Even with new price transparency laws that went into effect last year, healthcare pricing remains confusing. It is still difficult to estimate the price of a medical intervention before it takes place. After the fact, it is nearly impossible to understand the price and features of prior healthcare purchases. These limitations stem from the unusual way that healthcare is billed. The industry relies on data for analysis and decision making, but medical billing data provides incomplete information about purchases. As a result, healthcare markets have not been well managed. This has led to costs that continue to spiral out of control.
So how is healthcare billed, and why is it unusual? Healthcare is built upon aggregations of line items known as “claims”. Every healthcare service or encounter is a combination of the claims that went into it. There are two main categories used to analyze claims: diagnosis codes and procedure codes. Diagnosis codes tell why something was bought. Procedure codes show the components of what was bought.
For example, let’s look at an upper and lower GI endoscopy with biopsy. Below is a simplified example of claims from the encounter. The diagnosis codes — the why — are “diverticulosis of intestine,” “personal history of colonic polyps,” and “constipation, unspecified.” The procedure codes — the components — include a range of items from colonoscopy services (surgeon or operating room) to anesthesia and the recovery room.
Both the components of a procedure and why it was purchased can impact the total price. This is generally true of any product in any marketplace. The cost of the components helps determine the cost of the total product. The why helps determine the specific demand. In most other industries, elements of a product are combined into a standard unit price — a SKU — for the final version of the product that is actually sold. The price of the SKU is the one price the end-customer sees, because it is the one price that they can act on. (In other words, choose to buy or not.)
The Automobile Analogy
The best way to illustrate the issues of relying on procedure codes and diagnosis codes for analysis is via analogy. As an example, imagine we were buying a truck for our business and received claims data on the sale. The diagnosis code — the why — could be construction, landscaping, or transportation.
The procedure codes — the components — could be engine, steel, and aluminum.
The diagnosis and procedure codes do provide information,but they don't provide any detail of what truck we bought. We still don’t know the make, model, mileage, and price. Analyzing spend on an engine is not very useful if we don’t have information on what truck the engine belonged to. When we make a purchasing decision on a truck, we take actions at the truck level, not the component level. That is what we have control over — the engine has already been integrated into the truck.
This system of non-contextualized information may not be a huge problem if we were trying to analyze a single purchase. If we made the purchase ourselves, we may remember the specifics of the truck we bought. Even if someone else made the purchase, we could track down that information. Things get very complicated, however, if we are dealing with a whole fleet of vehicles purchased for the business. If all we had were diagnosis and procedure codes not mapped to the relevant truck details, we simply would not be able to achieve the goal of our analysis. We want to provide a clear picture of the entire fleet of vehicles purchased. Our goal is to identify the mix of trucks that will deliver the most value per dollar.
Back to Healthcare
Luckily, automobile shopping doesn’t work like this. When you’re in the market for a truck, you get a single price for the truck as a whole. Everything that led to that price already “baked in.” You can compare the price for one truck to the price of other trucks regardless of whether or not the trucks are similar, made by the same manufacturer, or sold at the same dealership. For our purposes, value analysis is also straightforward, whether on the scale of a single vehicle or an entire fleet.
Automobiles are just one example, but almost every industry works that way, from retail to wholesale. Why is healthcare the exception? When we billed for medical care, we see the reason(s) for the procedure and what components went into the procedure. Yet, those components are not mapped together and viewed as part of a comprehensive unit.
The result of the lack of cohesive information is an opaque pricing and procurement system. It makes comparing relative value, for the same thing, nearly impossible. Due to highly-variable billing patterns, analyzing purchases on diagnosis and procedure codes fails. This failure compounds when analyzing a full suite of services as part of a health plan.
The state of the industry is a problem because healthcare is vitally important. In healthcare, we expect informed decisions from health plans, providers, vendors, and employers. Healthcare is also extremely expensive in this country. Healthcare purchasers are not given a clear information on what they are buying. As a result, they spend large amounts of money without improving outcomes.
Our Goal at Careignition
At Careignition, we aim to create standardized units for healthcare. Just as you can see one all-inclusive price for a truck, you should be able to see one price for an entire medical intervention or service. An "Upper and Lower GI endoscopy" should be standardized. It should include inputs like the surgeon, operating room, anesthesia, and pathology fees.
We derive coherent units by using AI to map those codes together and tease out relevant features. This process allows features to be aggregated and presented with context. Below is an example of a standardized upper and lower GI endoscopy. The processed data is now in a format that people can understand and make decisions about.
While diagnosis and procedure codes can be used as inputs, we should not rely upon them to buy and analyze healthcare. With standard units, we can make healthcare purchasing decisions like we make business decisions. We can find ways to maximize savings while maintaining quality. Just as a business may pick a preferred partner to achieve truck-buying efficiency with scale, they can make similar decisions with their healthcare spend and selecting what providers to use.
With Careignition, you can see what you bought in terms you can understand. You can then use that information to buy healthcare better.
Learn how Careignition can illuminate your health data.