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Online COVID-19 misinformation is a serious concern in Brazil, home to the second-largest WhatsApp user base and the second-highest number of COVID-19 deaths. We examined the extent to which WhatsApp users might be willing to correct their peers who might share COVID-19 misinformation.

Recent changes to billing policy have reduced documentation requirements for outpatient notes, providing an opportunity to rethink documentation workflows. While many providers use templates to write notes-whether to insert short phrases or draft entire notes-we know surprisingly little about how these templates are used in practice. In this retrospective cross-sectional study, we observed the templates that primary providers and other members of the care team used to write the provider [...]

The aim of this study was to collect and synthesize evidence regarding data quality problems encountered when working with variables related to social determinants of health (SDoH).

During the coronavirus disease 2019 (COVID-19) pandemic, federally qualified health centers rapidly mobilized to provide SARS-CoV-2 testing, COVID-19 care, and vaccination to populations at increased risk for COVID-19 morbidity and mortality. We describe the development of a reusable public health data analytics system for reuse of clinical data to evaluate the health burden, disparities, and impact of COVID-19 on populations served by health centers.

This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019 (COVID-19).

Suboptimal design of health information technology (IT) systems can lead to the introduction of errors in the diagnostic process. We aimed to identify mechanisms that can affect the safety and effectiveness of these systems in hospital settings thus contributing to the building of an explicit and replicable understanding of the variables that can affect the functioning of IT systems.

Use of artificial intelligence in healthcare, such as machine learning-based predictive algorithms, holds promise for advancing outcomes, but few systems are used in routine clinical practice. Trust has been cited as an important challenge to meaningful use of artificial intelligence in clinical practice. Artificial intelligence systems often involve automating cognitively challenging tasks. Therefore, previous literature on trust in automation may hold important lessons for artificial intelligence applications in healthcare. In [...]

Suicide is one of the leading causes of death worldwide, yet clinicians find it difficult to reliably identify individuals at high risk for suicide. Algorithmic approaches for suicide risk detection have been developed in recent years, mostly based on data from electronic health records (EHRs). Significant room for improvement remains in the way these models take advantage of temporal information to improve predictions.