Development of a Preliminary Patient Safety Classification System for Generative AI.

Generative artificial intelligence (AI) technologies have the potential to revolutionise healthcare delivery but require classification and monitoring of patient safety risks. To address this need, we developed and evaluated a preliminary classification system for categorising generative AI patient safety errors. Our classification system is organised around two AI system stages (input and output) with specific error types by stage.

Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study.

Background: Adverse events associated with vaccination have been evaluated by epidemiological studies and more recently have gained additional attention with the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct postmarket surveillance, the US Food and Drug Administration continues to monitor several adverse events of special interest (AESIs) to ensure vaccine safety, including for COVID-19.

Objective: This study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the Food and Drug Administration's postmarket surveillance capabilities while minimizing public burden.

Effect of intensive physician oversight on a prehospital rapid-sequence intubation program.

Objective: To examine the effects of adding close concurrent and retrospective physician oversight, consistent with National Association of EMS Physicians (NAEMSP) recommendations, to an existing regional prehospital rapid-sequence intubation (RSI) program.

Methods: This study involved a retrospective cohort of patients receiving RSI between January 1, 2004, and July 31, 2008. On January 1, 2007, an updated program including additional concurrent and retrospective physician oversight, increased RSI-specific continuing medical education, and cadaver laboratory training was implemented.