Alert-Grouping: Smart Personalization of Monitoring System Thresholds to Help Healthcare Teams Struggle with Alarm Fatigue in Intensive Care.

In Intensive Care Units (ICUs), patients are monitored using various devices that generate alerts when specific metrics, such as heart rate and oxygen saturation, exceed predetermined thresholds. However, these alerts can be inaccurate and lead to alert fatigue, resulting in errors and inaccurate diagnoses. We propose Alert grouping, a "Smart Personalization of Monitoring System Thresholds to Help Healthcare Teams Struggle Alarm Fatigue in Intensive Care" model. The alert grouping looks at patients at the individual and cluster levels, and healthcare-related constraints to assist medical and nursing teams in setting personalized alert thresholds of vital parameters. By simulating the function of ICU patient bed devices, we demonstrate that the proposed alert grouping model effectively reduces the number of alarms overall, improving the alert system's validity and reducing alarm fatigue. Implementing this personalized alert model in ICUs boosts medical and nursing teams' confidence in the alert system, leading to better care for ICU patients by significantly reducing alarm fatigue, thereby improving the quality of care for ICU patients.

Project Management Monitoring Based on Expected Duration Entropy.

Projects are rarely executed exactly as planned. Often, the actual duration of a project's activities differ from the planned duration, resulting in costs stemming from the inaccurate estimation of the activity's completion date. While monitoring a project at various inspection points is pricy, it can lead to a better estimation of the project completion time, hence saving costs. Nonetheless, identifying the optimal inspection points is a difficult task, as it requires evaluating a large number of the project's path options, even for small-scale projects. This paper proposes an analytical method for identifying the optimal project inspection points by using information theory measures. We search for monitoring (inspection) points that can maximize the information about the project's estimated duration or completion time. The proposed methodology is based on a simulation-optimization scheme using a Monte Carlo engine that simulates potential activities' durations. An exhaustive search is performed of all possible monitoring points to find those with the highest expected information gain on the project duration. The proposed algorithm's complexity is little affected by the number of activities, and the algorithm can address large projects with hundreds or thousands of activities. Numerical experimentation and an analysis of various parameters are presented.

Statistical learning methodologies and admission prediction in an emergency department.

BACKGROUND: The quality of an emergency department (ED) is highly dependent on its ability to supply efficient, as well as high-quality treatment for all patients. Key performance indicators are important when measuring the performance of an emergency department. This study aimed to perform an exploratory data analysis and to develop an admission prediction model based on a dataset that was constructed from key performance indicators selected by a panel of expert physicians, nurses and hospital administrators. METHODS: A dataset of 172,695 records was retrospectively collected from an Emergency Department. The relationships within the dataset were analyzed and three machine learning algorithms were compared for an admission predictive model based on the initial patient information. RESULTS: The dataset showed that mean length of stay was similar in the different weekdays, there was a positive linear relationship between the length of stay and patient age and the admission predictive model yielded an AUC of 0.79. CONCLUSIONS: The selected indicators can be used to study whether emergency department allocates its resources properly to cope with overcrowding and the predictive model may be employed by Hospital and ED administrates to fill information gaps and support decision making for the improvement of the key performance indicators.