Guest post by Syed Mehmud, associate of the Society of Actuaries (ASA), MAAA, FCIA, Wakely Consulting Group.
The Affordable Care Act (ACA) produced a wealth of data from its first two years in operation. Health actuaries voraciously consumed that data, using predictive modeling techniques to solve healthcare industry problems that have never been seen before. While we don’t yet know how the ACA may change, I know actuaries will find solutions, because we thrive in the realm of the uncertain.
Actuaries have always been in the business of data. Centuries ago the work involved scanning clerical ledgers to create the first mortality tables. Today, human activity, including healthcare, is far more complex. Every two days, we create more data than was created from the dawn of civilization through the year 2000.
A significant portion of my recent work has involved studying ACA data, particularly deconstructing a health plan’s performance using the prism of risk adjustment.
Risk adjustment used to be a niche on the spectrum of a healthcare actuary’s work. However, since the ACA risk adjustment program is now a permanent fixture – for the time being – in commercial individual and small group markets, it is the focus of many actuaries’ every day work. Risk adjustment involves adjusting a health plan’s revenue based on a measure of morbidity of the average member enrolling with the plan. It aims to mitigate incentives to select low-risk populations, and instead re-focus the basis of competition on other factors such as quality, efficiency, and benefits delivered.
The program presents a great opportunity for actuaries to apply predictive modeling concepts on large scale data to deliver actionable insights to clients and employers. From the predictive modeling work, actuaries have learned that risk adjustment renders seemingly intuitive notions of health plan performance and profitability rather meaningless. For example, sicker and costlier individuals may have threatened a health plan’s viability in the past. But that may not necessarily be the case going forward.