Free athenaOne Practice Analysis: What It Shows

A read of your own data, before you buy anything

The free practice analysis is a no-obligation read of your own athenaOne data. We run it before anything is bought, and you keep it whether or not anything is bought.

It reports utilization, cancellation and no-show rates across your providers and locations, alongside trends in revenue, diagnoses, procedures, claims and payer mix. It costs nothing. If you read it and decide the answer is to change a scheduling template rather than to buy software, that is a fine outcome and we would rather find it out now.

What it is for is narrower and more useful than “insight”. It exists to answer one question: which workflow should you automate first? That decision is the deliverable. The document is just where it is written down.

Why the group average is the problem

Most practices already have every number in this analysis. They are in athenaOne. The gap is almost never access.

The gap is that the cross-cuts which answer an operational question are the ones nobody has time to build by hand, so the number that reaches the partners’ meeting is a group average. And a group average is the one number shape that cannot answer the question being asked of it.

A 12% no-show rate across a group tells you nothing actionable. The same rate split by provider, location, appointment type and how much notice the patient gave tells you where to act, and it frequently shows that one site or one appointment type is carrying most of it while everyone has been treating it as a general patient-behavior problem. Most groups rebuild that split in a spreadsheet each quarter, if at all.

The same is true of cancellations. A practice can post a respectable overall fill rate while recovering essentially nothing inside the day, because cancellations with several days of notice refill under almost any process, including a manual one. Blend the two and the number looks fine. Split them and the actual leak appears.

What the analysis contains

Utilization, per provider and per location. What share of each template was actually used, where the unused capacity sits, and whether it is concentrated in particular days or slot types.

Cancellations and no-shows, split by notice period. Same-day separated from advance, because they are different problems with different fixes. Broken out by provider, location and appointment type.

Appointment-type behavior. Which types run long, which get booked into the wrong template, and where the same visit is being scheduled inconsistently across sites. This one surprises people, because appointment types accumulate quietly over years and nobody audits them.

Revenue, procedure and diagnosis trends. Direction of travel over the period, at the level of granularity your own data supports.

Payer mix and claims patterns. Where the administrative load concentrates by payer, which is usually the strongest signal about which authorization or eligibility work is worth attacking first.

Where capacity is leaking. The synthesis. Not a score, not a grade: a short list of the two or three places where the data says time or capacity is going, ordered by size.

What it deliberately does not contain

No benchmark percentages borrowed from other practices. Your payer mix, your market and your specialty make most external benchmarks misleading at exactly the moment they feel reassuring.

No projected savings. A projection built before the workflow is scoped is a guess wearing a decimal point, and quoting one would undermine the point of showing you real numbers.

No clinical findings. This is an administrative operations analysis. It reads schedules, claims and queues, not care.

And no improvement figures attributed to us. How far any of these numbers move depends on which workflow gets built and on how much of your particular problem is addressable by automation at all. Neither is knowable from a data pull, and any vendor quoting you one before it has seen your data is quoting you somebody else’s.

Where the numbers come from

Everything in the analysis is derived from your athenaOne data, which means every figure can be checked against athenaOne rather than taken on trust.

Booked appointments come from GET /appointments/booked, which is what the day actually contained as opposed to what it was planned to contain. Remaining capacity comes from GET /appointments/open, bounded by department, provider and appointment type. Provider and department structure come from /providers and /departments, which is what makes the per-site and per-provider split possible rather than approximate. Appointment-type definitions come from /appointmenttypes. Coverage patterns come from the patient insurance records.

Production access to 820+ athenaOne APIs is why the analysis can be built from the live record instead of from an export somebody assembled by hand. Most marketplace vendors pick a narrower wedge and build deep inside it; we carry significant depth across the whole of athenaOne, and this is one of the places that shows up as something a practice can actually use.

We adapt to your practice, not the other way around

The standard version of this analysis covers the areas above, and for most practices that is the right shape.

What changes when your practice is different is the reading, not the report. A multi-site group with shared providers needs the utilization cut by provider-and-location together rather than by either alone, because a provider who looks underbooked overall is often fully booked at one site and thinly covered at another. A specialty practice running procedure series needs the series treated as one unit rather than as unrelated visits, or the drop-off between visit two and visit three stays invisible. A community health center with grant-funded programs needs the payer cut to keep those visible separately.

Those are scoping decisions we make with you before the analysis runs, which is the reason for the conversation rather than just a data connection. The direction of fit is the same as everywhere else in how we work: we map how your front office actually runs and read your numbers against that, rather than handing you a template and asking you to interpret yourself into it.

What we need, and what happens next

What we need from you: read access to your athenaOne data, and a short conversation about how your front office runs today. You do not need to pull reports, prepare anything, or write a specification. The point of the conversation is that the person who runs the workflow tells us what is supposed to happen, so we can compare it against what the data says is happening. Those two disagree more often than anyone expects, and the disagreement is usually the most valuable thing in the analysis.

What you get back: the analysis, walked through with you rather than emailed as a PDF, and a short ordered list of where the capacity is going.

What happens after that is your call. Some practices take the analysis and fix the top item with their own staff and a scheduling-template change. That is a legitimate outcome and it happens. Others pick one workflow to automate and we scope it, in writing, with the scope, the build time and the price agreed before work starts rather than discovered afterwards.

There is no obligation in either direction, and there is no version of this where you owe us something for having read your own numbers.

The short version

• A no-obligation read of your own athenaOne data, before you buy anything, and yours whether you buy or not.
• Its job is to decide which workflow to automate first. That decision is the deliverable.
• The value is the per-provider, per-location, per-notice-period split, because the group average hides exactly what you need to see.
• No borrowed benchmarks, no projected savings, no improvement figures attributed to us.
• Administrative operations only. Nothing in it is a clinical finding.
• What we need: read access, and an hour with the person who actually runs the front office.