Description: Health claims are not normally distributed. Claims Forecasting can be improved by understanding the entire distribution of plausible values of future claims, for example knowing there is an 80% chance future claims are between $340 and $375 PMPM. Confidence intervals around our estimates allow us to understand for each range around the best estimate (for example +/- 1%) what is the probability that future claims will fall in that range. Quantile Regression is a mathematical technique where you can estimate any percentile of the claims distribution you are looking for as a function of demographics, diagnosis indicators, prior cost variables, or other significant predictors. You can see which coefficients are important at different parts of the distribution. Individual and group distributions can be constructed. There are numerous applications in pricing and underwriting, stop loss, provider profiling, ACO target setting, allocation of group risk charges based on volatility, discounting strategy, and experience analysis.

Description: Through the sponsorship of the Robert Wood Johnson Foundation, The Society of Actuaries partnered with Wakely to use their health care data to study the effects of COVID-19 on overall health care claims for individuals with a COVID-19 diagnosis. The conclusion of the study is that individuals with COVID-19 claims had significant increases in health care costs leading up to their months of diagnosis, as well as in the months following their diagnosis. These effects were more pronounced for individuals who were hospitalized due to COVID-19 as well as for individuals with prior co-morbidities. Finally, the overall health care costs of these members plateaued at significantly higher levels than their pre-COVID baselines. In fact, all the main membership cohorts experienced claim levels that were at least 20% higher than their pre-COVID starting points.