Volume 23, Issue 1 (2026)
ioh 2026, 23(1): 113-138 |
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Farahani M, Rezaei F, Behbahani Nia A, Motehari S. PRESENTING A HOSPITAL RESILIENCE MODEL IN FACING HEALTH AND ENVIRONMENTAL RISKS ARISING FROM EMERGING DISEASES USING THE FUZZY DELPHI METHOD. ioh 2026; 23 (1) :113-138
URL: http://ioh.iums.ac.ir/article-1-3815-en.html
URL: http://ioh.iums.ac.ir/article-1-3815-en.html
Islamic Azad University, Roudehen Branch , Ma.Farahani_5@iau.ac.ir
Abstract: (8 Views)
Background and Objective: Given the intensification of health and environmental risks arising from emerging diseases, enhancing the resilience of hospital systems has become a strategic imperative. This study was conducted with the aim of designing a hospital resilience model and identifying key factors affecting its promotion or weakening in the face of these risks, with a special focus on health and environmental dimensions.
Methods: This study was conducted at Shahid Dr. Lavasani Hospital using a three-phase mixed-methods approach. In the first phase, a systematic review of credible databases and documents from international organizations was performed, extracting a preliminary framework consisting of 30 indicators across four main dimensions: managerial-structural, health, environmental, and technical-infrastructural. In the second phase, a 15-member expert panel was selected through purposive sampling. The indicators were evaluated using a two-round fuzzy Delphi technique. In the first round, a questionnaire based on triangular fuzzy numbers was designed and provided to the experts. In the second round, controlled feedback was provided based on the preliminary analysis of the first round's results. Data were analyzed using the centroid method, and the final importance coefficient (A) for each indicator was calculated. Indicators with an importance coefficient higher than 0.7 (21 indicators) were selected as final indicators. Reliability was confirmed with a Kendall's W coefficient of 0.85.
Findings: The fuzzy Delphi process led to the confirmation and prioritization of 21 final indicators from the initial set. The indicators "Staff infection rate during crisis" (A=0.847), "Infectious waste volume" (A=0.842), and "Continuous staff training for epidemic conditions" (A=0.838) respectively had the highest importance coefficients. Analysis of the results showed that critical indicators were mainly concentrated in two dimensions: health (focusing on human resource protection) and environmental (focusing on waste and wastewater management). This model emphasizes the interconnectedness of these dimensions and the necessity of an integrated approach.
Conclusion: The findings confirm that hospital resilience against pandemics is a multidimensional phenomenon dependent on the dynamic interaction of managerial, health, environmental, and technical factors. The proposed model provides an operational framework for assessing and enhancing hospital resilience. Simultaneous attention to staff health, management of environmental impacts, and reinforcement of critical infrastructure is recommended as fundamental pillars for increasing hospital system resilience in designing preparedness and response plans. This model can serve as a practical tool for hospital administrators and policymakers to prioritize resource allocation and improve preparedness for emergency situations.
Conflict of interest: Not reported
Finding: None
Methods: This study was conducted at Shahid Dr. Lavasani Hospital using a three-phase mixed-methods approach. In the first phase, a systematic review of credible databases and documents from international organizations was performed, extracting a preliminary framework consisting of 30 indicators across four main dimensions: managerial-structural, health, environmental, and technical-infrastructural. In the second phase, a 15-member expert panel was selected through purposive sampling. The indicators were evaluated using a two-round fuzzy Delphi technique. In the first round, a questionnaire based on triangular fuzzy numbers was designed and provided to the experts. In the second round, controlled feedback was provided based on the preliminary analysis of the first round's results. Data were analyzed using the centroid method, and the final importance coefficient (A) for each indicator was calculated. Indicators with an importance coefficient higher than 0.7 (21 indicators) were selected as final indicators. Reliability was confirmed with a Kendall's W coefficient of 0.85.
Findings: The fuzzy Delphi process led to the confirmation and prioritization of 21 final indicators from the initial set. The indicators "Staff infection rate during crisis" (A=0.847), "Infectious waste volume" (A=0.842), and "Continuous staff training for epidemic conditions" (A=0.838) respectively had the highest importance coefficients. Analysis of the results showed that critical indicators were mainly concentrated in two dimensions: health (focusing on human resource protection) and environmental (focusing on waste and wastewater management). This model emphasizes the interconnectedness of these dimensions and the necessity of an integrated approach.
Conclusion: The findings confirm that hospital resilience against pandemics is a multidimensional phenomenon dependent on the dynamic interaction of managerial, health, environmental, and technical factors. The proposed model provides an operational framework for assessing and enhancing hospital resilience. Simultaneous attention to staff health, management of environmental impacts, and reinforcement of critical infrastructure is recommended as fundamental pillars for increasing hospital system resilience in designing preparedness and response plans. This model can serve as a practical tool for hospital administrators and policymakers to prioritize resource allocation and improve preparedness for emergency situations.
Conflict of interest: Not reported
Finding: None
Keywords: Hospital Resilience, Health Risks, Environmental Risks, Emerging Diseases, Fuzzy Delphi Method, Crisis Management.
Type of Study: Applicable |
Subject:
Qualitative and quantitative studies
Received: 2025/11/12 | Accepted: 2026/02/24 | Published: 2026/03/30
Received: 2025/11/12 | Accepted: 2026/02/24 | Published: 2026/03/30
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