Rouhangiz Asadi, Dr Masoud Etemadian, Dr Pejman Shadpour, Fatemeh Semnani,
Volume 16, Issue 4 (2-2018)
Abstract
Background: In recent years, Hashemi Nejad Hospital was outsourced or insourced some of their services to private sector or will have decision to do it. Selection and assessment of suppliers in outsourcing of hospital services is a critical issue. In this study, selecting and evaluating suppliers for outsourcing services in hospitals was evaluated.
Materials and Methods: In order to achieve the goal, evaluating and selecting outsourcing service providers with studies and using opinion of the experts and medical experts, consisting of hospital manager, quality manager, HR managers, officials outsourced parts and other experts in this respect which includes 14 criteria. Identified criteria were clustered in three areas of service features, characteristics and criteria for communications suppliers in the supply chain; supplier selection problem is the problem multi-criteria decision. So, criteria were ranked and weighted using the Expert choice 11 software and AHP.
Results: Based on the study results, sub-criteria of the quality of service, management systems, customer care, and information security had greatest impact on the selection of suppliers and sub-criteria, geographic location, flexibility and problem solving had the lowest priority.
Conclusion: C supplier had the highest priority according to the communication criteria and A supplier had the highest priority according to two other criteria. In total, the supplier A had the first priority, supplier B had the second priority and supplier C had the third priority.
Mohammadreza Shahraki, Hamidreza Esmaeili,
Volume 22, Issue 4 (1-2024)
Abstract
Background and Purpose: Artificial intelligence (AI) plays a crucial role in the optimal management of hospital waste, particularly in predicting the volume and type of waste generated. This study aims to identify and rank the risks associated with the use of AI systems in hospital waste management by employing a multi-criteria decision-making approach.
Methods: This descriptive, cross-sectional study was conducted in 2023 (1402 in the Iranian calendar) at two hospitals, Ali Ibn Abitaleb and Khatam al-Anbiah, in Zahedan. Ten hospital staff members were selected as expert participants for the Delphi panel. The Shannon entropy method was utilized for risk weighting, and the TOPSIS method was applied to rank the identified risks.
Results: Kendall's coordination coefficient was used to assess the level of consensus among the Delphi panel members, with the coefficient values for the first, second, and third Delphi rounds being 6.3, 7.1, and 7.3, respectively. The indicators were weighted using the Shannon entropy method, based on three criteria: impact intensity (0.3), probability of occurrence (0.4), and detection probability (0.32). The TOPSIS method was then employed to rank the identified risks, with the most significant risks being the need for necessary infrastructure (0.847), the requirement for accurate and complete data (0.751), and budget constraints (0.749).
Conclusion: By applying multi-criteria decision-making methods, healthcare managers can effectively identify and prioritize the risks associated with using AI systems in hospital waste management, enabling them to focus on strengthening waste management practices based on these priorities.
