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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.

Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments due to numerous hurdles impeding its translation into practice. The few existing real-world FL initiatives rarely communicate specific measures taken to overcome these hurdles. To bridge this significant knowledge gap, we propose a comprehensive guide for real-world FL in radiology. Minding efforts to implement real-world FL, there is [...]

Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented in narrative clinical notes rather than as structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of extraction of such information.

To quantify how many patient scheduled hours would result in a 40-h work week (PSH40) for ambulatory physicians and to determine how PSH40 varies by specialty and practice type.

Human monitoring of personal protective equipment (PPE) adherence among healthcare providers has several limitations, including the need for additional personnel during staff shortages and decreased vigilance during prolonged tasks. To address these challenges, we developed an automated computer vision system for monitoring PPE adherence in healthcare settings. We assessed the system performance against human observers detecting nonadherence in a video surveillance experiment.

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.

Missed and delayed cancer diagnoses are common, harmful, and often preventable. We previously validated a digital quality measure (dQM) of emergency presentation (EP) of lung cancer in 2 US health systems. This study aimed to apply the dQM to a new national electronic health record (EHR) database and examine demographic associations.

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.

To examine changes in technology-related errors (TREs), their manifestations and underlying mechanisms at 3 time points after the implementation of computerized provider order entry (CPOE) in an electronic health record; and evaluate the clinical decision support (CDS) available to mitigate the TREs at 5-years post-CPOE.