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.

Development and Validation of an Algorithm for Thrombosis with Thrombocytopenia Syndrome (TTS) at Unusual Sites.

Introduction: Thrombosis with thrombocytopenia syndrome (TTS) has been reported following receipt of adenoviral vector-based COVID-19 vaccines. However, no validation studies evaluating the accuracy of International Classification of Diseases-10-Clinical Modification (ICD-10-CM)-based algorithm for unusual site TTS are available in the published literature.

Methods: The purpose of this study was to assess the performance of clinical coding to 1) leverage literature review and clinical input to develop an ICD-10-CM-based algorithm to identify unusual site TTS as a composite outcome and 2) validate the algorithm against the Brighton Collaboration's interim case definition using laboratory, pathology, and imaging reports in an academic health network electronic health record (EHR) within the US Food and Drug Administration (FDA) Biologics Effectiveness and Safety (BEST) Initiative.

Characterization of COVID-19 Hospitalized Patients in Three United States Electronic Health Record Databases.

COVID-19 infections have contributed to substantial increases in hospitalizations. This study describes demographics, baseline clinical characteristics and treatments, and clinical outcomes among U.S.