Proposal for a Fuzzy Model to Assess Cost Overrun in Healthcare Due to Delays in Treatment

Apart from the effects of treating those infected with COVID-19, the pandemic has also affected treatment for other diseases, which has been either interrupted or canceled. The aim of this paper is to provide a financial model for obtaining the cost overrun resulting from the worsening of illnesses and deaths for each of the causes considered. To achieve this, first deaths have been classified into causes of death and for each of these causes, an estimation has been made of the worsening condition of patients due to delay in treatment. Through these data, a fuzzy relation between deaths and the worsening condition of patients can be obtained. Next, the expertise process has been used to estimate cost overrun in relation to patients' pathologies. The experts' opinions have been aggregated using ordered weighted average (OWA). Lastly, using fuzzy logic again, a correction coefficient has been determined, which optimizes the future implementation of the proposed model without the need for a new estimation of inputs. The paper concludes with a numerical example for a better comprehension of the proposed theoretical model. Ultimately, it provides the scientific community in general and in particular managers of public administration entities with a novel tool for improving the efficiency of the healthcare system.

Automated summary

This paper examines the impact of the COVID-19 pandemic on the treatment of other diseases and provides a financial model to analyze the cost overrun resulting from worsening illnesses and deaths. By classifying deaths by causes and estimating the worsening condition of patients, a fuzzy relation is established, and experts' opinions are used to estimate cost overrun. A correction coefficient is determined through fuzzy logic to optimize the implementation of the proposed model. Ultimately, the paper presents a new tool for improving healthcare system efficiency to the scientific community and public administration entities.