Researcher at Department of Management Reports Research in Risk Management (Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data): Insurance – Risk Management

2024 FEB 12 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- Investigators publish new report on risk management. According to news reporting originating from Pisa, Italy, by NewsRx correspondents, research stated, “One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve.”

Our news reporters obtained a quote from the research from Department of Management: “Healthcare claims present huge variability and heterogeneity influenced by random decisions of the courts and intrinsic characteristics of the damaged parties, which makes traditional methods for estimating reserves inadequate. We propose a new methodology to estimate claim reserves in the healthcare insurance system based on generalized linear models using the Overdispersed Poisson distribution function. In this context, we developed a method to estimate the parameters of the quasi-likelihood function using a Gauss-Newton algorithm optimized through a genetic algorithm. The genetic algorithm plays a crucial role in glimpsing the position of the global minimum to ensure a correct convergence of the Gauss-Newton method, where the choice of the initial guess is fundamental. This methodology is applied as a case study to the healthcare system of the Tuscany region.”

According to the news editors, the research concluded: “The results were validated by comparing them with state-of-the-art measurement of the confidence intervals of the Overdispersed Poisson distribution parameters with better outcomes. Hence, local healthcare authorities could use the proposed and improved methodology to allocate resources dedicated to healthcare and global management.”

For more information on this research see: Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data. Risks, 2024,12(2). (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks12020024.

Our news journalists report that more information may be obtained by contacting Claudio Mazzi, Department of Management, School of Advanced Studies Sant’Anna, 56127 Pisa, Italy. Additional authors for this research include Angelo Damone, Andrea Vandelli, Gastone Ciuti, Milena Vainieri.

ORCID is an identifier for authors and includes bibliographic information. The following is ORCID information for the authors of this research: Claudio Mazzi (http://orcid.org/0009-0007-9407-9897), Angelo Damone (http://orcid.org/0000-0001-9640-5907), Andrea Vandelli (http://orcid.org/0000-0003-1198-6891), Gastone Ciuti (http://orcid.org/0000-0002-0855-7976), Milena Vainieri (http://orcid.org/0000-0002-0914-4487).

(Our reports deliver fact-based news of research and discoveries from around the world.)