Maximizing efficiency in emergency care: triple interventions to minimize left without being seen: An observational study.

Left without being seen (LWBS) is a quality care metric associated with patient-centered outcomes. The risks affecting LWBS are complex and interventions targeting certain risks have diverse effects. We aimed to use different artificial intelligence and machine learning (AI/ML) algorithms to identify the risks affecting LWBS, implement triple interventions specifically targeted at such risks, and compare daily LWBS rate changes before and after the intervention.

Correction: Peptide macrocyclisation intramolecular interception of visible-light-mediated desulfurisation.

[This corrects the article DOI: 10.1039/D3SC05865D.].

What explains differences in average wait time in the emergency department among different racial and ethnic populations: A linear decomposition approach.

Objective: Non-Hispanic Black (NHB) and Hispanic/Latino (Hispanic) patients wait longer in the emergency department (ED) to see practitioners when compared with non-Hispanic White (NHW) patients. We investigate factors contributing to longer wait times for NHB and Hispanic patients using a linear decomposition approach.

Methods: This retrospective observational study included patients presenting to one tertiary hospital ED from 2019 to 2021.

Peptide macrocyclisation intramolecular interception of visible-light-mediated desulfurisation.

Synthetic methods that enable the macrocyclisation of peptides facilitate the development of effective therapeutic and diagnostic tools. Herein we report a peptide cyclisation strategy based on intramolecular interception of visible-light-mediated cysteine desulfurisation. This method allows cyclisation of unprotected peptides in an aqueous solution the installation of a hydrocarbon linkage.

Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.

Objectives: Natural language processing (NLP) represents one of the adjunct technologies within artificial intelligence and machine learning, creating structure out of unstructured data. This study aims to assess the performance of employing NLP to identify and categorize unstructured data within the emergency medicine (EM) setting.

Methods: We systematically searched publications related to EM research and NLP across databases including MEDLINE, Embase, Scopus, CENTRAL, and ProQuest Dissertations & Theses Global.