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Somebody recently asked us to evaluate Athenahealth as a system. We wanted to provide a quick overview of what our perceptions were of that system.
I think there are a couple of different ways you can look at something. One is, “How effective is it in terms of revenue cycle management at billing and collecting?” There’s another way to look at that, which we tend to focus more on this time, which is the data and reporting and analysis capabilities.
From a billing standpoint, we’ve had quite a bit of experience with many different systems, including Athenahealth, because when we were running a billing company, we had some clients that had Athenahealth, so we had the opportunity to bill out of it. We’ve also had the chance to pull data out of it for many different clients for different things, everything from billing to analytics clients. We have a range of experience there.
I would say, from a billing standpoint, Athena was very good early on in the 2000s. I don’t think many companies out there had a payer policies database that they had built up that was large and accurate and effective to improve your ability to get clean claims out the door.
I think there’s an interesting conversation around like “What does it mean a clean claim and following payer policies, and using data to reevaluate that?” That’ll be a separate podcast.
They were very good at billing, building up that database. Athenahealth, I think, is much more of a software company than they are a billing company, although they do offer services and software. Their model seemed to grow and explode by getting hospital systems to make it one of the two EMRs that they offered. The practices were attached to the hospitals. The systems had to adopt as that was going gangbusters in the late 2000s and the early 2010s.
What Athena was good at was call it billing or call it the front-end side in terms of making sure that you passed clearinghouse edits, that you got through payer policies, that you had coding correct and diagnoses and modifiers and all those kind of things so that it was, quote, a clean claim. I think they were leaders in that arena.
Having said that, I think their sort of fatal flaw from a billing standpoint was the assumption that that would be all you needed. In other words, that if you had a quote clean claim and everything was correct with it, and you got it out the door to the payer, that meant you’re okay, and it would get paid.
They weren’t particularly good at the whole collecting part. That means having the backend capability to follow up on claims, do appeals, fix problems, and all of those sorts of things that are necessary to be effective. I think that was the challenge that they ran into. Their billing services were more of a data entry billing kind of service, not so much of a collecting one, where you employ your collectors or people to work the accounts receivable, even if you had Athenahealth doing the billing for you. That’s the key things with respect to the billing and collecting part of them.
Data and Reporting
When it comes to their reporting and data, they have excellent reporting systems. You can get a lot of data out of it. It’s easier than in many systems to do that. One of the challenges that we ran into with Athena that is not unique to them is that you see it in many places. When you get reports that are more supposed to be like an analysis, not raw data reports like a charge report or a collection report or an AR aging report, but something that you know is trying to synthesize and give you answers to something like an average reimbursement report. They had some challenges. The average reimbursement report starting with dollars per CPT had some fatal flaws that were astonishing.
For example, one of the things that we saw. By the way, to be clear, they don’t publicize this. It’s not obvious looking at it. You pull the data. When we kind of then looked at the data and tried to dig more deeply and cross-reference it from the raw data reports that we pulled from them to those synthesized reports, we found, for example, that it didn’t take into account units.
Problems to consider
Let me clarify what that means. If you billed out and collected $100 for 10 units, you collected on average $10 per CPT per unit. On a different one, you billed up 5 units and got 50 bucks. If you billed another one, you got $130 and 13 units. On average, you got $10 per unit. Well, if you looked at what I just said, where one got $50, one got $100, and one got $130, they would give you a $60 average. Not 10, 60. And 60, what is 60? I don’t even know. That didn’t even make any sense. What they were doing was they were taking the total reimbursement for that encounter for all of the units and just dividing it. That makes no sense.
The other major problem that we ran into was. Imagine you were billing a primary and then a secondary. If you’ve got $100 in total reimbursement where you’ve got $80 from the primary and $20 from the secondary, you’ve got $100 for that encounter, for that patient encounter. They didn’t say that you got $100 on average for that encounter. We’re assuming a simple case of N of 1. They said you’ve got an average reimbursement of $50 because they took the primary and the secondary effectively to be separate claims and averaged the two, which is wrong. It wasn’t apparent that was the case. You had to reverse engineer the reports to figure that out.
Those are the types of things where. I don’t think that’s unique to Athena. I know it’s not unique to Athena. The more the story is, we would counsel people, not just for Athenahealth, but this is a broad recommendation across all revenue cycle management systems. Get raw data out, validate the raw data, cross-reference it across reports, dig into it, and make sure that it is accurate. We found incredible problems with even raw data in different systems. Once you validate it and make sure, “Okay, I trust the raw data,” then do your analysis. Don’t trust the analysis that’s built in those systems for a whole combination of reasons.
We’ll get into that in another podcast, but that’s our summary of Athenahealth. We hope that’s helpful for you!