It seems like the more I look at websites in the healthcare IT space, the more confused I get. I don't know how many times I have read a website and come away thinking "Okay. Sounds impressive, but what do you actually do?" In fact, I have gotten away from using the generic term “analytics”, because it can mean almost anything and therefore means nothing. I like the term "rules engine", but even that can be confusing at times. I am sure many people think that if you've seen one, you've seen them all. However, healthcare rules and the accuracy and transparancy of those rules, are critically important, so if you are not questioning the rules engine of your population health analytics vendor, you should. Here's why.Simply put, try to think of a rules engine as a mailroom or assembly line for your data. When data comes to the mailroom, it goes through the "rules engine" and gets sorted by city, state, zip, etc. An assembly line combines raw materials and component parts to make an end product. A healthcare rules engine works in much the same way - taking raw healthcare data, such as medical claims, pharmacy claims, eligibility data or EMR data, and applies rules to turn that data into meaningful components that can then be further analyzed in order to create actionable steps to improve performance of a healthplan or a health system.
Let's take office visits as a very simple example. Let's say your goal is to create a transparent pricing program for a plan so that you can advise plan members about the relative costs of various providers. You might think office visits are straight-forward. However, some plans will reimburse an after-hours code in addition to the code for the office visit, so two charges, but one visit. Does your healthcare rules engine know to divide by one and not two in that situation? If not, you may identify a provider as a low cost provider when in fact they are one of the highest. This is a very simple example, but you get the idea.
Almost all analytics products have some sort of rules engine, but with varying degrees of sophistication and accuracy. If you don't know the level of sophistication of that engine, you may be making financial and clinical decisions based on incorrect results. So when looking at population health analytics products, don't get too excited by the beautiful data visualizations. Make sure you know whether the rules engine behind that visualization is presenting accurate information.