Generalizability in real-world trials.

Real-world evidence (RWE) trials have a key advantage over conventional randomized controlled trials (RCTs) due to their potentially better generalizability. High generalizability of study results facilitates new biological insights and enables targeted therapeutic strategies. Random sampling of RWE trial participants is regarded as the gold standard for generalizability.

Effects of Face Mask Mandates on COVID-19 Transmission in 51 Countries: Retrospective Event Study.

Background: The question of the utility of face masks in preventing acute respiratory infections has received renewed attention during the COVID-19 pandemic. However, given the inconclusive evidence from existing randomized controlled trials, evidence based on real-world data with high external validity is missing.

Objective: To add real-world evidence, this study aims to examine whether mask mandates in 51 countries and mask recommendations in 10 countries increased self-reported face mask use and reduced SARS-CoV-2 reproduction numbers and COVID-19 case growth rates.

[The maturity model for the public health service-a tool for assessing and improving the digital maturity level of German public health agencies].

The COVID 19 crisis has highlighted the key role of the public health service (PHS), with its approximately 375 municipal health offices involved in the pandemic response. Here, in addition to a lack of human resources, the insufficient digital maturity of many public health departments posed a hurdle to effective and scalable infection reporting and contact tracing. In this article, we present the maturity model (MM) for the digitization of health offices, the development of which took place between January 2021 and February 2022 and was funded by the German Federal Ministry of Health.

The Prediction of Fall Circumstances Among Patients in Clinical Care - A Retrospective Observational Study.

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.

Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review.

Background: Artificial intelligence (AI) holds the promise of supporting nurses' clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios.

Objective: This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care.