Description: Renal disease is a very heterogenous medical condition whose end state is dialysis, a recurring cost intensive medical condition. Dialysis cost is one of the major medical cost drivers in USA. Insufficient and improper care management in the early stages of CKD (chronic kidney disease) leads to early incidence of end stage renal disease (ESRD) among CKD patients which not only leads to high medical costs, but also reflects poor health quality.

Description: Gross premium calculation methods may be arbitrarily separated into: a- Type 1 methods those which initially have commissions and other per cent of premium expenses in the denominator and b- Type 2 methods those which initially have commissions and other per cent of premium expenses in the numerator. The traditional Type 1 methods directly calculate the gross premium. The purpose of this paper is: 1. to present a Type 1 method of gross premium calculation which does the following: a Incorporates the cost of reinsurance into the gross premium calculation and may be shown as a level annual cost over the gross premium calculation period. b Calculates the effect on profit margins of a change in the retention limit without redoing the original calculation. c Calculates the premiums needed to break even e.g., amortize the initial investment in surplus at the end of each policy year as a by-product. d Calculates the profit or loss generated in each policy year or over any number of policy years for any gross premium without redoing the original calculation or first using a trial gross premium. e Directly solves for the gross premium that will produce any specified profit over any number of policy years without redoing the original calculation or first using a trial gross premium. f Directly solves for the premium that will produce a specified yield on surplus invested in new business by a flexible technique not first involving the use of the renewal net premium or any other preliminary gross premium. 2. to provide a mathematical definition of a financially meaningful average policy size which recognizes the cost of reinsurance. 3. to provide for mathematical definitions of common measures of profit, and the mathematical relationships between them. 4. To establish formulas expressing the effect of changes in various assumptions used in the gross premium calculation as a lev

Description: This paper develops a parametric model for health care insurance claims to fit parametric models to the data. Model selection is based on the ChJ-squared goodness-of-fit test and a comparison of the empirical limited expected value function to parametric limited expected value functions. A copula function was used to model the bivariate portion of the data. From Actuarial Research Clearing House 1993, VOL. 3.

Description: This paper develops a parametric model for health care insurance claims using currently available software to fit parametric models to the data. Model selection is based on the Chi-squared goodness-of-fit test and a comparison of the empirical limited expected value function to parametric limited expected value functions. From Actuarial Research Clearing House 1993, VOL. 3.

Description: This article discusses the reliance the banking industry placed on sophisticated mathematical models.

Description: Actuaries historically modeled new launches in pharmaceuticals by anticipating the expected change to the market basket. This became more complicated with the recent launches of gene and cell therapies as they address rare diseases. Rare conditions have low total member volume to study and some do not currently have a diagnosis code to identify them within claims data. Addressing this modeling challenge has been growing more important in the recent years with the number of therapies launching. What has the health care industry, and more specifically health care, done to enhance modeling and how should this be altered for the future? Additionally, historic modeling has sometimes incorporated a level of direct medical treatment cost offsets where studies have shown lower treatment costs are achievable. Can gene and cell financial modeling include cost offsets as well? And if so, what are suggested precautions or limitations to this effort?

Description: In this paper several probability models for the random amounts of needed long-term care are explained, and one model is used to provide an alternate premium calculation method for certain long-term care coverages. From Actuarial Research Clearing House 1990, VOL. 2.

Description: The primary objective of the paper was to develop a straightforward method for constructing an interpolation formula of finite range with desired properties stipulated in advance. This paper had in view mainly the application to polynomial interpolation as used in actuarial work. Followed by 2 discussion papers submitted

Description: Risk adjustment in government regulated health plans has been around for over two decades. Yet the strategies for promoting risk score accuracy haven't varied much during that time. Most health plans, physician groups, and vendors promote chart reviews and/or in-home assessments as the primary vehicle for collecting data not perceived to be present in the routine claims submission and adjudication stream. We know that most organizations trying to coalesce encounter data from multiple sources often fail to ensure that all relevant data is ultimately submitted to CMS or a state Medicaid agency. Crucial data, often with significant financial value, frequently gets lost in the data pipeline, somewhere between the provider's billing system and the health plan and/or its vendors. Because 837-formatted data is difficult to read, it complicates payers and vendor’s abilities to track encounter data so that every service delivered to patients is credited to the applicable risk adjustment formula. It is incumbent upon successful risk-adjusted entities to be able to track encounter data, identify processes and/or vendors which may be mangling or losing data, in order to ensure that risk scores are sufficiently accurate to be compliant with plan sponsor requirements and to ensure appropriate revenue to the health plan or provider group.

Description: This paper analyzes the U. S. Treasury monthly interest rates from 1953 to 1992, and the daily rates from 1981 to 1992 for various forms of chaotic behavior. The primary analysis was in determining the Burst Exponent by the use of Rescaled Range Analysis. This paper also discusses other possible uses of Fractional Brownian Motion in Finance Theory, Macroeconomics, and Actuarial Science. From Actuarial Research Clearing House 1994 VOL. 1.