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- MakaleYalnız künye2026Scopus SJRQ1Enhancing waiting time performance of clinical laboratories for pregnant patients with seasonal respiratory diseases: a simulation-based Lean Six Sigma approach
Ortíz-Barrios, Miguel; Celani de Souza, Helder Jose; Petrillo, Antonella; Gul, Muhammet; Yücesan, Melih; Navarro-Jiménez, Eduardo; Alfaro-Saíz, Juan-José; Ariza-Ortíz, Andrés
Purpose – Clinical laboratory performance is critical in managing seasonal respiratory diseases (SRDs); however, prolonged waiting times can lead to patient dissatisfaction and nonquality costs. This challenge is particularly acute for pregnant patients, who face increased health risks from diagnostic delays. This study aims to improve waiting times before sample collection in clinical laboratories serving pregnant patients with SRDs by integrating Lean Six Sigma (LSS) and Discrete-Event Simulation (DES). Design/methodology/approach – The proposed framework was validated in a public clinical laboratory using the DMAIC (Define–Measure–Analyze–Improve–Control) methodology. DES was embedded within the Analyze and Improve phases to model patient flow and assess improvement scenarios before implementation. Findings – The integrated LSS–DES approach resulted in a significant reduction in waiting times. During SRD peak periods, waiting time before sample collection for pregnant patients decreased from 1.01 to 0.10 h per patient. These results demonstrate the framework's effectiveness and practical usefulness for decision-makers seeking performance improvements in resource-constrained healthcare environments. Originality/value – This study enhances existing literature by integrating LSS and DES to address laboratory performance challenges associated with SRDs. The combined LSS-DES used for pregnant patients during outbreak periods is novel. The study offers contextual innovation for a vulnerable population, methodological advancement through embedding DES within the DMAIC cycle, and practical laboratory validation. The framework is particularly valuable for low- and middle-income countries (LMICs), where scalable solutions are essential.
- MakaleAçık Erişim2023Scopus SJRQ2A Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak
Ortíz-Barrios, Miguel; Jaramillo-Rueda, Natalia; Gul, Muhammet; Yucesan, Melih; Jiménez-Delgado, Genett; Alfaro-Saíz, Juan-José
The use of emergency departments (EDs) has increased during the COVID-19 outbreak, thereby evidencing the key role of these units in the overall response of healthcare systems to the current pandemic scenario. Nevertheless, several disruptions have emerged in the practical scenario including low throughput, overcrowding, and extended waiting times. Therefore, there is a need to develop strategies for upgrading the response of these units against the current pandemic. Given the above, this paper presents a hybrid fuzzy multicriteria decision-making model (MCDM) to evaluate the performance of EDs and create focused improvement interventions. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique is used to estimate the relative priorities of criteria and sub-criteria considering uncertainty. Then, the intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL) is employed to calculate the interdependence and feedback between criteria and sub-criteria under uncertainty, Finally, the combined compromise solution (CoCoSo) is implemented to rank the EDs and detect their weaknesses to device suitable improvement plans. The aforementioned methodology was validated in three emergency centers in Turkey. The results revealed that the most important criterion in ED performance was ER facilities (14.4%), while Procedures and protocols evidenced the highest positive D + R value (18.239) among the dispatchers and is therefore deemed as the main generator within the performance network.
AtıfScopus28OpenAlex29
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