Sürdürülebilir Kalkınma Amaçları

• 2

Yazarlar

• Gul, Muhammet(2)
• Yücesan, Melih(2)
• Alfaro-Saíz, Juan-Jose(1)
• Alfaro-Saíz, Juan-José(1)
• Arias-Fonseca, Sebastián(1)
• Ariza-Ortíz, Andrés(1)
• Barbati, Maria(1)
• Celani de Souza, Helder Jose(1)
• Ishizaka, Alessio(1)
• Khan, Jehangir(1)
• Navarro-Jiménez, Eduardo(1)
• Ortiz-Barrios, Miguel(1)
• Ortíz-Barrios, Miguel(1)
• Petrillo, Antonella(1)
• Pérez-Aguilar, Armando(1)

• MakaleYalnız künye2026Scopus SJRQ1/r/munzur/1234
  Enhancing 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.

  • 
  İncele
• MakaleAçık Erişim2024Scopus SJRQ1/r/munzur/1705
  Integrating discrete-event simulation and artificial intelligence for shortening bed waiting times in hospitalization departments during respiratory disease seasons

  Ortiz-Barrios, Miguel; Ishizaka, Alessio; Barbati, Maria; Arias-Fonseca, Sebastián; Khan, Jehangir; Gul, Muhammet; Yücesan, Melih; Alfaro-Saíz, Juan-Jose; Pérez-Aguilar, Armando

  Seasonal Respiratory Diseases (SRDs) usually produce a heightened number of Emergency Department (ED) attendances due to their rapid dissemination within the community and the ineffective prevention measures. Such a context requires effective management of the emergency care processes to provide in-time diagnosis and treatment to infected patients. Nonetheless, EDs have evidenced severe operational deficiencies during these periods, thereby provoking extended bed waiting times in Hospitalization Departments (HDs). Therefore, this paper presents a hybrid approach merging Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to shorten the bed waiting times in HDs considering patient records collated in the first emergency care stages. First, we implemented Random Forest (RF) to estimate the probability of respiratory worsening based on sociodemographic and clinical patient data. Second, we inserted these probabilities into a DES model mimicking the emergency care from the admission to the HD. We then pretested different HD configurations and strategies seeking to reduce the HD bed waiting time. A case study of a European hospital group was used to validate the suggested framework. The AI-DES model enabled decision-makers to identify an improvement proposal with hospitalization bed waiting time lessening, oscillating between 7.93 and 7.98 h.

  • 
  AtıfScopus16·OpenAlex17

  İncele