We ran a three day masterclass with Out-Of-Pocket on practical AI implementation across the revenue cycle. Over three 90 minute sessions, a cohort of revenue cycle, finance, and operations leaders worked through vendor selection, front and mid-cycle applications, and back-end denial and AR strategy together.
This was never meant to be a webinar. We built it as a working cohort, with breakout rooms, peer discussion, and homework built into every session. Attendance was application based, and we were genuinely selective about who we let in, because the value of a room like this comes from the people in it.
The conversations that came out of it were worth sharing more broadly, so here is a recap of what the three sessions covered.
Who was in the room
We heard from applications across provider groups, digital health companies, health systems, consulting firms, billing companies and MSOs, and even a payer or two. Attendees touched nearly every part of the revenue cycle: front-end operations, mid-cycle coding and claims, denials management, AR follow-up, reporting and analytics, and compliance were all represented in the room.
Most people joining were either piloting AI tools already or actively exploring where AI could fit into their operations. A recurring theme in the applications was the same one that carried through all three sessions: teams know AI could help, and they are trying to figure out exactly where.
Start with the problem, not the tool
The idea that carried the most weight across the whole masterclass was this: deciding which vendor to work with has to start with the problem, not the tool. Before evaluating any AI platform, the group worked through a simple but often skipped question. Is the bottleneck a people problem, a process problem, or a technology problem? Applying AI on top of a broken process does not fix the process, it just gets you to chaos faster.
"Deciding which vendor to work with needs to start with the problem, not the tool. If I could put that on a t-shirt, I'd wear it every time I talk to a CEO who says we need AI for everything." — Becky Carlson, Head of RCM, Joyful Health
We also spent real time on evaluating and de-risking vendor relationships, including how to pressure test a vendor's claims about scale, and where payer data limitations will cap even the best built tool. A strong vendor can build a beautiful product and still be limited by what a payer is willing to send back, so understanding that boundary early saves a lot of frustration later.
Choosing the right team model
Becky walked through the spectrum of team models organizations are choosing from today: fully in-house, hybrid, and fully outsourced. Each comes with its own tradeoffs. In-house gives you the most control but rarely gets prioritized for engineering resources. Fully outsourced gets you speed and expertise but means giving up a meaningful amount of operational control. Hybrid models offer strategic control with more flexibility, but only work well when responsibilities and handoffs are clearly defined between teams.
"We've seen too many instances where things fall through the cracks when communication isn't clean. I always jokingly call it handoffs and handshakes." — Becky Carlson, Head of RCM, Joyful Health
None of these models is inherently right or wrong. The group spent time mapping which one fits based on organization size, growth stage, and how much internal capability already exists to support the function.
Where AI creates real leverage today
Across front-end and mid-cycle workflows, a few applications stood out as genuinely useful right now. In prior authorization, AI can auto populate requests using clinical documentation and payer criteria, predict approval likelihood, and automate status tracking across payer portals so nothing sits unworked. In coding, coding copilots, HCC capture tools, and documentation improvement platforms are helping catch gaps before a claim goes out the door.
The group was equally clear on where AI still falls short. Novel diagnoses and off-label treatment requests still carry real uncertainty because there is not enough historical data to train against. Peer to peer escalations still require a clinician talking to a clinician. And AI code suggestions still require a qualified coder's review, since the coding decision on the claim remains the clinician's responsibility.
Denials are usually a front-end problem wearing a back-end costume
One idea worth repeating on its own: if you have a denials problem, it is usually a front-end problem showing up on the back end. Back-end RCM, meaning denials, AR follow-up, and underpayment identification, is also the hardest part of the revenue cycle to automate.
We walked through where AI can genuinely help here, like scoring open AR accounts by dollar value, payer responsiveness, and likelihood of collection so staff know where to spend their time. But even the best scoring model depends entirely on the accuracy of your own contract and fee schedule data, which shifts more often than most teams expect. The group also talked candidly about a familiar risk in RCM teams:
"I like to call these folks the golden billers. They bring so much value in making sure providers get paid, but they also introduce a layer of risk if that knowledge never gets disseminated across the team." — Becky Carlson, Head of RCM, Joyful Health
The data fragmentation problem underneath it all
A thread that ran through every session, regardless of the specific topic, was data fragmentation. Remittances, EOBs, payer portals, and yes, still faxes, are disparate data sources that rarely speak the same language. That fragmentation is exactly why back-end RCM is so hard to automate cleanly, and why so many organizations can see that revenue is missing without being able to explain where it went or why.
Attendees who mapped their own data flow across systems during breakout sessions found the same thing again and again: the gaps between platforms are usually where the denials are quietly being generated.
AI as a co-pilot, not a replacement
The organizations getting the most out of AI in RCM are the ones treating it as a co-pilot for their team, not a replacement for judgment. Every session came back to where human oversight and escalation still matter, especially in prior auth, coding, and complex denial resolution.
"AI models can score every open account by dollar value, payer responsiveness, and likelihood of collection. Staff work the accounts the AI surfaces. It's a co-pilot model: we're giving people direction that makes their job easier, but they still need to do the job, and we still need people in the loop." — Becky Carlson, Head of RCM, Joyful Health
ROI is bigger than dollars, and vendor fatigue is real
The final stretch of discussion turned toward implementation. Groups worked through what ROI actually means beyond a straight dollar comparison, including the time and effort it takes to stand up a new workflow, and who inside the organization has the bandwidth to own that.
"If you can attribute a minute of time spent to an activity, you can attribute a dollar value to it, whether that's the person taking the action or the thing they aren't able to do because they're focused on this instead." — Becky Carlson, Head of RCM, Joyful Health
Several attendees raised a familiar tension: the risk of accumulating five or six point solutions that solve individual problems well but do not talk to each other. The people doing this work every day already know where the problems are. The barrier is rarely a lack of expertise. It is bandwidth, ownership, and the infrastructure to connect what everyone already knows into something actionable.
Didn't get to attend?
This recap only scratches the surface of what came out of three sessions of live discussion, breakout conversations, and peer problem solving. If you weren't able to join and any of this resonates with what your team is navigating around denials, aged AR, or underpayments, we would love to hear from you. Reach out to us anytime at joyfulhealth.com, and we're happy to talk through what we covered or dig into your specific situation.
Thank you to Alex, Nikhil, and the whole Out-Of-Pocket team for building this with us.
