Showing 2 results for Hospital Waste
Dr Mohammad Arab, Farhad Habibi Nodeh, Dr Abbas Rahimi Foroushani, Dr Ali Akbari Sari,
Volume 13, Issue 4 (3-2015)
Abstract
Background: Hospital waste need a very sensitive and cautious attention due to holding hazardous, toxic, and pathogenic factors such as infectious, pharmaceutical, pathological, chemical and radioactive left-overs. Thus, this study aimed to evaluate the observance of safety measures by workers responsible for collecting hospital wastes in the public hospitals affiliated to Tehran University of medical sciences.
Methods and Materials: This cross-sectional and descriptive-analytic study was conducted in 1391. Data were collected through using a questionnaire. According to the frequency distribution, total score for participants was divided into three weak (<26), average (26-30), and high (>30) categories. Data were analyzed by the SPSS 18 software using T-Test, one-way ANOVA and regression analysis.
Findings: Based on the results, 33.3% of hospitals received suitable, 55.5% received average and the remaining (11.2%) received a weak score regarding safety measures. Moreover, there was a statistically significant correlation between cleaning staff’s characteristics (education, age, work experiences and their training) with their safety status score.
Conclusion: Implementing current national principles and standards and conquering shortages, proper planning, using young workers alongside with experienced ones, more training courses and respecting and paying enough attention to cleaning staff would help to improve the safety of collecting hospital wastes.
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.
