Showing 4 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.
Abbas Jahangiri,
Volume 23, Issue 4 (2-2025)
Abstract
Background and purpose: Pharmaceutical pollutants in hospital wastewater pose significant environmental and public health risks. This study aimed to identify and prioritize effective strategies for removing these contaminants from the wastewater of selected hospitals in Markazi Province, Iran.
Methods: This descriptive-analytical study employed a multi-criteria decision-making (MCDM) approach. Initially, a comprehensive review of scientific literature, expert interviews, and consultation with professionals in environmental health and wastewater treatment was conducted. Relevant strategies and prioritization criteria were identified using MAXQDA 2022. Subsequently, the Analytic Hierarchy Process (AHP) method was applied using Expert Choice version 11 software to assign weights to criteria and rank the identified strategies.
Results: Five key treatment strategies were identified: (1) integrated biological, physical, and chemical treatment methods (hybrid systems), (2) advanced oxidation processes (AOPs), (3) aerobic and anaerobic biological reactors, (4) membrane filtration, and (5) activated carbon adsorption. The prioritization was based on five criteria: (1) pollutant removal efficiency (0.357), (2) environmental compatibility (0.241), (3) implementation and operational costs (0.198), (4) technology durability and sustainability (0.123), and (5) implementation complexity and feasibility (0.081). The final priority scores of the strategies were 0.312, 0.256, 0.211, 0.134, and 0.087, respectively.
Conclusion: The findings indicate that hybrid treatment systems combining biological, chemical, and physical processes offer the most effective strategy for eliminating pharmaceutical pollutants in hospital wastewater. These insights can guide healthcare policymakers and hospital administrators in selecting optimal wastewater treatment methods, contributing to environmental protection and water quality improvement.
Abbas Jahangiri,
Volume 24, Issue 1 (5-2025)
Abstract
Background and purpose: Hospital wastewater infrastructure is critical for safeguarding public health and protecting the environment. Deficiencies in the management of these systems can precipitate severe public health and environmental crises. This study aimed to identify and prioritize investment risks associated with hospital wastewater infrastructure.
Methods: This applied case study was conducted in a general hospital in Arak, Iran, during April 2025. Initial risk identification involved a comprehensive literature review and semi-structured interviews with 14 experts, with data analysis facilitated by MAXQDA 2022 software. Subsequently, a Failure Mode and Effects Analysis (FMEA) approach, utilizing a customized checklist, was employed to score each identified risk based on its severity, probability of occurrence, and detectability. The Risk Priority Number (RPN) for each risk was then calculated using Microsoft Excel. Finally, risks were ranked in descending order according to their RPN values.
Results: A total of 23 key risks were identified and categorized into five principal areas: design, technical, environmental, operational, and managerial. The highest RPNs were attributed to "lack of pre-treatment systems," "insufficient capacity planning," and "wastewater leakage into surrounding soil". Additionally, managerial and operational risks, such as "insufficient budget for maintenance" and "shortage of skilled personnel," were recognized as significant aggravating factors for other risks.
Conclusion: The findings underscore that many critical risks within hospital wastewater infrastructure originate from fundamental weaknesses in initial design and ongoing management. The FMEA method proved to be an effective and systematic tool for identifying and prioritizing these risks, thereby facilitating improved engineering and managerial decision-making and enhancing the overall effectiveness of investments in this vital infrastructure.
