The healthcare revenue cycle depends on teams investigating denied claims, checking payer rules and deciding which problems need a human’s attention. AI agents could take on some of that work, but the harder question is how to fit them into the way health systems operate.

For Mount Sinai Health System Inc., that starts with clear workflows and accountability when automation encounters an exception, according to Salonia Brown, system vice president of revenue cycle at Mount Sinai. Without those foundations, adding AI could make existing problems harder to manage.

“If you don’t have your workflows down pat, if you don’t have operational owners, when something kicks out, when you use AI, if there’s no ownership over that or accountability, then what you’re in essence doing is kind of digitizing dysfunction,” Brown said in an interview with theCUBE. “It’s very simple and easy to inject AI, but to do it the right way, making sure there’s governance [and] that the economics make sense? That takes a little bit of time.”

Brown and Akash Magoon, co-founder and chief executive officer of Adonis Technology Inc., along with other industry experts, spoke with theCUBE’s host Gemma Allen for the Adonis Reimagining Claim to Cash interview series on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how AI agents can handle claims work, how health systems govern exceptions and where human judgment remains essential. (* Disclosure below.)

Here are three insights from this exclusive interview series:

1. Mount Sinai brings AI agents into the healthcare revenue cycle.

Adonis is building agents to investigate denied claims and reduce the manual work involved in pursuing payment, according to Magoon. The company uses voice AI to call insurers for claims information and makes tens of thousands of those calls each week on behalf of its partners.

At Mount Sinai, Brown’s team helps determine which tasks can be automated and which require critical judgment. Employees need to trust the technology and know who is responsible when an exception arises, he noted. The goal is to free staff from repetitive administrative work so they can focus on more complex cases.

Watch the full interview on theCUBE.

2. Rush asks frontline staff what AI should take on.

Adonis distinguishes between frequent, repetitive tasks suited to automation and complex work that calls for better information and decisions, according to David Unger, managing director of health system sales at Adonis. For a high-value appeal, that can mean helping a team decide whether to pursue it and what evidence may improve its chances.

At Rush University System for Health, frontline employees help identify time-consuming steps that add little value, according to Blake Evans, system vice president of revenue cycle at Rush. Their firsthand knowledge helps the company decide where to automate and has made staff advocates for further changes.

Get the complete story on theCUBE.

3. Panoramic Health weighs AI’s promise against healthcare’s rules.

Adonis aims to identify revenue-cycle issues as they emerge and recommend a response, according to Doug Pickett, chief revenue officer of Adonis. An issue might call for automation, or it might reveal an upstream coding or provider-credentialing problem that a health system needs to fix before it grows.

Clinicians and operating teams are increasingly asking for AI tools that could reduce administrative burdens, according to Sap Sinha, chief executive officer of Panoramic Health. Yet rules governing billing, documentation and interactions with insurers have not kept pace with the technology. How those rules evolve could shape whether and how providers and insurers can use AI agents to interact over claims.

Check out the full discussion on theCUBE.

Here’s the complete video playlist from SiliconANGLE’s and theCUBE’s coverage of the Adonis Reimagining Claim to Cash interview series:

(* Disclosure: TheCUBE is a paid media partner for the Adonis Reimagining Claim to Cash interview series. Neither Adonis, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Image: ChatGPT/SiliconANGLE