Perceptions of Risk, Benefit and Trust - A Comparative Assessment Between Healthcare Workers and the General Public Towards 3 Medical Technologies.

Background and Objective: While there is a substantial amount of research on risk perception, there has been less focus on the way medical technologies are perceived by experts as opposed to lay individuals. We investigated the factors that may influence the risk perception of healthcare workers (HCWs) and the general public regarding 3 distinct medical technologies: magnetic resonance imaging (MRI), laser-assisted in situ keratomileusis (LASIK) and the Covid-19 vaccine. Methods: A cross-sectional study conducted in 2021 among 2 populations: HCWs employed at a general public hospital and a sample of outpatients and individuals who are not medical professionals. The participants completed an electronic questionnaire. Results: In total, 739 respondents were included: 197 HCWs (26.7%) and 542 members of the public (73.3%). Most of the respondents (89.4%) reported being vaccinated against Covid-19, 43.8% had previously undergone an MRI but 90% had not undergone LASIK. Overall, all 3 technologies assessed in the study were rated by the respondents as having a high benefit and low risk. HCWs and the public showed statistically significant differences in perceived risk towards MRI and LASIK, as well as in some of the risk perception characteristics of each technology. In contrast, no differences in risk perception towards the Covid-19 vaccine were found between HCWs and the public. Both study populations showed a significant negative correlation between trust in the MoH and the perceived risk towards MRI and the Covid-19 vaccine. Both study populations regarded information provided by medical sources as the most reliable for decision-making. Conclusion: The perceptions and concerns towards medical technologies influence individuals' behavior and acceptance of technologies. They are also essential for risk communication. The study contributes to the understanding of attitudes towards various medical technologies, including risk perception, risk characteristics, trust and sources of information pertaining to each of the technologies, by examining the differences between HCWs and the general public.

Comparison of Health Behaviors of Healthcare Workers and the General Public in Israel: A Cross-Sectional Survey.

Healthcare workers (HCWs) are role models and advisors for promoting health behaviors among their patients. We conducted a cross-sectional survey to identify and compare the health behaviors of 105 HCWs and 82 members of the Israeli public. Of 13 health behaviors examined, undergoing screening tests, getting influenza vaccines and smoking were significantly different between the HCWs and the public. Further comparison between physicians and other HCWs (e.g., nurses, physiotherapists, dieticians) showed that the physicians reported the least favorable health behaviors: having less than 7 h of sleep, being less likely to eat breakfast, having greater alcohol consumption and being least likely to undergo regular screening tests. Analysis of a composite healthy lifestyle score (which included 11 health behaviors) showed statistically significant differences among the three groups (p = 0.034): only 10.6% of the physicians had a high healthy lifestyle score compared to the other HCWs (34.5%). In conclusion, the HCWs and the public report suboptimal health behaviors. Beyond the concern for HCWs' personal health, their health behaviors have implications for the health of patients and the general public, as they play an important role in health promotion and counseling. HCWs' suboptimal "health profile" mandates implementing policies to improve their knowledge of recommended health behaviors, primarily targeting physicians, even at an early phase of their professional journey.

Surgery Duration Prediction Using Multi-Task Feature Selection.

Efficient optimization of operating room (OR) activity poses a significant challenge for hospital managers due to the complex and risky nature of the environment. The traditional "one size fits all" approach to OR scheduling is no longer practical, and personalized medicine is required to meet the diverse needs of patients, care providers, medical procedures, and system constraints within limited resources. This paper aims to introduce a scientific and practical tool for predicting surgery durations and improving OR performance for maximum benefit to patients and the hospital. Previous works used machine-learning models for surgery duration prediction based on preoperative data. The models consider covariates known to the medical staff at the time of scheduling the surgery. Given a large number of covariates, model selection becomes crucial, and the number of covariates used for prediction depends on the available sample size. Our proposed approach utilizes multi-task regression to select a common subset of predicting covariates for all tasks with the same sample size while allowing the model's coefficients to vary between them. A regression task can refer to a single surgeon or operation type or the interaction between them. By considering these diverse factors, our method provides an overall more accurate estimation of the surgery durations, and the selected covariates that enter the model may help to identify the resources required for a specific surgery. We found that when the regression tasks were surgeon-based or based on the pair of operation type and surgeon, our suggested approach outperformed the compared baseline suggested in a previous study. However, our approach failed to reach the baseline for an operation-type-based task. By accurately estimating surgery durations, hospital managers can provide care to a greater number of patients, optimize resource allocation and utilization, and reduce waste. This research contributes to the advancement of personalized medicine and provides a valuable tool for improving operational efficiency in the dynamic world of medicine.