Healthcare is a complex and fragmented sector that has long been weighed down by legacy systems and regulations.
If that sounds like a recipe for innovation, you might want to get your ears checked.
The industry’s longstanding institutional inertia when it comes to modernizing not just the business of care but the administrative workflows and processes supporting it might be beginning to thaw.
The reason? The evolution of agentic artificial intelligence, which represents the latest, autonomous iteration of the buzzy software technology.
“We are in a unique time in history,” Autonomize AI CEO Ganesh Padmanabhan said during a discussion hosted by PYMNTS CEO Karen Webster. “Until large language models specifically came about, it was impossible to distill information out of complex medical clinical documentation and contextualize it for different workflows. Now it’s possible,”
Still, Webster noted, agentic AI has become the latest talking point regardless of its real-world results in critical areas.
“It used to be generative AI, now it’s agentic AI,” she said. “But this is still an emerging technology. Why is now the time for it to be applied in healthcare, given that a lot of the industry is still trying to get its arms around basic automation?”
“Healthcare is one of those industries with a lot of knowledge work,” Padmanabhan said. “Data is often created by humans for other humans to consume, which makes automation innately harder.”
At the heart of the problem in healthcare is an industry drowning in administrative burdens. In the United States, an estimated $1.5 trillion is spent on healthcare administration annually, a cost that contributes to delayed care, clinician burnout and poor patient experience.
Targeting the ‘Business of Care’ With Agentic AI
Rather than tackling every facet of healthcare at once, Autonomize AI, which closed a $28 million funding round last month, focuses on what Padmanabhan called the “business of care.” That includes the invisible scaffolding that supports how care is delivered, such as insurance approvals, quality reporting and patient communication.
“Our focus is on building AI assistants, copilots and agents to augment the workforce,” Padmanabhan said. “There are two people often forgotten in healthcare: the providers who deliver care, and the patients who receive it. We’re putting them both back at the center.”
One example is prior authorization, a complex and manual process in which doctors seek insurer approval for medical procedures. It often involves faxes, weeks-long delays, and endless reviews by nurses and doctors, ultimately leaving patients in limbo.
“This whole process takes days, if not weeks,” Padmanabhan said. “It’s very error-prone. We aim to automate the intake, parse the information in the medical records, adjudicate that against policies, and summarize it for a clinician to make a decision in minutes.”
As Webster noted of the pain point: “After a doctor has said, ‘I want you to see XYZ doctor,’ you assume that call is going to happen. And then it doesn’t. You have to chase it down. That burden falls back on the patient.”
Building Trust in a High-Stakes Environment
For healthcare businesses, unburdening clinicians from administrative tasks isn’t just about productivity but can be about purpose, too.
“There’s a 300,000-nurse shortage in the provider spectrum,” Padmanabhan said. “Most are working at health plans doing paperwork. We need to enable a transition for them to do what they’re meant to do, which is provide care at the point of care.”
Yet automating workflows in healthcare isn’t as easy as flipping a switch.
“This is a hard problem,” Padmanabhan said. “Healthcare data isn’t fully digitized. There are gaps in knowledge.”
Autonomize AI’s own solution is to deploy “copilots” that identify which parts of a workflow can be automated, and then orchestrate seamless handoffs between AI and human workers, he said. Over time, these systems learn and improve based on real-world use.
Trust is the linchpin.
Webster pointed out the risks of incorrect output.
“In a clinical setting, the ramifications of wrong can be quite significant,” she said. “How do you build in those checks and balances?”
“You’ve got to build trust through product,” Padmanabhan said. “Showing evidence, provenance and allowing clinicians to go back to the source data is crucial.”
The long-term vision of agentic AI in healthcare isn’t just about optimizing current processes; it’s about redefining success.
“We don’t do healthcare in this country. We do sick care,” Padmanabhan said. “We need to shift from measuring mortality rates to tracking how many preventative interventions reduced chronic disease.”
Source: https://www.pymnts.com/
