Taxonomy-based prompt engineering to generate synthetic drug-related patient portal messages.

Objective: The objectives of this study were to: (1) create a corpus of synthetic drug-related patient portal messages to address the current lack of publicly available datasets for model development, (2) assess differences in language used and linguistics among the synthetic patient portal messages, and (3) assess the accuracy of patient-reported drug side effects for different racial groups.

Methods: We leveraged a taxonomy for patient- and clinician-generated content to guide prompt engineering for synthetic drug-related patient portal messages. We generated two groups of messages: the first group (200 messages) used a subset of the taxonomy relevant to a broad range of drug-related messages and the second group (250 messages) used a subset of the taxonomy relevant to a narrow range of messages focused on side effects.

Usability, Engagement, and Report Usefulness of Chatbot-Based Family Health History Data Collection: Mixed Methods Analysis.

Background: Family health history (FHx) is an important predictor of a person's genetic risk but is not collected by many adults in the United States.

Objective: This study aims to test and compare the usability, engagement, and report usefulness of 2 web-based methods to collect FHx.

Methods: This mixed methods study compared FHx data collection using a flow-based chatbot (KIT; the curious interactive test) and a form-based method.