Agentic AI Poised to Tackle Healthcare’s $1.5 Trillion Administrative Burden
The healthcare industry, long hampered by outdated systems and complex regulations, may finally be on the cusp of a technological shift. The emergence of agentic artificial intelligence – the latest evolution of AI software – is offering a potential solution to the massive administrative costs plaguing the sector, estimated at $1.5 trillion annually in the United States.
For years, attempts to modernize healthcare have stalled, but a confluence of factors is creating a unique opportunity for change. According to a recent discussion hosted by PYMNTS CEO Karen Webster and Autonomize AI CEO Ganesh Padmanabhan, advancements in large language models have unlocked the ability to efficiently process and contextualize the complex documentation inherent in medical care. “We are in a unique time in history,” Padmanabhan stated. “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.”
However, Webster cautioned that despite the buzz surrounding agentic AI, its real-world impact remains to be seen. “It used to be generative AI, now it’s agentic AI,” she observed. “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?”
The Challenge of ‘Knowledge Work’ in Healthcare
One of the primary obstacles to automation in healthcare is the nature of the work itself. Padmanabhan explained that healthcare relies heavily on “knowledge work,” where data is created by humans for human consumption, making automation inherently more difficult. This administrative overload contributes to delayed care, clinician burnout, and a diminished patient experience.
Rather than attempting a complete overhaul, Autonomize AI is focusing on what Padmanabhan calls the “business of care” – the supporting infrastructure of healthcare delivery, including insurance approvals, quality reporting, and patient communication. The company, which recently secured $28 million in funding, aims to build AI assistants, copilots, and agents to augment the existing workforce. “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,” Padmanabhan said.
Streamlining Prior Authorization with AI
A prime example of this approach is the automation of prior authorization, a notoriously cumbersome process requiring doctors to obtain insurer approval for medical procedures. This often involves manual paperwork, fax machines, and lengthy delays, leaving patients uncertain about their care. Autonomize AI’s solution aims to automate the entire process, from intake and data parsing to policy adjudication and clinician summarization, potentially reducing approval times from days or weeks to mere minutes.
Webster highlighted the patient impact of these delays, noting, “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.”
Addressing the Nursing Shortage and Building Trust
Beyond efficiency gains, automating administrative tasks could also address the critical nursing shortage. Padmanabhan pointed out that a significant number of nurses are currently employed by health plans performing paperwork, and AI could free them up to focus on direct patient care. “There’s a 300,000-nurse shortage in the provider spectrum,” he stated. “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.”
However, implementing AI in healthcare is not without its challenges. Padmanabhan acknowledged that healthcare data is often fragmented and not fully digitized. To overcome this, Autonomize AI is deploying “copilots” that identify automation opportunities and orchestrate seamless handoffs between AI and human workers, allowing the systems to learn and improve over time.
Crucially, trust is paramount. Webster emphasized the potential risks of inaccurate AI output in a high-stakes clinical environment. “In a clinical setting, the ramifications of wrong can be quite significant,” she said. Padmanabhan responded that building trust requires transparency, providing evidence of accuracy, and allowing clinicians to verify information by tracing it back to the original source data.
From Sick Care to Preventative Care
The long-term vision extends beyond simply optimizing existing processes. Padmanabhan believes agentic AI can help redefine success in healthcare, shifting the focus from treating illness to preventing it. “We don’t do healthcare in this country. We do sick care,” he asserted. “We need to shift from measuring mortality rates to tracking how many preventative interventions reduced chronic disease.”
