You know how they say there is more than one way to peel a potato? On most days, we’d agree….except when it comes to price transparency data.

The differing CMS guidance between payer and hospital machine-readable files (MRFs) makes it difficult to triple and quadruple-check that any rate is correct. This is because different methods of presenting the data mean that the rate may change, even if it’s still technically correct in context (looking at you, percent of charge contracted rates). We’re happy to say that, along with a bunch of other things, we’ve finally found a way to octuple our data confidence: validated rates.

What is a Validated Rate?

At Turquoise, a validated rate is a rate that has been matched between hospital and payer MRFs. This means a few things, but most importantly it means that we were able to match the organizations+payers+rates within 3% between both MRFs. So if you’re working within Rate Sense, an “Average Rate” is considered validated if at least one of its input rates is a validated rate. A single “Rate” (rates expanded down from average roll-up in the animation below) is also considered validated if it meets the matching criteria mentioned above.

Watch validated rates in action!

Let’s do some matching, shall we?

Talking about matching is cool, but showing you what we mean is way better. Let’s go through a couple different examples of matching rates within Rate Sense.

This is an example of a common occurrence: one payer posting multiple rates for the same plan. Using hospital data, we are able to validate the third rate, $10,404 as posted by Methodist Mckinney Hospital as the rate they receive from Aetna for this service. VALIDATED!

Multiple rates from the same plan+payer combo can make it hard to nail down the exact fee schedule for each code. As you can see here, Tufts Health Plan+Cigna have quite a few rates! Matching them with hospital rates helps us cut out the noise and the right rate validated.

Lastly, a certain blue-shielded payer doesn't often post file labels…to rectify this, we use matched rates to understand what rates + contract Blue Cross networks are accessing for certain rates at facilities.

Why might a rate not be validated?

A point of clarification: If a rate has not yet been validated, we’re not saying it’s invalid. Sometimes data, like feelings, are layered with nuance. Functionally, that means some rates require additional validation steps or data-ception levels.

When we initially sat down at our desks, downed a latte, and put on our thinking caps, we wanted to find the most accurate method of detecting and identifying validated rates. To do this, we started by tackling a limited scope of rates that would allow us to establish a credible validation process: hospital rates found in both payer and hospital MRFs. These rates are the “easiest” since they are the most prevalent amongst our two datasets. This means we were able to launch this functionality faster and ensure that we weren’t accidentally labeling a bad rate as validated just to increase our total number of validated rates and boost our street cred. We know the internet keeps read receipts.

As new data is released each month, we are releasing new matching logic. This will continue to broaden the scope of rates being matched and increase the total number of validated rates. Some fun future plans include validating non-hospital rates (freestanding imaging center, an independent ASCs, a behavioral health practice, a mental health clinic, and rural critical access hospital—we see you!) and finding ways to combat syntax quirks in the data preventing matching from occurring (here’s looking at you multiple-NPIs-for-one-hospital).

That said, here’s some context to keep in mind when you’re thinking about rate matching:

  • If a hospital and payer post different NPI / EIN values for the same hospital, we are hard at work fixing this by creating new logic that can instead look at addresses and the facility listed within a claim.
  • Right now, our matching logic is simple; it’s currently identifying rates within 3%. Initially, our thinking was that the typical managed care contract has a rate escalator of about 3% each year. However, in practice, this quickly limited our scope since a 3% difference in a low rate (think below $100) is a meaningful difference, whereas a 3% difference in a high rate is marginal. We intend to update this logic shortly!
  • We are currently only looking at hospitals since that was the cleanest data to match between hospital and payer rates. All other provider types will be added over time.

Then why Validate at all?

Like I said above, all the wonkiness of data means that it’s quite difficult for anyone (even your favorite Turquoise-colored start-up) to definitively create and decide upon a single source of truth when it comes to the cost of care. Creating, maintaining, and sharing that single source of truth is our credo—we don’t shy away from its challenges! We bother to match hospital rates with payer rates because it is another means to ensure that our data is as accurate as possible. The fun part is that “as possible” keeps evolving! So our data evolves, too. Go test out Validated Rates, yourself!