The Rise of Autonomous Healthcare Agents
Agentic AI represents the next evolution in healthcare technology, moving beyond standard machine learning models that simply analyze data. These autonomous agents can make decisions with minimal human involvement across clinical, administrative, and patient-facing workflows. Unlike conventional generative AI that produces output from input to aid human decision-making, agentic AI can independently make decisions to achieve specific goals.
The growing momentum behind autonomous agents was prominently featured at the recent HIMSS25 Global Conference & Exhibition. However, discussions highlighted a critical challenge: while using agentic technology to improve operations shows promise, navigating the complexities of employing autonomous AI in clinical care requires careful consideration.
AI Governance and Patient Safety Concerns
Healthcare leaders emphasize that building trust and ensuring transparency remain the biggest hurdles to adoption in care management systems. At the HIMSS AI in Healthcare Forum, Dennis Chornenky, chief AI advisor at UC Davis Health, warned that governance has not kept pace with technology advancement. Current health AI regulations were designed for machine-learning models—not for autonomous AI decisions.
A controversial bill (H.R.238) introduced in the U.S. House of Representatives would amend the Federal Food, Drug, and Cosmetic Act to enable FDA-authorized AI and ML to qualify as practitioners eligible to prescribe medications. However, many technology developers believe autonomous AI isn’t ready for prescribing and other direct care functions.
Moving too quickly with AI agents could result in biased decision-making and potential risks to patient safety. Healthcare CIOs face pressure to adopt these technologies while ensuring safety as AI and automation disruption advances.
Clinical Workflow Applications
A primary driver for adopting agentic AI across organizations is alleviating computer workload on clinicians and administrative staff. Epic is integrating AI across applications to improve clinical efficiency, according to Seth Howard, Executive Vice President for R&D at Epic.
“We expect AI agents to help with pre-visit prep by chatting with patients about their needs, identifying missing tasks (such as labs), helping schedule and complete those tasks, and creating an easy-to-read summary,” Howard explained.
At HIMSS25, Garrett Adams, Epic’s Vice President of Research and Development, demonstrated a post-surgical patient assistant agent. This multimodal follow-up tool uses voice and image capabilities to interact with patients, evaluate recovery progress, and even suggest appointment changes based on comparative data from Epic’s Cosmos database—all while maintaining human verification for critical decisions.
Zoom also launched a public beta of ambient notes for telehealth and in-person appointments, allowing doctors to quickly review, edit, and send notes directly to EHR systems with minimal effort.
Administrative and Revenue Cycle Management
While clinical applications advance cautiously, revenue cycle management (RCM) is driving bold implementation of agentic AI. InterSystems debuted IntelliCare, a new AI-powered EHR that includes ambient listening and generative AI features to simplify administrative tasks. The system can generate automatic patient history summarizations and agentically prepopulate billing codes within the revenue cycle management system.
“My feeling is that agentic AI and open source models are the two topics of the year for 2025, from an AI point of view,” said Don Woodlock, InterSystems’ head of global healthcare solutions. The potential to reduce clinicians’ administrative burdens by automating tasks like sending letters, processing orders, and handling prior authorizations is significant.
eClinicalWorks showcased its AI document intelligence tool that automatically extracts patient data from incoming documents in various formats with 75-85% accuracy. The company also introduced healow Genie, an agent providing patients with instant answers to common inquiries 24/7 via voice, text, or chat.
The Human-AI Balance in Healthcare
Despite technological advances, healthcare technology leaders emphasize the importance of maintaining human oversight in clinical decision-making. eClinicalWorks CEO Girish Navani noted, “There’s a time and place where there will be agents even in clinical decision support, but that’s not the problem statement for today.”
Alicia Bassolino, athenahealth’s Vice President of Analytics and AI, views AI as “the ability to remove some of the mechanically oriented thinking, the task creation, the things that [providers] have to enter into the system versus the thinking that is very much their decision-making and their application of medical knowledge.”
Future Developments
As healthcare AI technology progresses to make decisions autonomously, EHR vendors are implementing administrative advantages in revenue cycle management. Automation across a broader spectrum of payers, providers, specialties, and other revenue cycle permutations is now possible with AI.
Navani reported that eClinicalWorks is heavily focused on implementing agentic AI in its RCM products, with promising early results: “The productivities are ridiculously off the charts. You can talk about an entire group of 50 people that do something else now, because you can have the agent do that work.”
The potential for AI agents to autonomously process electronic and paper-based data, manage claim coding verification, and conduct compliance checks represents a significant opportunity for healthcare organizations looking to improve efficiency while maintaining quality care.
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