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By Paul Boal
What is price transparency in healthcare? Consider this analogy: You’re scheduling an operating room, but you don’t have sufficient information to know how long the procedure will take. You block out an hour for it, but the procedure only takes 43 minutes. If you’d known it would take only this long upfront, you could have scheduled the room for the proper amount of time (freeing it up for someone else in need of the space) and spent your time on another valuable task. By the end of the day, you would have picked up an extra hour of value-add work.
Price transparency in healthcare works the same way — it brings to the surface all of the small pricing inefficiencies that no one can see from the macro level alone. Because it sheds light on how much insurance companies are paying for individual services and how that compares to other payers, it enables them to make more precise pricing decisions that are in their best interest and the best interest of their customers.
Before modern healthcare price transparency, finance, actuarial, and contracting teams often set broad targets without much choice. If a hospital said that a payer must increase total spend year-over-year by 3%, payers did that without much regard for which services that additional spend would come through. This lack of price transparency in healthcare hurt their ability to make strategic, targeted decisions about where they were paying more for which services and why.
With price transparency data, payers get a better idea of where they’re low and where they’re high relative to one another. They can be tactical about where they increase prices and where they don’t. When used well, the data can enable payers to avoid significant financial waste — sometimes to the tune of millions of dollars each year.
Modern Healthcare Price Transparency and Data Access
Reaping these benefits isn’t easy, however. Comparing price transparency data across different hospitals and services is challenging, starting with the basic work of parsing disparate data formats into standardized data fields.
Add the fact that, until recently, only large healthcare payers have had access to this kind of data, and you get a fuller picture of the pricing landscape. The number of contracts massive payers have, and insights from their owned-provider business, has made it possible for them to acquire and use this kind of data. But the wider industry doesn’t have the same access. They face limitations and barriers to healthcare price transparency that bigger payers simply don’t.
In the world of healthcare, some are price setters and some are price takers, and a number of factors influence who plays which role. For the most part, the public sees healthcare providers (hospitals and doctors) as mission-driven, not-for-profit organizations that just want to provide the best care for patients. In fact, most hospitals only earn a net profit of 2% to 7%. Health insurance companies, on the other hand, are often perceived as being out to find new and creative ways not to pay claims. Essentially, there is a widespread assumption that insurance companies are setting the prices they pay hospitals, that they have the upper hand. But this often isn’t true.
Smaller payers historically haven’t had the opportunity to be informed and strategic about their pricing. They don’t have the size or financial resources of huge payers, such as UnitedHealthcare, which means that they can’t afford to acquire and analyze the data that would help them make more informed pricing decisions and that they don’t have the leverage to negotiate with hospitals. Their existence depends on keeping as many hospitals as possible in their network, and they consequently have to meet those hospitals’ pricing demands.
But as smaller payers gain access to price transparency data, they gain the power to improve their position. They can be strategic about where they build relationships and with which hospitals, target price savings in exchange for volume in specific specialties, and improve their overall spending.
Interpreting Price Transparency Data
Once companies have access to data, the next step is interpreting it and understanding the “who,” “how,” and “what.” Knowing what services are being provided, who is providing them (which doctors and hospitals), and how they’re being paid for is key.
“Who” is pretty standardized — all doctors, departments, and hospitals have an NPI (national provider identifier). “How” it gets paid for, on the other hand, isn’t. There is no national registry of healthcare plans, so comparing data from one hospital to the next is tricky. Government-funded plans and individual plans, for example, are hugely different, and data relevant to each much be analyzed as such. “What” is being paid for is even more complicated. Tens of thousands of code sets and modifiers for procedures, supplies, and drugs exist — and how they’re used can differ from place to place.
In spite of this, high-quality price transparency data collection and interpretation is possible. Here are six ways to make it happen:
1. Identify which price sets are most important.
Identifying which hospitals’ pricing data to analyze is the foundation of interpreting and using price transparency data. Often, a payer seeking and trying to interpret data will have a hypothesis they’re trying to test or an assumption they’re trying to verify, so the first step should be understanding the question.
2. Search and collect the raw data for all hospitals relevant to the analysis.
When researching a specific region or type of hospital, you should find all of them. It’s important to collect information about the whole sample and then use that to inform more specific, targeted questions. That said, be sure to understand whether the data from each of these hospitals is suitable for use.
3. Standardize the data.
Next comes examining the data files, understanding the technical layout and code sets being used, identifying the limitations of what is being published, and then building the process to standardize that data in a universal format. This can be done from some file formats fairly easily. Other “machine-readable formats,” such as JSON and XML, pose more of a challenge. Programmers can write code to parse those, but someone then has to restructure that data into a standard format using common code sets, so it’s important to be mindful of the required healthcare domain expertise throughout the process.
4. Figure out which questions to ask.
For data interpretation and analysis to be useful, it’s important to ask several questions. First, ask whether to compare your prices for a specific line of service at a specific hospital. You will also want to find out which hospitals have the best price positions and understand inpatient and outpatient costs.
5. Try data reporting and visualization.
One of the best ways to look at data is by building a comparison between yourself and the average of other payers. Look at the distribution of prices across various payers, too. Seeing data visually is a great way to understand the environment as a whole and where your needs or targets fit in relation to that. Effective data visualization isn’t always easy — pie charts are not going to cut it in this case.
6. Review data quality.
Unfortunately, no one else can quality-assure contracts except you. Your contracts are unique to your operation, so you have to be sure of the quality of the data you’re using. If you find issues, report them back to the provider immediately. When a provider reports misinformation, it can damage your reputation and put your client renewals at risk.
Ultimately, if you’ve ever heard the saying “you don’t know what you don’t know,” then you understand a little bit about modern healthcare price transparency.Price transparency in healthcarelevels the playing field for companies of all sizes. Once you can access data and analyze it, it becomes possible to more thoughtfully and strategically make pricing decisions. And that has the potential to create a dynamic, level playing field in healthcare that hasn’t existed before.
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.