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People are increasingly encouraged to self-manage their chronic conditions; however, many struggle to practise it effectively. Most studies that investigate patient work (ie, tasks involved in self-management and contexts influencing such tasks) rely on self-reports, which are subject to recall and other biases. Few studies use wearable cameras and deep learning to capture and classify patient work activities automatically.

To develop a lossless distributed algorithm for generalized linear mixed model (GLMM) with application to privacy-preserving hospital profiling.

A discussion and debate on the American Medical Informatics Association's (AMIA) Ethical, Legal, and Social Issues (ELSI) Working Group listserv in 2021 raised important issues related to a forthcoming conference in Texas. Texas had recently enacted a restrictive abortion law and restricted voting rights. Several AMIA members advocated for a boycott of the state and the scheduled conference. The discussion led the AMIA Board of Directors to request that the [...]

Health care providers increasingly rely upon predictive algorithms when making important treatment decisions, however, evidence indicates that these tools can lead to inequitable outcomes across racial and socio-economic groups. In this study, we introduce a bias evaluation checklist that allows model developers and health care providers a means to systematically appraise a model's potential to introduce bias.

We developed a comprehensive, medication-related clinical decision support (CDS) software prototype for use in the operating room. The purpose of this study was to compare the usability of the CDS software to the current standard electronic health record (EHR) medication administration and documentation workflow.

This study sought both to support evidence-based patient identity policy development by illustrating an approach for formally evaluating operational matching methods, and also to characterize the performance of both referential and probabilistic patient matching algorithms using real-world demographic data.

Tobacco use/smoking for epidemiologic studies is often derived from electronic health record (EHR) data, which may be inaccurate. We previously compared smoking from the United States Veterans Health Administration (VHA) EHR clinical reminder data with survey data and found excellent agreement. However, the smoking clinical reminder items changed October 1, 2018. We sought to use the biomarker salivary cotinine (cotinine ≥30) to validate current smoking from multiple sources.

To improve timely access to quality HIV research data, the Rakai Health Sciences Program (RHSP) Data Mart was developed to store cohort study data from a legacy database platform in a modernized system using standard data management processes. The RHSP Data Mart was developed on a Microsoft SQL Server platform using Microsoft SQL Server Integration Services with custom data mappings and queries. The data mart stores 20+ years of longitudinal [...]

Evaluate an initiative to distribute video-enabled tablets and cell phones to individuals enrolled in Veterans Health Affairs supportive housing program during the COVID-19 pandemic.

Opioid Overdose Network is an effort to generalize and adapt an existing research data network, the Accrual to Clinical Trials (ACT) Network, to support design of trials for survivors of opioid overdoses presenting to emergency departments (ED). Four institutions (Medical University of South Carolina [MUSC], Dartmouth Medical School [DMS], University of Kentucky [UK], and University of California San Diego [UCSD]) worked to adapt the ACT network. The approach that was [...]