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To assess the use of a co-designed patient-reported outcome (PRO) clinical dashboard and estimate its impact on shared decision-making (SDM) and symptomatology in adults with advanced cancer or chronic kidney disease (CKD).

We evaluated the effectiveness and implementability of a standardized EHR-integrated handoff report to support intraoperative handoffs.

Conduct a scoping review of research studies that describe rule-based clinical decision support (CDS) malfunctions.

Outpatient rehabilitation (rehab) physical, occupational, and speech therapists use electronic health records (EHR), yet their documentation experiences, including any documentation burden, are not well researched. Therapists are a growing portion of the U.S. healthcare workforce, whose need is critical to the health of an aging population. We aimed to describe outpatient rehab therapists' documentation experiences and identify strategies for mitigating any documentation burden.

Large Language Models (LLMs) have been proposed as a solution to address high volumes of Patient Medical Advice Requests (PMARs). This study addresses whether LLMs can generate high quality draft responses to PMARs that satisfies both patients and clinicians with prompt engineering.

To develop and validate a novel measure, action entropy, for assessing the cognitive effort associated with electronic health record (EHR)-based work activities.

To describe the prevalence and trends in the use of social media over time and explore whether social media use is related to better self-care efficacy and thus related to better mental health among United States older adults with multimorbidity.

To improve firearm injury encounter classification (new vs follow-up) using machine learning (ML) and compare our ML model to other common approaches.

Firearm injury research necessitates using data from often-exploited vulnerable populations of Black and Brown Americans. In order to reduce bias against protected attributes, this study provides a theoretical framework for establishing trust and transparency in the use of AI with the general population.

Firearm violence constitutes a public health crisis in the United States, but comprehensive data infrastructure is lacking to study this problem. To address this challenge, we used natural language processing (NLP) to classify court record documents from alleged violent crimes as firearm-related or non-firearm-related.