An Intelligent System for Patients’ Well-Being: A Multi-Criteria Decision-Making Approach

Fabián Silva-Aravena, Jimmy H. Gutiérrez-Bahamondes (), Hugo Núñez Delafuente and Roberto M. Toledo-Molina
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Fabián Silva-Aravena: Facultad de Ciencias Sociales y Económicas, Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3480094, Chile
Jimmy H. Gutiérrez-Bahamondes: Doctorado en Sistemas de Ingeniería, Facultad de Ingeniería, Universidad de Talca, Camino Los Niches Km 1, Curico 3340000, Chile
Hugo Núñez Delafuente: Doctorado en Sistemas de Ingeniería, Facultad de Ingeniería, Universidad de Talca, Camino Los Niches Km 1, Curico 3340000, Chile
Roberto M. Toledo-Molina: Facultad de Ingeniería, Universidad de Talca, Camino Los Niches Km 1, Curico 3340000, Chile

Mathematics, 2022, vol. 10, issue 21, 1-22

Abstract: The coronavirus pandemic has intensified the strain on medical care processes, especially waiting lists for patients under medical management. In Chile, the pandemic has caused an increase of 52,000 people waiting for care. For this reason, a high-complexity hospital (HCH) in Chile devised a decision support system (DSS) based on multi-criteria decision-making (MCDM), which combines management criteria, such as critical events, with clinical variables that allow prioritizing the population of chronic patients on the waiting list. The tool includes four methodological contributions: (1) pattern recognition through the analysis of anonymous patient data that allows critical patients to be characterized; (2) a score of the critical events suffered by the patients; (3) a score based on clinical criteria; and (4) a dynamic–hybrid methodology for patient selection that links critical events with clinical criteria and with the risk levels of patients on the waiting list. The methodology allowed to (1) characterize the most critical patients and triple the evaluation of medical records; (2) save medical hours during the prioritization process; (3) reduce the risk levels of patients on the waiting list; and (4) reduce the critical events in the first month of implementation, which could have been caused by the DSS and medical decision-making. This strategy was effective (even during a pandemic period).

Keywords: agglomerative cluster; patients prioritization; vulnerability and risk; decision support system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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