ABSTRACT
Hospitals around the world, as health institutions with a key role in the health system, face problems while providing health services to patients with various types of diseases. Currently, those problems are intensified due to the pandemic caused by SARS-CoV-2 virus. This pandemic has caused an extreme spread of the disease with constantly changing needs of patients which impacts the capacities and overall functioning of hospitals. In order to meet the challenge of the COVID-19 (COronaVIrus Disease- 2019) pandemic, health systems must adjust to new circumstances and establish separate hospitals exclusive for patients infected with SARS-CoV-2 virus. In the process of creating COVID-19 hospitals, health systems face a shortage of medical professionals trained for work in COVID-19 hospitals. Using this as a starting point, this study puts forward a two-phase model for the evaluation and selection of nurses for COVID-19 hospitals. Each phase of the model features a separate multiple-criteria model. In the first phase, a multiple-criteria model with a dominant criterion is formed and candidates who meet the defined requirements are evaluated. In the second phase, a modified multiple-criteria model is formed and used to evaluate medical professionals who do not meet the requirements of the dominant criterion. By applying this model, two groups of medical professionals are defined: 1) medical professionals who completely meet the requirements for working in COVID-19 hospitals and 2) medical professionals who require additional training. The criteria for evaluation of medical professionals in this multiple-criteria model are defined based on research conducted on medical professionals assigned to the COVID-19 Crisis Response Team during the COVID-19 pandemic in the Republic of Serbia. The model was tested on a real example of evaluating medical professionals assigned to the COVID-19 hospital in Sombor. The model for evaluating medical professionals presented in this paper can help decision makers in hospitals and national policy makers to determine the readiness level of hospitals for working in the conditions of the COVID-19 pandemic, as well as underline the areas in which hospitals are not ready to meet the challenges of the pandemic. © 2020 Regional Association for Security and crisis management. All rights reserved.
ABSTRACT
Healthcare systems worldwide are facing problems in providing health care to patients in a pandemic caused by the SARS-CoV-2 virus (COVID-19). The pandemic causes an extreme disease to spread with fluctuating needs among patients, which significantly affect the capacity and overall performance of healthcare systems. In addition, its impact on the sustainability of the entire economic and social system is enormous and certain sustainable management strategies need to be selected. To meet the challenges of the COVID-19 pandemic and ensure sustainable performance, national healthcare systems must adapt to new circumstances. This paper proposes an original multi-criteria methodology for the sustainable selection of strategic guidelines for the reorganization of a healthcare system under the conditions of the COVID-19 pandemic. The selection of an appropriate strategic guideline is made on the basis of defined criteria and depending on infection capacity and pandemic spread risk. The criteria for the evaluation of strategic guidelines were defined on the basis of a survey in which the medical personnel engaged in the crisis response team during the COVID-19 pandemic in the Republic of Serbia participated. The Level-Based Weight Assessment (LBWA) model and Measuring Attractiveness by a Categorical-Based Evaluation Technique (MACBETH) method were used to determine the weight coefficient criteria, while a novel fuzzy Ranking of Alternatives through Functional Mapping of Criterion Subintervals into a Single Interval (RAFSI) model was used to evaluate the strategic guidelines. The proposed multi-criteria methodology was tested in a case study in the Republic of Serbia. The validity of the proposed methodology is shown through the simulation of changes in input parameters of Bonferroni aggregation functions and through a comparison with other multi-criteria methodologies.