Description: The COVID-19 pandemic has had an impact on all aspects of daily life and its impact extends to how disability incidence may play out in the future. Product profitability will be shaped by how morbidity patterns, interest rate levels and other key drivers will play out into the future. In this session, our panel will discuss COVID’s impact on IDI morbidity experience as observed over the past 18 months, offer comparisons and insights in light of other insurance lines of business, and share emerging information on the on-going effects of Long COVID. Highlights will also include how regional differences and implications for insurers as the industry moves further into the COVID vaccine era.

Description: As actuaries in health care, we spend most of our time focused on reviewing claims, enrollment and clinical data. That information is captured while patients are in health care facilities. Yet most people spend only a small fraction of their time in these facilities. This article explores what types of data are available to actuaries the provide a more holistic view of the member so that a members physical and emotional needs, the social determinants of health can be taken into consideration along with their clinical needs. Two uses cases are presented so actuaries can see how these types of analyses can be leveraged to improve clinical, operational, and financial performance of risk bearing health entities. Understanding the customer (member) not just the patient enables actuaries to provide new insights and values to our stakeholders across a wide range of business and risk applications.

Description: Analyzing large quantities of raw complex data is often a challenge in the health care industry. During this webcast, there will be a demonstration of how unsupervised machine learning techniques can be applied to such data to help bring useful insights. This will include examples that use unsupervised learning techniques spanning dimensionality reduction, clustering and anomaly detection.

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. This paper presents a long term healthcare trends resource model, prepared for the Society of Actuaries by Professor Thomas Getzen, 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.

Description: The article documents the efficacy of infra-low frequency neurofeedback regarding how it improves patient no show rates, decreases mental health symptoms by 40% or more, and self-harm and suicidal ideations.

Description: The momentum in value-based care has produced an opportunity for actuaries to become pioneers in health care

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: 2021 ImpACT Conference - Session 11D: Health Care Technologies in the New Normal 10.28.21 Recording

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: 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.