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Somebody recently asked us to evaluate Athena Health 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: Data and reporting and analysis capabilities.

Compare multiple medical billing systems

From a billing standpoint, we’ve had quite a bit of experience with many different systems, including Athena Health. When we were running a billing company, we had some clients that had Athena Health, 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. However, we have a range of experience there.

Why is this important?

I would say, from a billing standpoint, Athena was excellent early on in the 2000’s. I don’t think many companies out there had a payer policies database that they had built up that was large and accurate and practical 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.

Software vs. billing

They were very good at billing, building up that database. Athena Health, I think, is much more of a software company than they are a billing company, although they do offer services and software. For instance, their model seemed to grow and explode by getting hospital systems to make it one of the two EMRs they provided. The practices were attached to the hospitals. The systems had to adopt as that was going gangbusters in the late 2000’s and the early 2010’s.

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. Diagnoses and modifiers and all those kinds of things so that it was, quote, a clean claim. I think they were leaders in that arena.

Review the billing flaws

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 clean quote 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.

It all comes down to this.

Further, they weren’t particularly good at the whole collecting part. That means having the back end 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.

Moreover, their billing services were more of a data entry billing kind of service, not so much of a collecting one. To illustrate, it’s the type where you employ your collectors or people to work the accounts receivable, even if you had Athena Health doing the billing for you. That’s the critical thing concerning the billing and collecting part of them.

Data and reporting up to standard

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. In addition, when you get reports that are more supposed to be like an analysis, not raw data reports like a charge report, a collection report, or an AR aging report. Instead, it was something you know is trying to synthesize and give you answers to something like an average reimbursement report.

Undoubtedly, they had some challenges. The average reimbursement report starting with dollars per CPT, had some fatal flaws that were astonishing.

For example, let’s examine one of the things that we saw. By the way, to be clear, they don’t publicize this. It’s not apparent looking at it. You pull the data. When we kind of then looked at the data and tried to dig more deeply. We also tried 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.

Consider the following billing issues

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 five units and got 50 bucks. If you billed another one, you got $130 and 13 units. On average, you received $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. 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 this: Imagine you were billing a primary and then a secondary. Imagine if you’ve got $100 in total reimbursement where you’ve got $80 from the primary and $20 from the secondary. Then, you’ve got $100 for that encounter, for that patient encounter.

Nonetheless, 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 effects separate claims and averaged the two, which is wrong. It wasn’t apparent that was the case. Further, you had to reverse engineer the reports to figure that out.

Final thought

Those are the types of things where. I don’t think that’s unique to Athena. I know it’s not uncommon for Athena. The more the story is, we would counsel people, not just for Athena Health, but this is a broad recommendation across all revenue cycle management systems.

So what’s the answer?

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 unexpected 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 Athena Health. We hope that’s helpful for you!



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