Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data
One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. Healthcare claims present huge variability and heterogeneity influenced by random decisions of the courts and intrinsic characteristics of the damaged parties, which makes tradition...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Risks |
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| Online Access: | https://www.mdpi.com/2227-9091/12/2/24 |
| _version_ | 1827342793072181248 |
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| author | Claudio Mazzi Angelo Damone Andrea Vandelli Gastone Ciuti Milena Vainieri |
| author_facet | Claudio Mazzi Angelo Damone Andrea Vandelli Gastone Ciuti Milena Vainieri |
| author_sort | Claudio Mazzi |
| collection | DOAJ |
| description | One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. 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. 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. |
| first_indexed | 2024-03-07T22:14:37Z |
| format | Article |
| id | doaj.art-128d7589bdaf47fa9f807c71a09bd6a3 |
| institution | Directory Open Access Journal |
| issn | 2227-9091 |
| language | English |
| last_indexed | 2024-03-07T22:14:37Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj.art-128d7589bdaf47fa9f807c71a09bd6a32024-02-23T15:33:19ZengMDPI AGRisks2227-90912024-01-011222410.3390/risks12020024Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian DataClaudio Mazzi0Angelo Damone1Andrea Vandelli2Gastone Ciuti3Milena Vainieri4Department of Management, School of Advanced Studies Sant’Anna, 56127 Pisa, ItalyThe BioRobotics Institute, School of Advanced Studies Sant’Anna, 56127 Pisa, ItalyDepartment of Management, School of Advanced Studies Sant’Anna, 56127 Pisa, ItalyThe BioRobotics Institute, School of Advanced Studies Sant’Anna, 56127 Pisa, ItalyDepartment of Management, School of Advanced Studies Sant’Anna, 56127 Pisa, ItalyOne of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. 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. 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.https://www.mdpi.com/2227-9091/12/2/24healthcareclaims reservinggeneralized linear modelsmedical malpracticeerror estimation |
| spellingShingle | Claudio Mazzi Angelo Damone Andrea Vandelli Gastone Ciuti Milena Vainieri Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data Risks healthcare claims reserving generalized linear models medical malpractice error estimation |
| title | Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data |
| title_full | Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data |
| title_fullStr | Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data |
| title_full_unstemmed | Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data |
| title_short | Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data |
| title_sort | stochastic claims reserve in the healthcare system a methodology applied to italian data |
| topic | healthcare claims reserving generalized linear models medical malpractice error estimation |
| url | https://www.mdpi.com/2227-9091/12/2/24 |
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