The substance-exposed birthing person-infant/child dyad and health information exchange in the United States.

Objective: Timely access to data is needed to improve care for substance-exposed birthing persons and their infants, a significant public health problem in the United States. We examined the current state of birthing person and infant/child (dyad) data-sharing capabilities supported by health information exchange (HIE) standards and HIE network capabilities for data exchange to inform point-of-care needs assessment for the substance-exposed dyad.

Material And Methods: A cross-map analysis was performed using a set of dyadic data elements focused on pediatric development and longitudinal supportive care for substance-exposed dyads (70 birthing person and 110 infant/child elements).

The potential of Generative Pre-trained Transformer 4 (GPT-4) to analyse medical notes in three different languages: a retrospective model-evaluation study.

Background: Patient notes contain substantial information but are difficult for computers to analyse due to their unstructured format. Large-language models (LLMs), such as Generative Pre-trained Transformer 4 (GPT-4), have changed our ability to process text, but we do not know how effectively they handle medical notes. We aimed to assess the ability of GPT-4 to answer predefined questions after reading medical notes in three different languages.