Improving the hospital waste management at the Farabi hospital in Malekan -Iran: An action research study.

Hospital waste poses numerous concerns for both human health and the environment. Using an action research technique, this study attempts to improve waste management at the Farabi Hospital in Malekan city-Iran. In 2020, integrated (quantitative-qualitative) action research was done. For action research, the Simmons model was employed. First, a list of significant issues was found during the waste management process evaluation using a standard checklist and brainstorming with hospital officials and workers. The identified issues were prioritized using a prioritization matrix. Then, after consulting with hospital officials, 11 interventions were designed and implemented over six months. Finally, waste management performance was re-evaluated. Average knowledge of the participants about hospital waste management (HWM) standards was improved significantly (64 ± 13.8 before the training, 84.6 ± 20.6). General waste production was reduced by 27.7% in terms of garbage bags and 23.4% in terms of waste weight (95.5 kg-73.1 kg), respectively. Infectious waste output was reduced by 22.8% in the number of garbage bags and 32.1% in the weight of waste (57.5 kg-39 kg). The rate of compliance with HWM criteria was improved from 10 to 33 items. Although the interventions in this study improved the HWM to an acceptable level, more interventions and ongoing monitoring are required. The study's findings also show that an action research strategy might address a wide range of issues and weaknesses in hospitals and related facilities.

Health Care Waste Management Improvement Interventions Specifications and Results: A Systematic Review and Meta-Analysis.

BACKGROUND: Given the importance of proper management of Health Care Waste Management (HCWM), comprehensive information on interventions in this field is necessary. Therefore, we aimed to systematically review and meta-analysis of characteristics and results of interventions in the field of HCWM. METHODS: The required data were gathered through searching the keywords such as waste management, biomedical waste, hospitals waste, health care waste, infectious waste, medical waste, Waste Disposal Facilities, Garbage, Waste Disposal Facilities, Hazardous Waste Sites in PubMed, Scopus, EMBASE, Google scholar, Cochrane library, Science Direct, web of knowledge, SID and MagIran and hand searching in journals, reference by reference, and search in Gray literatures between 2000 and 2019. CMA software: 2 (Comprehensive Meta-Analysis) was used to perform the meta-analysis. RESULTS: Twenty-seven interventions were evaluated. Most of the studies were conducted after 2010, in the form of pre and post study, without control group, and in hospital. Interventions were divided into two categories: educational interventions (19 studies) and multifaceted managerial interventions (8 studies). The most studied outcome (in 11 studies) was KAP (knowledge, attitude and practice). The mean standard difference of interventions on KAP was estimated 3.04 (2.54-3.54) which was significant statistically (P<0.05). Also, interventions were considerably effective in improving the indicators of waste production amount, waste management costs and overall waste management performance. CONCLUSION: Despite positive effect of interventions, due to the methodological deficiencies of published studies and high heterogeneity in results of studies, caution should be exercised in interpreting and using the results of the studies.

Using a priori information for regularization in breast microwave image reconstruction.

Regularization methods are used in microwave image reconstruction problems, which are ill-posed. Traditional regularization methods are usually problem-independent and do not take advantage of a priori information specific to any particular imaging application. In this paper, a novel problem-dependent regularization approach is introduced for the application of breast imaging. A real genetic algorithm (RGA) minimizes a cost function that is the error between the recorded and the simulated data. At each iteration of the RGA, a priori information about the shape of the breast profiles is used by a neural network classifier to reject the solutions that cannot be a map of the dielectric properties of a breast profile. The algorithm was tested against four realistic numerical breast phantoms including a mostly fatty, a scattered fibroglandular, a heterogeneously dense, and a very dense sample. The tests were also repeated where a 4 mm x 4 mm tumor was inserted in the fibroglandular tissue in each of the four breast types. The results show the effectiveness of the proposed approach, which to the best of our knowledge has the highest resolution amongst the evolutionary algorithms used for the inversion of realistic numerical breast phantoms.

3D breast microwave imaging based on wavefront reconstruction.

Currently, breast cancer is the leading cause of cancer death in women between the ages of 15 and 54, and the second cause of cancer death in women 55 to 74. In recent years, Breast Microwave Imagery (BMI) has shown its potential as a promising breast cancer detection technique. This imaging technology is based on the electrical characteristic differences that exist between normal and malignant breast tissues at the microwave frequency range. A novel reconstruction approach for the formation of 3D BMI models is proposed in this paper. This technique uses the phase differences introduced during the collection of target responses in order to determine the correct spatial location of the different scatterers that constitute the final image. The proposed method yielded promising results when applied to simulated data.