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Registered nurses (RNs) regularly adapt their work to ever-changing situations but routine adaptation transforms into RN strain when service demand exceeds staff capacity and patients are at risk of missed or delayed care. Dynamic monitoring of RN strain could identify when intervention is needed, but comprehensive views of RN work demands are not readily available. Electronic care delivery tools such as nurse call systems produce ambient data that illuminate workplace [...]

Improving the usability of electronic health records (EHR) continues to be a focus of clinicians, vendors, researchers, and regulatory bodies. To understand the impact of usability redesign of an existing, site-configurable feature, we evaluated the user interface (UI) used to screen for depression, alcohol and drug misuse, fall risk, and the existence of advance directive information in ambulatory settings.

Performing high-quality surveillance for influenza-associated hospitalization (IAH) is challenging, time-consuming, and essential.

Recent health care developments include connected health interventions to improve chronic disease management and/or promote actions reducing aggravating risk factors for conditions such as cardiovascular diseases. Adherence is one of the main challenges for ensuring the correct use of connected health interventions over time.

An electronic pathway for the management of adhesive small bowel obstruction (SBO) was built and implemented on top of the electronic health record. The aims of this study are to describe the development of the electronic pathway and to report early outcomes.

With the increased usage of dashboard reporting systems to monitor and track patient panels by clinical users, developers must ensure that the information displays they produce are accurate and intuitive. When evaluating usability of a clinical dashboard among potential end users, developers oftentimes rely on methods such as questionnaires as opposed to other, more time-intensive strategies that incorporate direct observation.

Patient attribution, or the process of attributing patient-level metrics to specific providers, attempts to capture real-life provider-patient interactions (PPI). Attribution holds wide-ranging importance, particularly for outcomes in graduate medical education, but remains a challenge. We developed and validated an algorithm using EHR data to identify pediatric resident PPIs (rPPIs).

Although diagnostic error (DE) is a significant problem, it remains challenging for clinicians to identify it reliably and to recognize its contribution to the clinical trajectory of their patients. The purpose of this work was to evaluate the reliability of real-time electronic health record (EHR) reviews using a search strategy for the identification of DE as a contributor to the rapid response team (RRT) activation.

The increasing availability of molecular and clinical data of cancer patients combined with novel machine learning techniques has the potential to enhance clinical decision support, example, for assessing a patient's relapse risk. While these prediction models often produce promising results, a deployment in clinical settings is rarely pursued.