Feedback on clinical team performance: how does it work, in what contexts, for whom, and for what changes? A critical realist qualitative multiple case study.

Background: Feedback on clinical performance aims to provide teams in health care settings with structured results about their performance in order to improve these results. Two systematic reviews that included 147 randomized studies showed unresolved variability in professional compliance with desired clinical practices. Conventional recommendations for improving feedback on clinical team performance generally appear decontextualized and, in this regard, idealized.

How does nursing-sensitive indicator feedback with nursing or interprofessional teams work and shape nursing performance improvement systems? A rapid realist review.

Background: Care quality varies between organizations and even units within an organization. Inadequate care can have harmful financial and social consequences, e.g.

Inspirational Adversarial Image Generation.

The task of image generation started receiving some attention from artists and designers, providing inspiration for new creations. However, exploiting the results of deep generative models such as Generative Adversarial Networks can be long and tedious given the lack of existing tools. In this work, we propose a simple strategy to inspire creators with new generations learned from a dataset of their choice, while providing some control over the output.

Nursing discharge teaching for hospitalized older people: A rapid realist review.

Aim: To develop, refine and put forward a programme theory that describes configurations between context, hidden mechanisms and outcomes of nursing discharge teaching.

Design: Rapid realist review guided by Pawson's recommendations and using the Realist and Meta-narrative Evidence Syntheses: Evolving Standards.

Data Sources: We performed searches in MEDLINE, Embase, CINAHL Full text, Google Scholarand supplementary searches in Google.

A deep neural network for 12-lead electrocardiogram interpretation outperforms a conventional algorithm, and its physician overread, in the diagnosis of atrial fibrillation.

Background: Automated electrocardiogram (ECG) interpretations may be erroneous, and lead to erroneous overreads, including for atrial fibrillation (AF). We compared the accuracy of the first version of a new deep neural network 12-Lead ECG algorithm (Cardiologs®) to the conventional Veritas algorithm in interpretation of AF.

Methods: 24,123 consecutive 12-lead ECGs recorded over 6 months were interpreted by 1) the Veritas® algorithm, 2) physicians who overread Veritas® (Veritas® + physician), and 3) Cardiologs® algorithm.