Description: Learn about key actuarial and analytic tools and methodologies used by commercial health insurance to identify and quantify drivers of healthcare cost and utilization (including wasteful and low-value ones) and risks. Use case scenarios in which these tools and methodologies apply, such as underwriting, reinsurance, medical management for population health and chronic diseases, managed care contracting, insurance market reform, provider payment reform, etc., will also be covered. Experience from the U.S., China and other parts of the world will be shared.

Description: This paper provides a description of a method that one can use to develop an estimate of accident year incurred losses if the only available data is aggregate paid losses by calendar year for a number of calendar years. An exhibit demonstrating the use of this method follows this description. From Actuarial Research Clearing House 1993, VOL. 3.

Description: 1994 SOA Meeting, San Antonio. This session was about provider network building in nonmetropolitan areas. Question addressed: 1. How can an effective delivery system be built to serve rural areas? 2. What influence will health care reform have on such systems? 3. Does a single-payer system make sense for such populations? 4. How does health care reform impact nonmetropolitan markets?

Description: This session provides actuaries who work with group life and disability products with the opportunity to participate in a moderated discussion with their colleagues. The specific topics will be those of interest to attendees, but are likely to include recent experience trends, product development, pricing and reserving.

Description: This paper provides a method for estimating long-tailed unpaid losses from paid loss development using trended generalized bondy development. From Actuarial Research Clearing House 1993, VOL. 3.

Description: This session will explore the national landscape of state Medicaid risk adjustment program design & application and the key differences that drive effective managed care organization program management. Risk adjustment is a mechanism to adjust capitation rates between Managed Care Organizations to account for population morbidity differences. Although many states utilize this mechanism, the application of risk adjustment can vary significantly across states. The primary differences include populations, risk score models and data collection. These state application nuances affect the strategies and tactics utilized by MCOs for effective risk adjustment program management including provider financial alignment, encounter data submission and the capture of accurate diagnosis codes. The session is intended for Medicaid Managed Care Actuaries working to develop or support comprehensive risk adjustment program management functions.

Description: Analysis of Alzheimer’s Disease as a potential Critical Illness insurance benefit trigger.

Description: 1994 SOA Meeting, San Antonio. This session on physician reimbursements focused on arrangements that might be offered by managed care companies, such as HMOs. It included an overview of key definitions, concepts, and incentive compensation arrangements.

Description: This model arose from a project to evaluate the cost of a health care access' program to provide comprehensive medical care to uninsureds in Minnesota, for the Minnesota Health Care Access Commission. The project was to provide estimates of the impact on per-person and aggregate costs for various participation percentages in the access program. In order to evaluate those costs, the antiselection model described in this was developed. Of note, this was the ACTUARIAL EDUCATION AND RESEARCH FUND 1991 PRACTITIONERS AWARD WINNER. From Actuarial Research Clearing House 1991, VOL. 2.

Description: This chapter focuses on creating practical models that aid in the prediction of the risk of readmission to a specific hospital within 30 days of discharge for Medicare patients, to aid in the identification and stratification of at-risk patients for hospitals considering building an intervention program in a cost-effective manner. For readers who wish to develop their own predictive model, a dataset is provided on the Actexmadriver.com website, and R code is provided in the appendix to this chapter.