Patient Scheduling Software

This research was conducted by M. Aloraini, A. Al-Badr, and A. AlDhalaan. It was published in the journal BMC Medical Informatics and Decision Making in 2023. The research showed that machine learning models can be used to predict patient no-shows in hospital outpatient departments, which can help improve patient scheduling and reduce wasted resources. The study used data from a large hospital in Saudi Arabia and found that the Gradient Boosting model had the highest prediction accuracy.

This research was conducted by a team of researchers and published in the journal Breast Cancer Research and Treatment in 2023. The study examined the use of 'bridging' endocrine therapy (BrET) as a strategy to manage patient scheduling during the COVID-19 pandemic. The study found that BrET was effective in managing patient care during a period of reduced surgical capacity, and that the majority of patients on BrET had strong ER positivity, suggesting this strategy was reserved for those with tumors that were most likely to respond.