Ep. 112: Melanie Howitt – Improving Profitability: Automating Claim Adjudication
Here’s what to expect on this week’s episode. 🎙️
In this week’s episode, Melanie Howitt, Transition & Implementation Manager at in2itive, shares how automation and AI are transforming back-end claim adjudication workflows in ASCs. Melanie shared:
💰 Up to 75% of claims are paid within 41 days of service
💰95% of claims receive some payer response (payment, denial, or request) within that same timeframe
💰 Shifting the first touchpoint from 30 to 41 days led to better staff utilization and fewer unnecessary touches
💰 Automation + AI are now used to auto-pull payer claim status, backfill missing info via payer portals, and identify trends using historical data
Melanie also breaks down how to align human workflows with smart automation—freeing up staff to handle complex cases and improving days to pay.
Listen in to hear how data-driven guardrails can prevent claims from slipping through the cracks and elevate your revenue cycle performance.
Episode Transcript
[00:00:00] Welcome to this week in Surgery Centers. If you are in the ASC industry, then you are in the right place every week. We’ll start the episode off by sharing an interesting conversation we had with our featured guest, and then we’ll close the episode by recapping the latest news impacting surgery centers.
We’re excited to share with you what we have, so let’s get started and see what the industry’s been up to.
Erica: Hi everyone. Here’s what you can expect on today’s episode. Today kicks off a brand new three-part series focused on improving profitability at your A SC. Our first guest is Melanie Howt, transition and implementation manager at Intuitive. She joins our host Latz to explore how AI and automation can boost your ability to collect outstanding balances and shares proven best practices.
She’s seen work in the field. After our conversation with Melanie, we’ll switch to our data and insights segment. HSC released our annual state [00:01:00] of the industry report in September, which analyzed client data from 590 surgery centers. Today we’ll spend a few minutes breaking down the most common case cancellation reasons.
Hope everyone enjoys the episode, and here’s what’s going on this week in surgery centers.
Nick: Melanie, welcome to the show.
Melanie: Hello. Thanks for having me.
Nick: Melanie. Can you give the audience a brief overview of your role at Intuitive?
Melanie: Sure. I am the transition and implementation manager here at Intuitive. It’s a pretty broad term that encompasses a lot of different pieces ranging from, client onboarding and making sure that those processes are successful to implementation of.
New automation IT projects. I spent a lot of time kind of playing both sides of the coin between our it initiatives as well as within operations.
Nick: Fantastic. And so in this episode, Melanie, [00:02:00] we wanted to drill into one specific piece of the revenue cycle process in claim adjudication and automation around that.
But to set the context here of what we’re gonna talk about. Can you give our listeners a feel for how you think about the key pieces of the overall revenue cycle process and where claim adjudication fits into that? Well,
Melanie: and claim adjudication falls into two different, contexts.
You have payer adjudication, which is everything that the payer’s doing once they’ve got the claim. But in the context of RCM and managing an overall AR claims adjudication really falls into all of the pieces that happen on our side with our staff, making sure that claims are processing the way they should.
Doing follow up, checking on claim status. Following up on denials, making sure appeals get out the door. It’s that all encompassing backend piece that is more than just what the payer is doing once they have the [00:03:00] claim.
Nick: Right. So backend after the claim submission,
Melanie: a after the claim submission.
Nick: Fantastic.
Got it. And so, as you started to mention there on the back end there’s touch points and different things going on. How have you thought about or what are the best practices around. Those process steps. And is this an area where AI can come into play in the process?
Melanie: Actually it’s pretty expansive.
Automation especially now plays a massive role in streamlining follow up processes. And that can happen a couple of ways. For instance, pulling claim statuses from the payers. Two, using the 2 77 x 12 messages as an example. Soon after, after claim drop on a regular cadence.
We are currently doing this to identify claims that are still in process, set to pay, or possibly denied or requiring additional information in a much faster and much more accurately than what we’ve been able to do in the past. The sooner that we’re able to [00:04:00] surface this information to our staff in an automated fashion, we reduce the cycle times of working problem claims.
We reduce the instances of people touching claims unnecessarily. AI tools will layer on top of this, and they’re able to backfill missing information by reaching out to like payer IBR systems or interacting with the payers directly for additional data gathering. This provides our staff with a clear picture so they’re able to take effective action in getting those claims adjudicated in the shortest amount of time possible.
Got it. When we utilize all of this information in our analytics in both a systemic and iterative ways, then we can also identify trends and patterns of behavior.
Nick: Got it. And so it sounds like, just to play that back the first step is really enhancing the visibility and information for your team on claim status.
And then the second layer on top of that [00:05:00] is AI and automation around what do you do to follow up? Is that. Is that a fair summary?
Melanie: That’s fair. Really what it comes down to is using both automation and AI as those data gathering points so that our staff are more effective in what they’re doing.
Doing that lift of the simple repetitive pieces so that the staff are actually able to take action on claims instead of just churning through them.
Nick: Yeah. Fantastic. And that’s what I wanted to ask you about next is. If you’re able to automate the more repeatable side, what is that free up the staff to focus on?
And is it that you need less staff for the same number of claims, or is it you’re spending more time on the complex ones?
Melanie: It’s a little bit of both. Ultimately. And AI to handle those simple repetitive tasks does make it possible to increase our [00:06:00] staff capacity. We can work with, a set number of staff to handle a much larger set of claims. But at the same time, because we’re taking that repetitive stuff off the table, they’re actually able to focus on the complex, the things that automation just can’t handle, that AI cannot effectively handle at this point. That’s the big piece for us is making sure that our staff are focused on the things they need to stay focused on that we can’t do in a simple, repetitive manner.
Nick: Yeah. Makes sense.
Melanie: Yeah. Human touch is, still important. There’s cognitive interpretation that a human can do that a machine just is not capable of.
Nick: So it’s getting the right, right task to the right. And follow up mechanism. And you guys have enough volume where I’m sure you’ve done a lot of testing.
Have you found that there’s certain frequency and certain, number of sweet spot of touch points in the [00:07:00] follow up to be most effective or less effective?
Melanie: The biggest piece that we’ve done some pretty extensive ANA analytics on and. We’ve done it over the course of time and we’ve come back to it a few times, and results have actually stayed pretty consistent.
Is that first touch point, date we’ve found that, upwards of 75% of claims will pay within 41 days and from data service. And 95% of claims are going to have some kind of response within that timeframe, whether they’re coming back and asking for additional information. Maybe a denial is.
A patient needs to reach out for coordination of benefits. We get some kind of response from the payer 95% of the time in within that 41 day period. And for us that’s a pretty massive shift. Historically, we’ve always worked within this concept of you touch every claim that’s sitting out there every 30 days no matter what by extending that first touch timeframe by 11 days.
We [00:08:00] see a much better use of our staff’s time in being able to focus on accounts that actually require intervention versus, like I said, just churning through accounts for the sake of touching them. And our staff capacity also gets enhanced which when you pair it with automation and AI driven statusing, we actually have a functional guardrail in place now to prevent claims from falling through cracks.
Nick: Yeah, that’s super interesting. Sounds, sounds like the analytics is allowing you to better match. The timing of your outreach and process with the pays. Absolutely. Follow timing and process. Yep. And you mentioned a couple metrics in there and I wanted to double click and pull those out. Do you guys, are you guys pretty maniacal about first touch date as a metric and KP that you track?
Melanie: That is absolutely one that we keep track of. But overall touch rates. Is something to be paying attention to. And we look at it really within, the scope of, our clients and our total volume of claims that [00:09:00] we look at. But those account touch rates is a good indicator of staff effectiveness once a claim is gone to a payer, how often are staff needing to touch an account before payments received?
A high average rate either globally, individually, or maybe related specifically to a payer may indicate that claims are being touched too soon. Maybe you’ve got some staff upskill opportunities or even potential payer side issues that may need to be escalated. Another piece that we throw in there, when we are looking at the data is, our days to pay, which is different than days in ar they’re often conflated, but they aren’t calculations.
Your days to pay is. How long does it take you to get the claim out the door? Combined with how long may it sit in rejection status of the clearinghouse? Along with those, the days that a payer’s gonna adjudicate, when you’re looking at this number from a payer level, you’ve got a good baseline determining not just [00:10:00] potential issues with maybe getting claims out the door, but tracking over time potential issues or delays that happen with the payers.
Nick: Got it. And I wanna make sure I understand that. What’s the primary difference in days to pay versus days in ar?
Melanie: Yeah, days to pay. Days to pay is, like I said, specifically the sum of how long does it take you to get a claim out the door? How long does it sit in rejection? With the clearinghouse and how long is it gonna sit with the payer?
Your days in AR is more of an amalgamated looking at three months worth of data between your cash coming in as long as well as your net revenue. So it’s a slightly different calculation and it’s looking at two different sets of numbers. They can overall end up being pretty close.
Nick: This should trend.
Directionally the same. Right. But yeah, days in AOR is cash in and out.
Melanie: Yeah. And whereas this is actually looking at time as a matter of how long does it take from [00:11:00] that data service to that data payment, and how long does that take? Like I said, different calculations. They tend to track pretty close most of the time.
But when you start seeing the divergence is when that really comes into this place of you’ve got something to take a look at.
Nick: Got it. That’s helpful. Okay. And for ASCs that wanna improve their follow up process, what low hanging fruit do you recommend tackling first,
Melanie: honestly, just as we started with your lowest hanging fruit is improving your in improving processes is having clear expectations around that first touch follow up timeframe, too soon.
And you’re gonna have staff spending time on accounts they don’t really need to touch too late. And you’re gonna see a potential drag on cash flow as claims fall through the crack. Automation of this activity where it’s possible speeds up your over overall cycle time and reduces your days to pay and ensuring your staff are bogged down with, non-value added steps.
Nick: Sure. Okay. And last question for you today, Melanie, and we do this every week with our [00:12:00] guests. What is one thing our listeners can do this week to improve their surgery centers?
Melanie: Ultimately, a concentrated effort on maintaining a low claim submission timeframe, your days to bill. And analyzing those ch claim adjudication bottlenecks within claim rejections and payer adjudication lags.
It’ll give you a solid starting point to identify potential areas for improvement. Or if everything is going great, celebrate the suce success of your staff.
Nick: Fantastic. I like the celebration piece. And if we double click on days to bill you have one or two tips for centers on how to get, how to bill faster.
Melanie: On how to bill faster, your documentation being in line as quickly as possible. Ultimately you wanna be looking at your mid days to be at, two to three from the date of service documentation’s done. Coding’s done charge entries done by day three.
Nick: Fantastic. Melanie, thanks so much for joining us.[00:13:00]
Erica: HSC Pathways released an updated version of our state of the industry report this past September, highlighting best practices, key process steps, and KPIs for every step of the patient journey and for nearly every recurring administrative duty. Most importantly, using our own unique data set from our clients, we were able to extract data points so that anyone in the industry could compare themselves to their peers.
Two quick disclaimers. We only pulled data from clients who gave us permission, and we omitted any extreme outliers. So today we’re taking a closer look at a number that’s costing surgery centers much more than you might think. Case cancellations. According to the 2024 state of the industry report, 21% of cases at an A SC are canceled on average, which, if think about it, is a little more than one out of every five patients.
When we looked at the data, here’s what stood out. Patient canceling is the number one reason with roughly 45,000 cases. The provider canceling is the number two [00:14:00] reason with roughly 41,000 cases. So combined, if you think about those two, the top two reasons account for over 40% of case cancellations.
Other notable contributors a reschedule was around 12,800 cases. Patient health issues was around 6,800. Surgery center canceling was also around 6,800. Insurance issues was around 5,600, and then scheduling errors was around 4,500. Now there’s about 20 reasons or so on the actual graph. So, if you wanna see the full breakout and the other reasons as well, I’ll link to the full state of the industry report in the episode notes, so you can check that out and follow along as if you’d like.
Now, some of these reasons are completely unavoidable, right? Sudden patient illness, family emergencies, environmental events, but many are within your control. So let’s break down a few key categories. Obviously, let’s [00:15:00] start with the number one reason the patient canceling. While not all are preventable, many stem from fear, confusion, or cost concerns.
So I’d recommend having subcategories here to get even deeper into the reasons. And if you see this number growing, remember that proactive communication matters. So using text reminders, pre-op instructions make sure your estimates are set, sent well in advance. If the patient shows up for the first time just to learn about their out of, I don’t like this.
Let’s start with the patient canceling again. This is the number one reason for a case being canceled. So not all of these are preventable of course, but many do stem from fear, confusion, or cost concerns, which are things you can get ahead of. I’d also recommend having subcategories here to get even deeper into the reasons, just so you can see, okay, out of, all the reasons a patient canceled, maybe there are a few that are in our control, [00:16:00] or maybe we’re seeing an uptick somewhere that you can get ahead of and address.
And at the end of the day, just proactive communication matters. So use those text reminders. Send the pre-op instructions through text, through email and also make sure you’re sending out your financial estimates well in advance. Of the procedure, all of that will help contribute to improving that number.
The second reason is that the provider canceled. Now, this is a tough one, but again, I’d recommend having subcategories to give you even more context here. Why did the provider cancel? That’ll help ev, shed even more light on this issue. I. I would also recommend here sharing those provider cancellation statistics in your a SC somewhere so that all your staff can see, like the break room.
You’ll also wanna share these numbers at your board meetings for sure. As providers are extremely competitive and no one wants to look like they’re the reason that there’s disruption to the daily workflow or things are falling behind. So. I would first give your docs a heads up. Maybe you [00:17:00] say, hey, starting in Q4 I’ll be sharing individual pro provider cancellation rates.
It just could be an effective way to try to lower this number. The third reason, so let’s start talk about insurance or financial issues. Over 10,000 cancellations were linked to insurance authorization or financial concerns. To improve these numbers, at the bare minimum, you should be re-verifying insurance at least twice.
So once at scheduling, and then once the week of the procedure. Automated verification tools can catch these issues before they become cancellations. You’ll also wanna send each patient a detailed itemized invoice that is void of any medical jargon, so they know exactly what they’re paying for and when.
This will help, again, reduce those last minute cancellations due to financial confusion or just financial stress. Fourth reason here. Let’s talk about scheduling errors in labs. So scheduling and lab related cancellations accounted for over 8,000 cases being canceled. In this instance, using [00:18:00] a centralized case management where your entire care team, so surgeons and anesthesia front desk, can access updates in real time.
The earlier you spot an issue, the more time you have to course correct. And lastly, if you’re not already tracking your own cancellation reasons, obviously you should start now. But if you wanna take it a step further, this is where a custom GPT can really be a game changer for you. So a custom GPT is basically a tailored AI assistant that analyzes your specific data and workflows and can help deliver super fast actionable insights.
So you don’t need any technical experience to create one. It only takes a few minutes to set up, and you can do so without using any PHI or integrating it with your existing tech. So all of your data is completely safe. You do need the paid version of chat GPT, but it’s only about $21 per month and it’s so worth it.
So again, check out chat, GPT, get the paid version, and start creating your own custom [00:19:00] gpt. And if you’ve never used one before for anything, I actually have a video on how you can create one. So I’ll link that in the episode notes as well. But back to case cancellations. So you can upload in your custom GPT, your own historical case data, going back as long as you’d like.
So you know, obviously the longer the better, but let’s say you have it for a year, two, three years, all of it can be super helpful. And then moving forward, maybe you get into a routine of uploading data into your custom GPT every 30 or 60 days, let’s say. So then in seconds you can ask things like, what were the top three reasons for case cancellations last quarter?
So. Or which physicians had the highest cancellation rates? Rates in March, or let’s say you’re in that board meeting and on the spot, a physician asks How many cases have been canceled due to anesthesia? Is that number going up or down? You can immediately ask your custom GPT and get an answer on the spot.
Or even better, let’s say you upload a few gears worth of data, [00:20:00] you can ask it looking ahead to Q3 2025. Please use our historical data to look at case cancellation reasons and share what we should start prepping for now. So the point is you don’t have to just use this data for retroactive insights, but instead you can also use it to improve future KPIs and prevent what you can from happening again.
Case cancellations are inevitable, but 21% is just not sustainable. So use your tools, streamline your workflows, talk to your patients and lean into your data. And if you’re interested in more data points and use cases, head to our website to check out the full state of the industry report to get your hands on even more data.
And that officially wraps up this week’s podcast. Thank you as always for spending a few minutes of your week with us. Make sure to subscribe or leave a review on whichever platform you’re listening from. I hope you have a great day, and we will see you again next week.[00:21:00]