Evaluating the capability of ChatGPT in predicting drug-drug interactions: Real-world evidence using hospitalized patient data.

Drug-drug interactions (DDIs) present a significant health burden, compounded by clinician time constraints and poor patient health literacy. We assessed the ability of ChatGPT (generative artificial intelligence-based large language model) to predict DDIs in a real-world setting. Demographics, diagnoses and prescribed medicines for 120 hospitalized patients were input through three standardized prompts to ChatGPT version 3.

On- and off-label utilisation of antipsychotics in Australia (2000-2021): Retrospective analysis of two medication datasets.

Objective: To determine antipsychotic utilisation patterns in Australian adults from 2005 to 2021, with a focus on on-label and off-label prescriptions.

Methods: We examined antipsychotic dispensing trends in adults from 2005 to 2021 using a 10% sample of the Pharmaceutical Benefits Scheme (PBS) dataset, which contains patient-level information on medicines dispensed throughout Australia. The lack of diagnostic information in PBS was substituted by analysing BEACH (Bettering the Evaluation And Care of Health) dataset, a cross-sectional national survey from 2000 to 2016, consisting of data from general practitioner-patient encounters.