How To Maximize Your Data Quality While Aiming For Price Transparency
By Paul Boal, vice president of innovation, Amitech Solutions.
It’s almost impossible to overestimate interest in price transparency. People want to know how their healthcare prices are determined, and payers do, too. But getting that information has proven to be a challenge. Why? Partially because so much pricing data isn’t readable or usable.
What makes healthcare data so hard to define and use? Historically, finance, actuarial, and contracting teams haven’t gathered or shared enough key information about pricing at the micro level. Consequently, a payer has no ability to make adjustments by independent services or categories. Rather, it might commit to boosting its “total contract value” by a certain percentage. That’s not a winning solution because it sets an artificial floor that has zero bearing on individual services, procedures, or cases.
The Case for More Accurate Price Transparency
One way around this problem is through comprehensive price transparency, which can allow for better precision and control over where to make increases and decreases. We saw Arkansas Blue Cross and Blue Shield try this approach successfully. Its leaders were getting ready to raise rates in sync with CMS rate increases. However, they took time to dive deeply into their true position. They learned they weren’t positioned as strongly as they assumed within a specific hospital system. Accordingly, they decided to forgo an annual rate bump in exchange for having a much better pricing position that attracted positive attention.
The bottom line is that healthcare pricing is complicated, whether you’re on the side of the price setter or the price taker. The public likes to assume that health insurance companies decide what a procedure costs. That might be the case with large, national insurance providers, but it’s not true across the board. Most insurance providers are smaller. Plus, they may never have had the opportunity to be informed or strategic about their pricing. For them, meeting hospital pricing demands has been a mainstay. But all that’s about to change.
As smaller providers get more access to thorough, complete price transparency information, they can be more tactical when building relationships with hospitals, exchanging price savings for member volume, and improving their overall spending.
Say a provider realizes most of its members are statistically unlikely to experience cardiac issues. It can then accept higher cardiac procedure rates in exchange for discounts in a different area that affects a higher percentage of the target population, like oncology. At the end of the day, the hospital gets the average rate increase it wants, and the provider exercises smart negotiating power.
Moving Toward Sophisticated Pricing Models Backed by High-Quality Data
What will be the special ingredient to enable pricing sophistication? Beyond a doubt, it’s high-quality data. Regrettably, the data quality in healthcare hasn’t been as high as is needed. Additionally, many providers and hospitals have such complex data structuring systems that it becomes difficult to interpret data points accurately.
Even data from pricing contracts can be deceptive. On paper, the contract may look like “Hospital does X. Payer reimburses Y.” In reality, a complex patient situation may change the landscape completely and confuse the accuracy of what appears to be simple fee-for-service data.
Given that fact, it becomes easy to see why patients get frustrated when their bills reflect something other than the typical contracted price they were quoted. Remember: Typical contracted prices come from “base” cases that don’t take into consideration provisos like the severity of illness. So what looks like a high or low contracted rate might be misleading. And when hospitals insert legal language into price transparency files, the data quality turns from readable to incomprehensible.
Making Better Data Quality in Healthcare a Reality
“It’s the best we can do.” That’s what many healthcare providers and hospitals have said until this point. It’s no longer good enough, though. With today’s software, all healthcare organizations should be able to mitigate the problems associated with poor-quality data in healthcare and move toward (nearly) error-free price transparency.
The following steps are good starting points.
- Have well-defined questions or hypotheses in mind when starting a price transparency data investigation.
Beginning with broad strokes doesn’t work well when it comes to price transparency. For instance, it’s not good enough to ask, “Who has the best prices in Missouri?” A more appropriate question might be, “Who has the best patient prices for X service in Missouri?”
Thinking through questions upfront provides a better road map for brainstorming how to get those answers. That road map can then become the framework for an action plan.
- Use data to shed light on the most pressing questions.
With a problem and potential follow-through items defined, leaders can begin sourcing data points. These could include published payer-specific prices, patient volume numbers, and patient demographics.
It can be difficult to find data, although it’s possible to find a few proxies such as the social determinants of health. Relying upon pricing data based on a similar scenario from another market or state could be helpful, too.
- Conduct a thorough analysis.
Once they have data, payers and hospitals can make assumptions. However, it’s important to note that data can be misleading. Case in point: Simpson’s paradox is a classical statistical issue. The paradox is that some data will show high-level patterns and trends that might not be consistent with the patterns and trends in underlying sub-population groups.
Our team ran into Simpson’s paradox during our price transparency work with a large Arkansas hospital system and the predominant payer in the region. The hospital’s pricing data seemed to indicate the payer was paying well above average. After looking more closely, both parties realized the data quality wasn’t up to par. The information was based on averages, and the payer had the majority of complex cases, which artificially raised the published rates. The results? The payer actually had a below-average cost, all things considered.
- Create an action plan.
After undergoing a complete analysis, healthcare leaders can move forward with choices. Those choices may need to be tweaked but can’t be avoided any longer. There’s just too much confusion and misinterpretation happening at the moment. The only way to finally put an end to “Who is getting a good deal versus who is paying too much?” will be through bold action.
The good news is that more industry decision makers are open to understanding their data, combining it with other sources of information, and examining it from multiple perspectives. In time, their work will lay a stronger foundation for healthcare data quality improvements in the coming years.
Is the data we have perfect? No, but it would be impossible to have “perfect” data in the healthcare space. The mechanisms and finances are too complex. Nevertheless, the available data absolutely informs smarter decisions that will benefit all stakeholders. And that’s the key to a better, more transparent future in healthcare.