A Hybrid Multi-criteria Framework for Evaluating the Performance of Clinical Labs During the Covid-19 Pandemic

Clinical laboratories were affected by the recent Covid-19 pandemic, evidencing the low preparedness of some clinical labs when responding to seasonal diseases, epidemics/pandemics, and other disastrous events. However, various operational shortcomings become glaring in the labs also propelled by the virus’s ever-changing dynamics and rapid evolution. Therefore, this paper presents a novel hybrid intuitionistic Multi-criteria Decision-Making (MCDM) approach to evaluate the performance of clinical labs during the Covid-19 pandemic. First, we used Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) to estimate the relative weights of criteria and sub-criteria considering hesitancy and uncertainty properties. Second, we employed Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL) to evaluate the interrelationships among performance criteria as often found in the healthcare context. Ultimately, the Combined Compromise Solution (CoCoSo) technique was applied to estimate the Performance Index (PI) of each clinical laboratory and pinpoint the main weaknesses hindering the effective response in presence of the Covid-19 and other disastrous events. This approach was validated in 9 clinical labs located in a Colombian region. The results evidenced that Operating capacity (global weight = 0.1985) and Occupational health and safety (global weight = 0.1924) are the most important aspects for increasing the overall response of the labs against new Covid-19 waves and future outbreaks. Besides, operating capacity (D + R = 37.486) and Equipment (D + R = 38.024) were concluded to be the main performance drivers. Also some clinical labs uncovered major shortcomings that may restrict their functioning in a future contingency.

Palavras-chave

Clinical Laboratories, Combined Compromise Solution (CoCoSo), Covid-19, Disaster management, Healthcare, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP), Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL), Multi-criteria Decision-Making (MCDM), Performance evaluation

Idioma

Inglês

Citação

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 14029 LNCS, p. 104-122.