Did you know that nearly 60 million Americans are covered by an insurance plan that requires risk adjustment? Why is risk adjustment important and how can it drive healthcare to a quality level we expect from consumer products like our smartphones?
It’s important first to understand what exactly risk adjustment is. Risk adjustment is used to define the health and wellbeing of an specific individual, and then set how much a particular insurance company or health care provider will get paid for providing care for them. These payments are based on how sick or healthy their particular membership is, ensuring sick patients get the care they need and providers get reimbursed for providing that care.
The Affordable Care Act (ACA) ushered in new regulation, one that many other countries take as table-stakes: payers and providers cannot deny care or coverage based on a patient’s pre-existing conditions. Before you think this isn’t a big problem to begin with, remember that 34% of Americans are considered obese and that the prevalence of diabetes is growing at an alarming rate. In 2014, the Centers for Disease Control reported that 9% of the population had diabetes, or 29 million Americans. The level of wellness amongst the population is not evenly distributed amongst insurance or provider groups that care for them—putting significant strain on resources.
What is Risk Adjustment?
To illustrate the concept of risk adjustment, imagine that Insurance Company A, let’s call them West Coast Insurance, has a relatively healthy membership, with few smokers, low obesity, and diabetes levels and who don’t require medical treatment as often over the course of a year. Insurance Company B, let’s call them North Coast Insurance, has a membership with relatively high levels of smoking, obesity and diabetes and who seek medical treatment three times as much over the course of a given year. Simply put, North Coast’s patients are much sicker than West Coast’s.
Medicare Advantage and Commercial Risk plans distribute payments on a per-patient basis not on a fee-for-service basis. In other words, payment is based on a set amount for each patient, not for each service delivered. In this scenario, North Coast’s cost of providing care would be much higher because of their relatively sicker membership—putting severe strains on their resources and providing a disincentive to insure and care for sick patients.
What the risk adjustment process does is measure the health of each individual over the course of the year by tracking a series of conditions or disease states. At the end of the year, each insurance company and provider with members enrolled in Commercial Risk or Medicare Advantage plans submit the disease codes of their membership, which then get added up to create a risk adjustment score—an overall measure of the health and wellness of their membership. CMS then adjusts payments according to how sick or well their membership is in relation to all the others.
In our example, North Coast Insurance would get a higher reimbursement rate from CMS to fund the care their sicker patient population may need. Phased in over the coming years, the ACA will be including additional criteria to incentivize these payers and providers to improve the overall risk score of their members over time.
Apixio’s technology eases the risk adjustment process
For risk adjustment to function optimally and help ensure efficient and effective allocation of healthcare resources, it requires just one thing: accurate and long-term risk assessment. Sounds simple, right? Well, technically it is. The problem is that assessing a patient’s overall wellness is largely a manual task. Coders go through hundreds of pages of documents to identify disease categories on a per-patient basis, for millions of patients, every year.
Powered by a cognitive computing platform that has been built on the analysis of more than 125 million patient documents, Apixio’s HCC Profiler breaks down the risk adjustment processes into simple and straightforward coder and quality assurance workflows, improving productivity while reducing errors and closing gaps for improved accuracy and care.