Agentic AI is making strides in the healthcare sector by improving the connection between medical decisions and patient care. Unlike basic systems that merely summarize data or answer queries, these advanced AI tools actively collect information, integrate with various software platforms, initiate actions, and pursue specific outcomes. Healthcare institutions are leveraging this technology to tackle three significant challenges: obtaining insurance approvals, ensuring patient adherence to care plans, and linking eligible individuals with clinical trials.
Navigating Prior Authorization with AI Agents
The process of prior authorization can resemble a complex relay race, with medical documentation, coverage protocols, and approval forms being shuffled among patients, providers, and payers. Any missing document can disrupt the entire procedure. An AI agent can simplify this by starting from a physician's order, determining if authorization is needed, retrieving necessary clinical evidence, and compiling the submission. It can then forward the request to the insurer, track its progress, and respond to additional information requests from the payer.
This application of AI is particularly relevant as new regulatory deadlines approach. The CMS Interoperability and Prior Authorization Final Rule mandates that affected payers implement new application programming interfaces (APIs) by January 1, 2027. These APIs will enable seamless communication, allowing providers to verify authorization requirements and manage requests and decisions electronically. An AI agent could utilize these integrations to handle the authorization process within existing provider systems and even draft appeals in case of denials, armed with patient records and payer explanations.
While clinicians will continue to assess cases requiring medical judgments, AI agents will alleviate the burden of administrative tasks such as searching for information and repetitive data entry, thereby expediting patient treatment.
Enhancing Patient Care Post-Appointment with AI Agents
After a medical appointment, patients often face a lengthy list of follow-up actions without support. Here, an agentic care navigator can seamlessly transform a discharge plan or physician referral into actionable tasks. It can arrange specialist visits, verify insurance coverage, provide preparation guidelines, and facilitate transportation. Post-discharge, the agent can offer medication reminders, track symptoms, and confirm attendance at follow-up appointments.
This innovative approach is moving beyond mere concepts into practical applications. For instance, CVS Health is unveiling Health100, an AI platform created in collaboration with Google Cloud to provide consumers with real-time, proactive support across their healthcare journey, including interactions with doctors, pharmacies, and insurance companies. Further supporting this trend, research has shown the effectiveness of automating specific phases of patient care. A clinical trial funded by the NIH demonstrated that an AI screening tool can identify hospital patients dealing with opioid use disorder and facilitate referrals to specialists.
An AI agent could further enhance this process by monitoring whether referrals result in appointments and escalating cases that are stalled or at higher risk. While it wouldn't be involved in diagnosing or altering prescriptions, its role would be to keep the patient's care plan on track and inform clinicians when human intervention is necessary.
Facilitating Access to Clinical Trials with AI Agents
Locating suitable clinical trials can be a daunting task for healthcare providers, as it requires matching a patient’s medical history, diagnoses, lab results, and genetic information against extensive eligibility criteria. Much of this critical data resides in unstructured formats within physician notes. An AI agent has the capacity to sift through these records, cross-reference findings with available trials, and generate a prioritized list of matches, each accompanied by an explanation.
A notable study published in *Nature Communications* regarding TrialMatchAI illustrates the advancements in this area. The system effectively analyzes both structured and unstructured medical records, retrieves relevant studies, and assesses eligibility criteria methodically. In real-world tests, it identified at least one applicable trial among the top 20 suggestions for 92% of cancer patients, achieving over 90% accuracy in its eligibility assessments.
A more sophisticated AI agent could go further by confirming whether a trial is currently recruiting, pinpointing the nearest site, and preparing patient information for review. It could also alert research coordinators when new test results affect a patient's eligibility. Ultimately, researchers and clinicians would retain control over the final decision-making regarding screening, consent, and enrollment. The primary advantage of an AI agent lies in uncovering opportunities that busy medical teams or patients might overlook.
In the short term, the true promise of agentic AI in healthcare shines where coordination often falters. The challenges of prior authorization, patient navigation, and trial matching all involve multiple systems, extensive follow-ups, and the careful handling of sensitive information. With stringent controls and human oversight, these AI agents have the potential to bridge administrative gaps, helping patients access approved care, follow-up services, and experimental treatments more efficiently.
