Operating Room Usage Time Estimation with Machine Learning Models.

Effectively handling the limited number of surgery operating rooms equipped with expensive equipment is a challenging task for hospital management such as reducing the case-time duration and reducing idle time. Improving the efficiency of operating room usage via reducing the idle time with better scheduling would rely on accurate estimation of surgery duration. Our model can achieve a good prediction result on surgery duration with a dozen of features. We have found the result of our best performing department-specific XGBoost model with the values 31.6 min, 18.71 min, 0.71, 28% and 27% for the metrics of root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), mean absolute percentage error (MAPE) and proportion of estimated result within 10% variation, respectively. We have presented each department-specific result with our estimated results between 5 and 10 min deviation would be more informative to the users in the real application. Our study shows comparable performance with previous studies, and the machine learning methods use fewer features that are better suited for universal usability.

Can occupational therapy manpower be replaced with social robots in a singing group during COVID-19?

BACKGROUND: Prior to the COVID-19 global health emergency, reducing direct contacts between therapists and patients is an important issue, and could be achieved by using robots to perform certain caring activities. OBJECTIVE: This study compares therapeutic factors of singing group activities directed by social robots and by occupational therapists at elderly care centers during this COVID-19 outbreak. METHODS: This project has a quasi-experimental research design, based on a pilot study of 14 subjects aged above 65 years. They received eight sessions of singing group therapy given by a social robot or an occupational therapist. Completed copies of a therapeutic-factor questionnaire were then collected. RESULTS: At the 4th week, the scores for 8 therapeutic factors were higher in sessions with the occupational therapist than the robot-directed sessions, reaching a statistically significant level; at the 8th week, the scores for 3 therapeutic factors, including imparting of information, were higher in sessions with the occupational therapist than in sessions with the robot. The top scoring therapeutic factor in the robot sessions was group cohesiveness. CONCLUSIONS: Social robots may be good companion tools for elderly care during this COVID-19 outbreak, but group therapy sessions supervised by real-person therapists still have higher therapeutic factor scores than those conducted by robots. The number of subjects needs to be increased to enhance the validity of future study results.