Integrated fuzzy decision-making methodology with intuitionistic fuzzy numbers: An application for disaster preparedness in clinical laboratories

Society is on constant alert due to the increasing frequency and severity of Seasonal Respiratory Diseases (SRDs), posing significant challenges from both a humanitarian and public health perspective. The recent COVID-19 pandemic has tested the capacity of clinical laboratories to address seasonal infections, epidemic outbreaks, and critical emergencies. This scenario has led to operational burdens, primarily from resource limitations, a lack of proactive planning, and the low adaptation to unforeseen circumstances. Coupling different data-driven approaches considering multi-criteria weighting, interdependence assessment, and outranking are critical for devising effective interventions upgrading the operability of clinical labs during SRDs. Nonetheless, a deep literature review revealed there are no studies using these hybridized approaches when addressing this problem. Consequently, this article proposes the application of an innovative hybrid Multicriteria Decision-Making (MCDM) methodology that integrates the Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP), Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL), and Combined Compromise Solution (CoCoSo) to assess the disaster preparedness of clinical laboratories during SRDs. Initially, we applied IF-AHP to assign the relative weights to criteria and sub-criteria, considering the inherent hesitation and uncertainty in decision-making. Subsequently, IF-DEMATEL was utilized to analyze the interrelationships between criteria, providing insights into the interrelations among clinical lab disaster management drivers. Finally, the CoCoSo method was applied to estimate each lab’s Preparedness Index (PI) and detect response gaps when coping with SRDs. The suggested methodology was validated across nine clinical laboratories in Colombia during the most recent respiratory pandemic. This study contributes to the healthcare sector authorities by identifying key criteria and sub-criteria affecting the response of clinical labs, the elicitation of main response drivers in clinical labs when facing SRDs, and the calculation of a multidimensional indicator representing the preparedness of the labs. This work also enriches the literature by applying the IF-AHP, IF-DEMATEL, and CoCoSo approach to a challenging case study requiring a multi-method data-driven application. Furthermore, it suggests future directions to improve the proposed framework in other related contexts.