Predictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning.

The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions.

Trophic levels of fish species of commercial importance in the Colombian Caribbean.

Ecological studies on commercial important fish species are of great value to support resource management issues. This study calculated trophic levels of those Colombian Caribbean fish species whose diet has been locally described. Usable diet data of 119 species resulted in 164 trophic level estimates.