Healthcare — with its ever-increasing costs, lack of value, and consistent confusion — behaves unlike any other area of our economy, because it is not managed like any other area of our economy. This economic disorder is in part due to the unique lack of clear information that stakeholders possess to make and manage decisions.
In healthcare, the standard units of information that purchasers use are claims — bills that result from parties involved in the healthcare process. This data is meant to facilitate payment between parties, and it does that relatively effectively. Claims data does not, however, lend itself to clear analysis or decision-making, because it does not reflect the reality of healthcare purchases.
Every medical encounter is billed as one or numerous claims; each claim breaks down services into component parts (i.e. procedure codes). For a specific type of clinical encounter (i.e. a diagnostic visit with an MRI), the component parts that are included in the claims, and the structure of a claim itself, can be completely different between providers and encounters. The exact type and shape of those differences will depend on place of service, practice patterns, corporate structure of the provider, and the contract between the payer and provider. This variation creates an industry-wide disconnect between what units are purchased and what items are recorded on the billed claims. In most other industries, that disconnect is avoided by utilizing Product IDs or SKUs (stock keeping units) as standard units of purchase — instead of being standardized, healthcare’s units of purchase are fragmented, inconsistent, and opaque.
How Groceries are Billed
To show just how unwieldy healthcare purchasing has become, it is helpful to compare it to a simple form of purchasing that we perform in our everyday lives: grocery shopping. Imagine you bought some groceries last week, and you want to look at your grocery bill and see if it might actually be cheaper to go to a different store down the street. When you find the bill, it looks like this:
Unless you have a degree in chemistry, this is probably not very helpful. Yes, it is an accurate list of all the ingredients you bought, but it does not include clear information on the relationship of those ingredients to one another (i.e.how they combine to form the items we want to buy). The data in this list does not show what we bought or give any information on how we could buy it better. Imagine trying to use that data to decide where to buy your favorite loaf of bread!
Luckily, grocery stores tend to bill in a far simpler manner. Here is what you bought, expressed in a way that you can understand:
This list clearly and succinctly describes what you bought at the store. It has mapped the ingredients together and aggregated them into the end-product item we purchased. Here, a loaf of bread (as well as a bottle of water, and a container of bleach) is an SKU because it is sold as an individual unit. The costs of the bread’s ingredients, as well as logistical costs like packaging and shipping, certainly play into the price of the loaf at the store — but that final SKU price is the only one the consumer sees, because it is the only one that matters to their purchasing decision.
You can do so much more when you have a clear picture of what you’ve bought. You can decide where to buy your favorite brand of bread and determine if you got a good deal for Clorox. In business terms, you can manage value.
How Healthcare is Billed
Unfortunately, healthcare does not operate like this. At a grocery store, if we buy a loaf of bread, we get billed for one item — a loaf of bread. In healthcare, if we buy a medical procedure, we get billed for a varying configuration of components that go into that procedure — the operating room, the surgeon, the anesthesia, etc.
For a specific example, below is a simplified (if you can believe that) set of several claims for a single member’s encounter: an upper and lower GI endoscopy with biopsy.
This billing sample is represented as a mess of information whose meanings vary depending on context. Numerous elements of the bill seem designed to create unnecessary confusion:
- Despite the fact that each code represents a complementary element of the same procedure — e.g., anesthesia is not delivered without a surgical service, pathology cannot be performed without a specimen — they are billed separately.
- Though there was only one colonoscopy performed, there are two line-items listed for “colonoscopy”: one represents the operating room while the other represents the surgeon’s fees.
- The diagnoses from this single-purpose encounter are not aggregated to a single category: they can be categorized as “factors influencing health status” (personal history of colonicpolyps) or “diseases of the GI tract” (diverticulosis).
While there is plenty of information on the bill, the information is not particularly useful for analysis by itself. Due to the lack of clear definition or delineation of the relationships between billing data, there is no information of “what” was bought, “who” is responsible for it, “how much” it cost, and “where” it was provided. You couldn’t analyze how to buy the above encounter better by looking at spend on “anesthesia”, just like you can’t analyze whether you got a good deal on bread by simply looking at spend on “flour substance.”
To make matters even more confusing, a different provider can bill the same core “ingredients” with an entirely different terminology or provide the same service with a different “recipe” of ingredients. For example, some doctors use anesthesia for a procedure while others may not. This variation makes comparing services across providers extremely difficult.
Why This Matters
This lack of coherent information has dire consequences, as evidenced by how much we spend on healthcare and how much more we are projected to spend. If we do not know these essential components of healthcare purchasing, we cannot find ways to buy healthcare better. Product prices and features are the central information that makes any economy or market work; without that information, we are wandering in the dark and getting unknown (and often very low) value for our money. The buyers and sellers lack meaningful accountability for their performance.
We need to figure out how to get the most value out of our healthcare dollar. Solving this problem is essential, whether you are an employer sponsoring a health plan, a patient trying to make sense out of an Explanation of Benefits, or a health plan trying to provide affordable and high-quality healthcare for a population. If we want to navigate ourselves out of our healthcare cost crisis, we must evolve beyond the current state of the data.
Healthcare Billing Data, Simplified
Our goal at Careignition is to make healthcare purchasing as easily understandable as grocery purchasing. To do this, we are creating healthcare SKUs, where a full medical intervention or service can be presented as one standard unit, rather than an aggregation of variable component units. By leveraging AI, we can map disparate components together, turning messy data into units that are understandable, comparable, and have features that we can analyze. Here’s how the same complex surgical example above looks in our framework:
The entire medical interaction — the thing that the patient or health plan actually buys — has been aggregated into a single standardized unit. The price and the other relevant details have been clearly defined and categorized, making the bill not just easier to understand, but also comparable to similar procedures, or equivalent procedures from other providers.
The more we can create standard purchasing units, the better we can determine the value of procedures — which then means we can make better decisions about where to purchase those procedures. This matters for all healthcare stakeholders, from patients to payers to governments to hospitals. Getting medical treatment will always be more complicated than getting groceries, but the way we pay for and analyze it doesn’t have to be.
Learn how Careignition can illuminate your health data.