By Paul Boal
Receiving unpleasant news by mail is always stressful. It’s especially so for the more than 300 hospital leaders who have received price transparency on compliance warnings issued by the Centers for Medicare & Medicaid Services (CMS), knowing they could be fined more than $2 million.
The positive news for them is that no hospitals have been fined yet, even though rules governing the release of comprehensive hospital pricing data have been in effect since Jan. 1, 2021. However, reports of widespread non-compliance or partial compliance are still affecting hospitals’ reputations with the public, as well as their relationships with health insurance companies. So why haven’t they fully complied? While price transparency in healthcare is an achievable goal, it’s hardly a simple fix.
What Makes Releasing Hospital Pricing Data a Challenge?
Pricing medical care is complicated. Despite the assumption that insurance companies are setting the prices they pay hospitals, that’s not always true. Certainly, large, national insurance companies have an upper hand because they have the capacity to walk away. Most insurance companies aren’t that big, geographically dispersed, or aggressive with their business tactics, though. Therefore, they depend on keeping each hospital in-network. To do that, they accept hospital pricing requests without much debate.
This doesn’t mean that hospitals are “bad” players. On the contrary, the majority make as little as 2% profit annually. They’re also unlikely to have an internal team with the technical skills required to amass and integrate data into sophisticated pricing structures. Consequently, the data they release may not be complete or completely accurate. As such, they may be labeled as part of the “low-compliance” group even though they’re trying their best to comply with CMSprice transparency requirements, which ask for all pricing information to be in a comprehensive, machine-readable file that can actually be understood.
To be sure, many hospitals that are receiving a CMS warning letter may be compliant on some level. But different hospitals price services differently. One may charge a flat daily rate, another may charge based on the severity of a patient’s illness, another may charge based on the actual supplies that were needed and used. So when journalists and think-tank analysts point to low compliance levels, they miss the nuances.
Without a doubt, the methodologies for contracting and reporting are all over the spectrum. Rather than penalizing hospitals for noncompliance, though, regulatory agencies may want to encourage a starting point of bare minimum compliance. Bare minimum compliance isn’t the end objective, but it can serve as a baseline to prompt fairer negotiations that benefit both hospitals and insurance companies, not to mention patients.
If small insurance companies get access to usable price transparency information — even if it’s far from perfect in structure — they can start to be more strategic when working with hospital partners. In the reverse, hospitals can feel more confident at the negotiation table because their conversations will be backed by numbers.
How and where can hospitals begin? Below are a few recommendations for healthcare leaders to move forward with price transparency data collection and dissemination and how to reap the benefits.
1. Get a handle on the hospital’s data.
Many hospital executives have trouble complying with CMS regulations not because they aren’t interested in improving health outcomes through transparency but because they don’t know their data. They’re not sure what to expect in terms of payments. They might not even know whether they are getting the prices they expect.
It’s not possible to put any information together without initially putting data governance in place. Once data is gathered and assessed for accuracy, it can be turned into a machine-readable format. In general, simple, tabular forms can be easy to work with, though they’re not as efficient as a JSON format. It may be necessary for hospitals to enlist the help of someone with data architecture expertise to create a sensible, logical format.
2. Look for areas of friction and improvement in the data.
Once hospitals have some form of price data to share, they can use the data as a springboard to answer price transparency questions. These could include finding out which statewide hospitals have the best prices for specific services for specific patient groups or illnesses. Ideally, any questions asked should be very specific and narrowly focused.
After leveraging the data to come up with questions, leaders can then come up with potential actions to better position their hospitals against competitors. Taking an action-forward stance is critical. Healthcare finance is not a topic suitable for data science for the sake of discovery alone. Rather, data deserves to be used as a launching pad for actions.
3. Seek out ways to augment available data.
Hospitals are under a tremendous administrative burden. Teams may find it hard to collect all the data they need to move on action items. Proxies like social determinants of health or pricing data from similar scenarios in other states can add more credibility and reliability to incomplete data sets.
One caveat: Hospitals should watch out for potential traps like Simpson’s paradox and other forms of bias. Those types of issues can show up in real-world data where information is imperfect. Taking steps to verify data before enacting financial decisions can avoid foreseeable side effects like a negotiation that ends up leading to a lose-lose compromise even though it seems like a win-win one on the surface.
The benefits of price transparency in healthcare haven’t yet been fully realized, but hospitals and insurance companies are getting closer to reaping them. Rather than viewing CMS warning notes with fear, then, hospitals can use them as catalysts to meet compliance expectations and achieve full price transparency.
Paul Boal is the vice president of innovation at Amitech Solutions. He has two decades of experience in information management, analytics, and operational solutions; he’s also an adjunct professor of healthcare data and analytics at St. Louis University and Washington University.