Search results for: Milana Avramov
3 Exploring Factors Influencing Orthopedic Patients' Willingness to Recommend a Hospital: Insights from a Cross-Sectional Survey
Authors: Merav Ben Natan, David Maman, Milana Avramov, Galina Shamilov, Yaron Berkovich
Abstract:
Introduction: Patient satisfaction and the willingness to recommend a hospital are vital for improving healthcare quality. This study examines orthopedic patients to identify factors influencing their willingness to recommend the hospital. Aim: This study to explore the demographic and clinical variables affecting orthopedic patients' willingness to recommend the hospital and to understand the role of patient satisfaction in this context. Methods: A cross-sectional survey was conducted with 200 orthopedic patients hospitalized between July and December 2023 in north-central Israel. Data were analyzed to assess the impact of various factors on the willingness to recommend the hospital. Results: Age was positively associated with the willingness to recommend (OR=2.44), while the length of stay in the Emergency Department negatively impacted this willingness (OR=0.58). Satisfaction with hospital care had a positive effect on willingness to recommend (OR=1.96). Gender, comorbidities, and total hospital stay length did not significantly influence willingness to recommend. Conclusions: Satisfaction with hospital care and the length of Emergency Department stays are crucial factors affecting orthopedic patients' willingness to recommend the hospital. This underscores the need for strategies to improve patient experiences and address delays in the Emergency Department. The findings offer valuable insights for healthcare providers and policymakers.Keywords: orthopedic patients, patient satisfaction, willingness to recommend, hospital recommendation
Procedia PDF Downloads 02 Monitoring of Pesticide Content in Biscuits Available on the Vojvodina Market, Serbia
Authors: Ivana Loncarevic, Biljana Pajin, Ivana Vasiljevic, Milana Lazovic, Danica Mrkajic, Aleksandar Fises, Strahinja Kovacevic
Abstract:
Biscuits belong to a group of flour-confectionery products that are considerably consumed worldwide. The basic raw material for their production is wheat flour or integral flour as a nutritionally highly valuable component. However, this raw material is also a potential source of contamination since it may contain the residues of biochemical compounds originating from plant and soil protection agents. Therefore, it is necessary to examine the health safety of both raw materials and final products. The aim of this research was to examine the content of undesirable residues of pesticides (mostly organochlorine pesticides, organophosphorus pesticides, carbamate pesticides, triazine pesticides, and pyrethroid pesticides) in 30 different biscuit samples of domestic origin present on the Vojvodina market using Gas Chromatograph Thermo ISQ/Trace 1300. The results showed that all tested samples had the limit of detection of pesticide content below 0.01 mg/kg, indicating that this type of confectionary products is not contaminated with pesticides.Keywords: biscuits, pesticides, contamination, quality
Procedia PDF Downloads 1701 Automatic Classification of the Stand-to-Sit Phase in the TUG Test Using Machine Learning
Authors: Yasmine Abu Adla, Racha Soubra, Milana Kasab, Mohamad O. Diab, Aly Chkeir
Abstract:
Over the past several years, researchers have shown a great interest in assessing the mobility of elderly people to measure their functional status. Usually, such an assessment is done by conducting tests that require the subject to walk a certain distance, turn around, and finally sit back down. Consequently, this study aims to provide an at home monitoring system to assess the patient’s status continuously. Thus, we proposed a technique to automatically detect when a subject sits down while walking at home. In this study, we utilized a Doppler radar system to capture the motion of the subjects. More than 20 features were extracted from the radar signals, out of which 11 were chosen based on their intraclass correlation coefficient (ICC > 0.75). Accordingly, the sequential floating forward selection wrapper was applied to further narrow down the final feature vector. Finally, 5 features were introduced to the linear discriminant analysis classifier, and an accuracy of 93.75% was achieved as well as a precision and recall of 95% and 90%, respectively.Keywords: Doppler radar system, stand-to-sit phase, TUG test, machine learning, classification
Procedia PDF Downloads 153