Trend Topic: How the ACA is Changing Health Care Analytics

By Syed Mehmud, ASA, FCA, MAAA

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 health care, is far more complex. Every two days, we create more data than was created from the dawn of civilization through the year 2000[1].

Our ability to create and record information far surpasses our ability to analyze it, for now anyway. If the Affordable Care Act (ACA) was passed a decade ago, it would take years for health care actuaries to be able to get a pulse on the program. As we navigate the third year of the program, data from the first two years is already available and being voraciously consumed by health care actuaries.

A significant portion of my recent work has involved studying ACA data, particularly deconstructing the wavelengths of a health plan’s performance using the prism of risk adjustment. Stick with me and I’ll explain my (rather forced!) Newtonian analogy.

Risk adjustment used to be a niche on the spectrum of a health

Syed Mehmud, ASA, FCA, MAAA

care actuary’s work. Risk adjustment is typically the process of adjusting a health plan’s revenue based on a measure of morbidity of the average member enrolling with the plan. The process 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 ACA risk adjustment program is now a permanent fixture in commercial individual and small group markets. 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. Its importance can be gauged by the scale of risk adjustment transfers, which as a percentage of premiums easily exceeds the typical profit margins for health plans. The program itself is in its infancy and changing as fast as infants do (e.g., recalibration, high-cost enrollee pooling, incorporation of pharmacy data). We are helping clients model and understand the impact this evolution has on their business.

What we have learned is 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.

At Wakely Consulting Group, my team developed a new data analytics model to parse and understand the drivers of financial performance in the ACA market. Our model revealed that sicker/costlier members are typically overcompensated for the risk that is assumed. It also found that richer metallic tier plans (e.g., platinum, gold) struggle to perform, and variables such as network size and income play an important role in relative profitability. By deconstructing the impact of isolated variables, and reconstituting this impact in the overall financial statement, an organization can measure and manage its risk.

Our model demonstrated that in addition to a top-down view of financials, we need to understand how a health plan’s experience is evolving at a member-level to provide valuable insight to insurers navigating the financial complexities of the ACA. In this manner, actuaries can help identify segments of the business that are underperforming, or craft a strategy to survive and even thrive in rocky markets.

I am often asked whether investing in predictive modeling and analytics is the way to “win” today. I look at it differently. You may not necessarily “win” by being up to par on advanced analytics. But you will definitely lose if you are not. Organizations that bridge the gap between data generation and data analysis will outperform their competitors. Health actuaries have a tremendous opportunity to play a pivotal role in this transformation.

[1] A variation of a quote credited to Eric Schmidt: https://techcrunch.com/2010/08/04/schmidt-data/

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