RESUMEN
Digital Mental Health Technologies (DMHTs) have the potential to close treatment gaps in settings where mental healthcare is scarce or even inaccessible. For this, DMHTs need to be affordable, evidence-based, justice-oriented, user-friendly, and embedded in a functioning digital infrastructure. This viewpoint discusses areas crucial for future developments of DMHTs. Drawing back on interdisciplinary scholarship, questions of health equity, consumer-, patient- and developer-oriented legislation, and requirements for successful implementation of technologies across the globe are discussed. Economic considerations and policy implications complement these aspects. We discuss the need for cultural adaptation specific to the context of use and point to several benefits as well as pitfalls of DMHTs for research and healthcare provision. Nonetheless, to circumvent technology-driven solutionism, the development and implementation of DMHTs require a holistic, multi-sectoral, and participatory approach.
RESUMEN
Home delivery services require the attendance of the customer during delivery. Hence, retailers and customers mutually agree on a delivery time window in the booking process. However, when a customer requests a time window, it is not clear how much accepting the ongoing request significantly reduces the availability of time windows for future customers. In this paper, we explore using historical order data to manage scarce delivery capacities efficiently. We propose a sampling-based customer acceptance approach that is fed with different combinations of these data to assess the impact of the current request on route efficiency and the ability to accept future requests. We propose a data-science process to investigate the best use of historical order data in terms of recency and amount of sampling data. We identify features that help to improve the acceptance decision as well as the retailer's revenue. We demonstrate our approach with large amounts of real historical order data from two cities served by an online grocery in Germany.
RESUMEN
Standardized fall risk scores have not proven to reliably predict falls in clinical settings. Machine Learning offers the potential to increase the accuracy of such predictions, possibly vastly improving care for patients at high fall risks. We developed a boosting algorithm to predict both recurrent falls and the severity of fall injuries. The model was trained on a dataset including extensive information on fall events of patients who had been admitted to Charité - Universitätsmedizin Berlin between August 2016 and July 2020. The data were recorded according to the German expert standard for fall documentation. Predictive power scores were calculated to define optimal feature sets. With an accuracy of 74% for recurrent falls and 86% for injury severity, boosting demonstrated the best overall predictive performance of all models assessed. Given that our data contain initially rated risk scores, our results demonstrate that well trained ML algorithms possibly provide tools to substantially reduce fall risks in clinical care settings.
