Description: Acknowledgments, Copyright, and Title Page. From Group Medical Insurance Large Claims Database Collection and Analysis: Acknowledgments, M-HB97-1.

Description: Project Oversight Group. From Group Medical Insurance Large Claims Database Collection and Analysis, M-HB97-1.

Description: Almost half of recent FDA approvals have been for orphan drugs. This trend is expected to continue in 2023. Orphan drugs are priced more than 30 times higher than non-orphan drugs, therefore their cost burden in aggregate has the potential to overwhelm the budgets of payers, especially that of smaller health plans. Hear how the cost impact analysis for smaller health plans require different considerations than for a larger health plan when modeling the impact of rare diseases.

Description: Chapter 1 presents the background to the current study, followed by the methodology used, and a summary of the findings of the study. From Group Medical Insurance Large Claims Database Collection and Analysis, M-HB97-1.

Description: The purpose of this SOA-sponsored research was to assemble and analyze a limited large claims database which may serve as a model for expanded inter-company studies of health care benefits in the future. From Group Medical Insurance Large Claims Database Collection and Analysis, M-HB97-1.

Description: The famous 80/20 rule in data science states that only 20% of your time working with data is spent driving insightful business analytics. The rest of your time is spent trying to query, clean and organize your data. Even with this knowledge, the majority of actuarial training/focus is on the 20 percent. This session takes a different approach, focusing solely on speeding up your work on the other 80 percent. Practical examples will be part of a code walkthrough in R.

Description: Data science, predictive analytics and AI are certainly the buzzwords these days. They are heavily used and developed in underwriting and marketing but for most actuaries, they might/may as well be science fiction. The presenters will demonstrate how a normal actuary can utilize data science techniques in their daily job function without forming an official data science team within a company. It can be done in a gradual fashion so that normal business is not disrupted and improvements are made over time. The usage of data science techniques will include demonstrations of the following areas: - Regression analysis - Economic scenario generator - Experience study - Data quality checks - Inforce compression for explicit hedging

Description: Data is at the core of the actuaries' role within companies, but how can actuaries translate data insights into informed and actionable decision making? The key is communicating data insights into a language that transcends analysis and enables real-time decisions that can provide an advantage to your management team. Learn an approach to choosing a tool or medium for your data insights, questions to ask yourself to better understand the communication necessary, and perspectives on how actuaries can take a larger role in actionable decisions within the industry. Understand availability, quality, and practical application of data sources health actuaries can access easily, how that data looks, approaches to transforming data into decisions, how it enriches administrative and claims data, and applications to pricing and reserving. Finally, discuss implications of ASOP 23, ASOP 41, and ASOP 56 as it relates to this topic. By attending this session, you will be able to: - Create an action plan on how to transform data into messaging for your leaders. - Use real experiences to formulate a strategy to expand the actuaries' role in informing decisions within the industry, including examples, to illustrate how data can benefit financials of health coverages. - Understand the application of ASOP 23 (Data), ASOP 41 (Communication), and ASOP 56 (Modeling) as it relates to turning data into decisions. - Be knowledgeable about new sources of morbidity for medical care and LTC and how to acquire and apply this information in actuarial models.

Description: Four appendices are attached to the monograph: a letter requesting information, two letters requesting participation, and a set of general database specifications. From Group Medical Insurance Large Claims Database Collections and Analysis, M-HB97-1.

Description: This is a list of members on the Mortality and Morbidity Experience Studies Committees.