The European Researchers' Network Working on Second Victim (ERNST) Policy Statement on the Second Victim Phenomenon for Increasing Patient Safety.

Background: The second victim phenomenon refers to the emotional trauma healthcare professionals experience following adverse events (AEs) in patient care, which can compromise their ability to provide safe care. This issue has significant implications for patient safety, with AEs leading to substantial human and economic costs.

Analysis: Current evidence indicates that AEs often result from systemic failures, profoundly affecting healthcare workers.

A large dataset of annotated incident reports on medication errors.

Incident reports of medication errors are valuable learning resources for improving patient safety. However, pertinent information is often contained within unstructured free text, which prevents automated analysis and limits the usefulness of these data. Natural language processing can structure this free text automatically and retrieve relevant past incidents and learning materials, but to be able to do so requires a large, fully annotated and validated corpus of incident reports.

A deep learning model based on fusion images of chest radiography and X-ray sponge images supports human visual characteristics of retained surgical items detection.

Purpose: Although a novel deep learning software was proposed using post-processed images obtained by the fusion between X-ray images of normal post-operative radiography and surgical sponge, the association of the retained surgical item detectability with human visual evaluation has not been sufficiently examined. In this study, we investigated the association of retained surgical item detectability between deep learning and human subjective evaluation.

Methods: A deep learning model was constructed from 2987 training images and 1298 validation images, which were obtained from post-processing of the image fusion between X-ray images of normal post-operative radiography and surgical sponge.

Annotation Guidelines for Medication Errors in Incident Reports: Validation Through a Mixed Methods Approach.

At present no adequate annotation guidelines exists for incident report learning. This study aims at utilizing multiple quantitative and qualitative evidence to validate annotation guidelines for incident reporting of medication errors. Through multiple approaches via annotator training, annotation performance evaluation, exit surveys, and user and expert interviews, a mixed methods explanatory sequential design was utilized to collect 2-stage evidence for validation.