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Falls in community-dwelling older adults are common, and there is a lack of clinical decision support (CDS) to provide health care providers with effective, individualized fall prevention recommendations.

Hospital-acquired conditions (HACs) are common, costly, and national patient safety priority. Catheter-associated urinary tract infections (CAUTIs), hospital-acquired pressure injury (HAPI), and falls are common HACs. Clinicians assess each HAC risk independent of other conditions. Prevention strategies often focus on the reduction of a single HAC rather than considering how actions to prevent one condition could have unintended consequences for another HAC.

Documentation burden associated with electronic health records (EHR) is well documented in the literature. Usability and functionality of the EHR are considered fragmented and disorganized making it difficult to synthesize clinical information. Few best practices are reported in the literature to support streamlining the configuration of documentation fields to align clinical workflow with EHR data entry elements.

During the coronavirus disease 2019 pandemic, as a safety-net organization with a substantial percentage of patients of color and with limited English proficiency (LEP), we were wary of furthering health disparities in our community. We analyzed gaps in telemedicine (telephone and video) delivery in our communities, quantified the effects of our tests of change, and began the process of accumulating evidence to create a road map for other organizations.

The Chronic Pain Treatment Tracker (Tx Tracker) is a prototype decision support tool to aid primary care clinicians when caring for patients with chronic noncancer pain. This study evaluated clinicians' perceived utility of Tx Tracker in meeting information needs and identifying treatment options, and preferences for visual design.

Emergency department (ED)-based injury surveillance systems across many countries face resourcing challenges related to manual validation and coding of data.

Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions.

As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing diagnostic aid. The purpose of this review is to evaluate machine learning models that use text data for diagnosis and to assess the diversity of the included study populations.

The growing trend to use wearable devices to track activity and health data has the potential to positively impact the patient experience with their health care at home and with their care team. As part of a pilot program, the U.S. Department of Veterans Affairs (VA) distributed Fitbits to Veterans through four VA medical centers. Our objective was to assess the program from both Veterans' and clinicians' viewpoints. Specifically, we [...]

We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown.