International Journal of Public Health and Safety

Educational escape rooms are an emerging form of experiential learning that has been gaining popularity in health professions education. An educational escape room is a physical or digital game in which players are placed in a simulated environment and must use critical thinking and problem-solving skills to solve puzzles and challenges to escape the room. The aim of this systematic review is to assess the usefulness of educational escape rooms in health professions education.

Dose optimization techniques are important strategies used by healthcare systems to ensure that patients receive the right amount of medication. These techniques are designed to optimize the benefits of medication while minimizing the risks of side effects and adverse events. In this article, we will discuss dose optimization techniques from the perspective of healthcare systems. Medication reconciliation: Medication reconciliation is the process of creating a comprehensive list of a patient's current medications, including the dose and frequency of each medication. This process helps to identify potential drug interactions, duplicate therapies, and medication errors. By reconciling medication lists, healthcare systems can ensure that patients are receiving the right dose of each medication.

Food safety is an important concern for consumers and food producers alike. With the increasing complexity of the food supply chain and the global nature of the food industry, the risk of foodborne illness has become a major public health issue. Risk prediction models are an important tool for food safety management, as they can help identify high-risk products, processes, and supply chains. In this article, we will discuss an improved random forest-based risk prediction model for food safety with virtual sample integration. Random forest is a popular machine learning algorithm that is widely used for risk prediction in various fields, including healthcare, finance, and ecology. Random forest is an ensemble learning method that combines multiple decision trees to generate a robust and accurate prediction model. In food safety, random forest has been used to predict the risk of foodborne illness based on various factors, such as food type, production process, and contamination history.