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The expansion of artificial intelligence (AI)-enabled clinical decision support (CDS) requires nurses to interpret complex model outputs. However, their cognitive readiness remains underexplored, particularly in terms of their understanding of statistics. To assess nurses' understanding of key statistical concepts underlying AI predictions and their relationship to health numeracy.

The COVID-19 pandemic caused a major health crisis worldwide significantly impacting mental well-being. In this study, our objective is to assess the resilience of pre-pandemic depression level prediction models when applied to COVID-19 era data. We leverage advanced Machine Learning (ML) and Explainable Artificial Intelligence (XAI) techniques to identify the key factors impacting the shifts in depression levels during the pandemic. We aim to align the later identification with interventions [...]

This study aims to develop a detailed understanding of provider and Information Technology (IT) operations staff experiences and attitudes regarding patients' ability to edit their data. This includes understanding barriers to developing a process to write back data into the electronic health record (EHR) as well as a concrete set of recommendations on incorporating patient-generated data into the EHR.

To develop a multimodal pediatric critical care datamart supporting predictive modeling and decision support tool development, integrating high-resolution physiologic and clinical data and future clinical deployment.

Electronic health record (EHR) order preference lists and order sets potentially improve efficiency but have limited utility in complex primary care settings. We assessed adoption, impact on ordering efficiency, and clinician perceptions of a comprehensive set of nested order panels (xOrders) for adult primary care.

This study explores the capability of passive digital sensor data from smartphones and smartwatches to predict self-reported ecological momentary assessments (EMA) of affect, motivation, interest, and pleasure in activities in an unseen test sample.

To inform initiatives to improve the interoperability of healthcare data, we described the experience of distinct phenotypes of physicians when obtaining information from outside sources.

To support ambulatory care innovation, we created Observer, a multimodal dataset comprising videotaped outpatient visits, electronic health record (EHR) data, and structured surveys. This paper describes the data collection procedures and summarizes the clinical and contextual features of the dataset.

To develop a transfer-learning Bayesian sparse logistic regression model that transfers information learned from one dataset to another by using an informed prior to facilitate model fitting in small-sample clinical patient-level prediction problems that suffer from a lack of available information.

This content analysis study investigates potential biases in image generation by 2 artificial intelligence (AI) tools, DALL-E 3 and Midjourney, in portraying older adults and individuals living with dementia. Despite widespread use of generative AI in various sectors, there is limited research on how these models might perpetuate stereotypes and stigmatization through their images.