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Predicting and explaining absenteeism risk in hospital patients before and during COVID-19

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  • Borges, Ana
  • Carvalho, Mariana
  • Maia, Miguel
  • Guimarães, Miguel
  • Carneiro, Davide

Abstract

In order to address one of the most challenging problems in hospital management – patients’ absenteeism without prior notice – this study analyses the risk factors associated with this event. To this end, through real data from a hospital located in the North of Portugal, a prediction model previously validated in the literature is used to infer absenteeism risk factors, and an explainable model is proposed, based on a modified CART algorithm. The latter intends to generate a human-interpretable explanation for patient absenteeism, and its implementation is described in detail. Furthermore, given the significant impact, the COVID-19 pandemic had on hospital management, a comparison between patients’ profiles upon absenteeism before and during the COVID-19 pandemic situation is performed. Results obtained differ between hospital specialities and time periods meaning that patient profiles on absenteeism change during pandemic periods and within specialities.

Suggested Citation

  • Borges, Ana & Carvalho, Mariana & Maia, Miguel & Guimarães, Miguel & Carneiro, Davide, 2023. "Predicting and explaining absenteeism risk in hospital patients before and during COVID-19," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    • Handle: RePEc:eee:soceps:v:87:y:2023:i:pb:s0038012123000496
    DOI: 10.1016/j.seps.2023.101549
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    References listed on IDEAS

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    1. Jeon, Jeonghwan & Suvitha, Krishnan & Arshad, Noreen Izza & Kalaiselvan, Samayan & Narayanamoorthy, Samayan & Ferrara, Massimiliano & Ahmadian, Ali, 2023. "A probabilistic hesitant fuzzy MCDM approach to evaluate India’s intervention strategies against the COVID-19 pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).

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