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This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for patients with kidney failure on dialysis. We explore factors that inform acceptability, interpretability, and implementation of ML-based CPMs among multiple constituent groups.

To evaluate the likelihood of linking electronic health records (EHRs) to restricted individual-level American Community Survey (ACS) data based on patient health condition.

We analyzed trends in adoption of advanced patient engagement and clinical data analytics functionalities among critical access hospitals (CAHs) and non-CAHs to assess how historical gaps have changed.

Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects.

To understand barriers to obtaining and using interoperable information at US hospitals.

The American Medical Informatics Association (AMIA) Task Force on Diversity, Equity, and Inclusion (DEI) was established to address systemic racism and health disparities in biomedical and health informatics, aligning with AMIA's mission to transform healthcare. AMIA's DEI initiatives were spurred by member voices responding to police brutality and COVID-19's impact on Black/African American communities.

We proposed adopting billing models for secure messaging (SM) telehealth services that move beyond time-based metrics, focusing on the complexity and clinical expertise involved in patient care.