies the advancements in this capability. The system can process structured records and unstructured physician notes, retrieve pertinent studies, and execute a criterion-by-criterion eligibility assessment. In practical validation, it identified 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 of this system could undertake additional steps, including verifying whether a trial is actively recruiting, pinpointing the nearest participating site, and preparing the patient's information for review. It could also notify the research coordinator when new test results alter a patient's eligibility. Researchers and clinicians would retain authority over the final screening, consent, and enrollment decisions. The agent's value would stem from uncovering opportunities that time-constrained medical teams or patients might otherwise overlook. Agentic AI's Near-Term Healthcare Value Lies in Coordination Breakdowns The immediate practical value of agentic AI in healthcare is most pronounced where coordination falters. Prior authorization, patient navigation, and trial matching each necessitate multiple systems, repeated follow-ups, and careful handling of sensitive data. With stringent controls and human oversight, agents could diminish administrative gaps while helping patients access approved care, follow-up services, and experimental treatments more swiftly. For all PYMNTS AI coverage, subscribe to the daily AI Newsletter.",
  "summary": "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 […] The post…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}