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This study aims to investigate whether different types of electronic health record (EHR) users have distinct preferences for data quality assessment indicators (DQAI) and explore how these preferences can guide the enhancement of EHR systems and the optimization of related policies.

To assess the current state of sex, sexual orientation, and gender identity (SSOGI) data collection options in US electronic health record (EHR) platforms.

To improve the performance of a social risk score (a predictive risk model) using electronic health record (EHR) structured and unstructured data.

Extracting the sample size from randomized controlled trials (RCTs) remains a challenge to developing better search functionalities or automating systematic reviews. Most current approaches rely on the sample size being explicitly mentioned in the abstract. The objective of this study was, therefore, to develop and validate additional approaches.

This perspective summarizes key themes that arose from stakeholder discussions at the inaugural Clinical Decision Support Innovation Collaborative (CDSiC) 2023 Annual Meeting. The CDSiC is an Agency for Healthcare Research and Quality (AHRQ)-funded innovation hub for patient-centered clinical decision support (PC CDS).

The NIH All of Us Research Program aims to advance personalized medicine by not only linking patient records, surveys, and genomic data but also engaging with participants, particularly from groups traditionally underrepresented in biomedical research (UBR). This study details how the dialogue between scientists and community members, including many from communities of color, shaped local research priorities.

Implicit bias perpetuates health care inequities and manifests in patient-provider interactions, particularly nonverbal social cues like dominance. We investigated the use of artificial intelligence (AI) for automated communication assessment and feedback during primary care visits to raise clinician awareness of bias in patient interactions.

Conducting simulation testing with end-users is essential for facilitating successful implementation of new health information technologies. This study designed a standardized simulation testing process with a system prototype prior to implementation to help study teams identify the system's interpretability and feasibility from the end-user perspective and to effectively integrate new innovations into real-world clinical settings and workflows.

To assess the health disparities across social determinants of health (SDoH) domains for the risk of severe acidosis independent of demographical and clinical factors.

This survey aims to understand frontline healthcare professionals' perceptions of artificial intelligence (AI) in healthcare and assess how AI familiarity influences these perceptions.