Optimizing ChatGPT's Interpretation and Reporting of Delirium Assessment Outcomes: Exploratory Study.

Background: Generative artificial intelligence (AI) and large language models, such as OpenAI's ChatGPT, have shown promising potential in supporting medical education and clinical decision-making, given their vast knowledge base and natural language processing capabilities. As a general purpose AI system, ChatGPT can complete a wide range of tasks, including differential diagnosis without additional training. However, the specific application of ChatGPT in learning and applying a series of specialized, context-specific tasks mimicking the workflow of a human assessor, such as administering a standardized assessment questionnaire, followed by inputting assessment results in a standardized form, and interpretating assessment results strictly following credible, published scoring criteria, have not been thoroughly studied.

Validation of the Sour Seven Questionnaire for screening delirium in hospitalized seniors by informal caregivers and untrained nurses.

Background: Delirium is a common condition in hospitalized seniors that nonetheless often goes undetected by nurses or is delayed in being detected which negatively impacts quality of care and outcomes. We sought to develop a new screening tool for delirium, The Sour Seven Questionnaire, a 7-item questionnaire suitable to be completed from informal or untrained caregiver observation. The study aimed to develop the scoring criteria for a positive delirium screen and assess concurrent validity of the questionnaire against a geriatric psychiatrist's assessment.