The Days to Bill Case Study You Need to See

[00:00:00] Erik Sunset: Quick question before we start the show today. What does it look like when 12 different surgery centers with 12 completely different starting points, some already fast, one taking over a month to bill, all end up landing in the same tight window within a couple days of each other? That’s not a hypothetical.

That’s a real result from a real ASC network we’re going to dig into today. One center went from almost 34 days to bill down to under three. Another was already a strong performer at four days and still cut that by more than half. Different centers, different histories, same destination. Stick around. I’m breaking down exactly how that happened and why the consistency here might actually be the more important story than speed.

Let’s get into it

all right, welcome back to the show. I’m your host, Eric. As always, this is The DocBuddy Journal. And before I jump into today’s case [00:01:00] study, I wanna spend a minute on something we’re really excited about, which is our continued support of the California Ambulatory Surgery Association’s annual conference.

Yes, of course, that’s CASA. It’s coming up very soon, uh, and CASA is just one of those events that matters if you’re operating in the California ASC space. This year, it’s September second through the fourth at the Monterey Conference Center, and it’s not just a trade show either. Um, I wanna shout out April Lightenberg and the entire CASA team that the program this year is built around the stuff that actually keeps administrators up at night: finance, HR, clinical and quality oversight, accreditation and compliance.

There’s even a great pre-conference session specifically on the KPIs that move the needle financially for a center, which honestly ties in really well with what we’re talking about today, because at the end of the day, a lot of what I’m about to walk through in this episode, which is this, this case study, is exactly that.

It’s a KPI, days to bill, and it’s one of the clearest financial health [00:02:00] indicators an ASC has. So we’re proud to once again support CASA, and if you’re gonna be in Monterey, be sure you come find us in the exhibit hall. Uh, but for now, let’s set the stage for today’s topic because before I get into the results, excuse me, I wanna make sure everyone listening actually understands what we’re measuring the impact of, and that, of course, is DocBuddy OpNote.

So we’re gonna back up just a little bit for anyone who’s newer to the show or who needs a refresher on DocBuddy OpNote. OpNote is our solution for instant operative reports, uh, their generation and immediate sign-off right from the point of care. The workflow is pretty simple conceptually, even though what’s happening behind it is sophisticated With OpNote, a surgeon dictates the operative report using free dictation and voice commands.

Then, and this is the part that changes everything downstream, they review and finalize that report in real time. No waiting on signatures, no transcriptionist in the loop, and that’s the big structural change [00:03:00] really. Once the surgeon signs off, the finalized report automatically syncs out to their PM and/or EHR system back to the surgeon’s clinic and by extension to their billing team or to their RCM partner.

And the reason that matters isn’t just convenience for the surgeon, although that’s real too. It’s that the traditional model, which is dictate, send a transcription, wait for a draft, review, correct, sign, that whole chain is where time leaks out of the process. Every handoff traditionally is a place where a report can sit in a queue.

On average, centers using OpNote are seeing completed, finalized operative reports in less than a day, some as fast in less than an hour, coming down to just mere minutes. And when you compare that to a transcription-dependent workflow where a note might not be finalized for days, uh, that’s a huge impact because once that report’s finalized, coding and billing can actually start, and as I mentioned, that’s [00:04:00] happening the same day in many cases.

So that’s the mechanism. Take the biggest bottleneck, which is transcription, uh, which nobody at the surgery center actually controls, by the way, and remove it entirely. Put the report in the surgeon’s hands at the point of care. Done. And that’s our setup for today’s case study because this next one isn’t just a single site, it’s actually 12 So getting into this case study, and it’s a brand new one that is recently published on docbuddy.com.

You better believe we’re gonna have a link to that in the show notes. And this is a little bit different from some of the other materials I’ve shared before because it is not just one facility story, it’s a network-wide rollout. So in this particular case, this is a joint venture, a regional health system partnered with a national ASC management company.

Facility names and the w- joint venture partner are anonymous, uh, but the shape of this organization is a really common one, uh, in this ASC space. And what they did was scale OpNote from an initial [00:05:00] pilot group up to twelve centers across their footprint. To measure the impact of OpNote, they were tracking a rolling average of days to bill at each individual center.

And this is a pretty cool part of the methodology. They compared the exact same calendar month one year apart. It’s June twenty-twenty-five versus June twenty-twenty-six in this case. So there’s no seasonal noise. There’s no, “Wow, well, volume was just lower with that month,” uh, or any excuses like that. We are talking apples to apples.

And rather than talk about produce, let’s talk about the numbers because the spread here, uh, going in is honestly pretty wild. Pre DocBuddy, going back to June twenty-twenty-five, their fastest center was already billing in about four days. Their slowest center was averaging almost thirty-four days to bill.

That’s a thirty-day gap between their best and worst site all in the same network. And for those listening on a finance committee trying to forecast cash flow across a growing multi-site portfolio, it’s also a nightmare. You can’t build a reliable model when your [00:06:00] sites behave that differently from each other.

So what happened after OpNote went live? Every single one of the twelve centers improved. Every one. But here’s the number that actually stopped me when I read it. Post DocBuddy, all twelve centers landed between one point six six and two point eight two days to bill. That’s it. That’s the entire range across the entire network So the site that was almost 34 days converged to about 2.7 days to bill, which puts it basically in the same neighborhood as the site that had already been a strong performer at four to five days going in.

And that, that’s the part I think is genuinely the headline here, more than any single percentage. Because if you look at the percentage improvement, sure, the worst performing site had the biggest swing, down like 92%, but every center dropped somewhere between about 53% and 92%. The network average went from 9.31 days to bill down to 2.21.

[00:07:00] That’s a 76% reduction network-wide. And this real story, and this is a direct quote from the write-up, is that the technology didn’t just make things faster, it made the network’s billing performance predictable site to site. And that’s the harder problem. You can sometimes fix a slow site with more staff or a process change or a new manager, but getting 12 different sites with 12 different histories and cultures and case mixes to all converge on nearly identical billing speed, that’s not a staffing fix.

That’s a structural fix And that lines up exactly with what I talked about earlier with OpNote itself. The biggest source of variability in time to bill isn’t really the surgeon all the time. It can be, but it’s generally and, uh, absolutely the transcription step. A fast transcriptionist versus a backlogged one, a clean dictation versus one full of blanks that bounces back for correction.

And that’s inconsistent by nature because it depends on people and cues outside the center’s control. Remove that step, and you remove the [00:08:00] variability along with the delay.

And that’s exactly why the case study notes that the sites with the worst, most variable starting points saw the largest gains. They obviously had the most bottleneck sitting there to remove in the first place. Uh, but there’s also a quote in there from the network’s business office manager that I think is worth reading.

She shared that implementing DocBuddy drastically improved the turnaround time from the date of service to coding and billing for operative procedures. And she specifically called out that because they’re no longer re-relying on transcriptionists to generate the note, doctors can sign off immediately, which also increased physician satisfaction.

So this is the two-sided win, uh, that I talk a lot about on this program. It’s not just a revenue cycle story, it’s a physician experience story too. Faster money and less admin burden on the surgeon. What’s not to like? Twelve centers, twelve different starting points, one converged outcome for a joint venture network scaling across multiple states and markets.

That’s what turns a good pilot into an actual [00:09:00] network-wide standard. So if there’s one thing to take away from today’s episode, it’s this: don’t just look at your fastest site and call it a win. Look at the gap between your fastest and your slowest. That gap is where your forecasting risk lives, and it’s often a bigger operational problem than the average itself.

If you want to see a full breakdown, the site-by-site numbers, the methodology, that case study is live now on our website, along with more detail on how OpNote works. Of course, we’re gonna have links to these things in the show notes. And one more time, if you’re headed to CASA’s annual conference in Monterey, September second through the fourth, come say hi.

We’d love to walk you through this in person. Thanks for listening, everybody. We’ll see you next time. And until then, be sure you’re subscribed on Apple Podcasts, Spotify, and YouTube so that you always get the newest episodes of the show. Once again, I’m your host, Derek. Thanks for listening. Take care.