Description: Heatmaps are an effective tool for visualizing changes in cancer incidences over time in large datasets. They can show the impact of screening measures, particularly when targeting specific age groups, and reveal characteristics of individual cohorts.

Description: Synopsis of Issue Brief prepared by Tax Work Group of the Academy-LTD Claim Reserve Assumption Basis: Use of date of incurral/date of issue of original policy.

Description: Discussion of issues from The Credibility Task Force for Health Coverage. From Health Section News, June 1998, Issue No. 35.

Description: Considerations in the Development of Area Factors, Issue No. 38.

Description: ACA risk adjustment has now been in play for years. The original intent of risk adjustment was to help stabilize the market through measuring risk levels of each issuer. However, the risk adjustment program may have introduced additional uncertainty due to annual market shifts and model changes, making risk adjustment estimation is a difficult task. This article analyzes the results over time, including impacts from market changes and model changes.

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: In the evolving realm of healthcare actuarial practice, value-based contracts are reshaping the landscape. This article explores the pivotal role of AI-enhanced Electronic Medical Records (EMRs) in empowering actuaries to navigate this dynamic sector. Despite challenges in data standardization, AI-enhanced EMRs offer a solution by extracting and standardizing diverse medical records. The article delineates how AI-EMR integration benefits actuaries and provider organizations, enabling more accurate forecasting, earlier shared savings prepayments, and real-time quality metric aggregation. Moreover, it discusses how this fusion addresses historical data limitations, can help project catastrophic claims, and can help identify risk score capture opportunities. Ultimately, it indicates the transformative potential of AI-EMR synergy, positioning actuaries at the forefront of healthcare analytics with unprecedented precision and insight.

Description: This paper is about an Interacting Particle System [IPS] approach to modeling treatment costs associated with a multiple stage pandemic. The model population receives differing treatments with possibly differing costs depending on the stage of exposure/infection. The total cost of treatment is a time varying utility function of the ensemble population for which this approach uses particle system methods to simulate the cost of maintaining survival of the population. The paper gives a brief background on particle systems with specific focus on 'the contact process', followed by expanding the contact process to represent more realistic state transitions.

Description: Healthcare costs have been rising faster than general inflation for many years. Early models that predicted this past pattern projected healthcare expenditure growth that continued to outpace total expenditure growth leading to the fallacious position that the whole of the US economy would ultimately be consumed by healthcare. The Society of Actuaries' Pension Section and Health Section Research teams commissioned Professor Thomas E. Getzen of Temple University to construct a resource model for the projection of long term healthcare cost trends that includes capacity constraints to ensure the long-term projection is rational. The motivation for creation of the model was driven by its potential usage as a resource for the estimation of reportable liabilities for retiree healthcare benefits as specified under FAS 106 and GASB 45 accounting statements. This is a zip file containing the following: 1. A background paper on the creation of the health trend model, 2. The model, programmed in Microsoft Excel [xls format], that projects per-person expenditures and growth rates through 2099 using baseline assumptions, but also permits the flexibility for user–inputted alternative assumptions, and 3. Technical documentation providing transparency and support for the ultimate results.

Description: This report explores AI bias in insurance and health data, highlighting the ethical role of actuaries and offering cross-disciplinary guidance for fairer decision-making systems.