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Health care institutions have their own "picklist" for clinicians to document adverse drug reactions (ADRs) into the electronic health record (EHR) allergy list. Whether the lack of a nationally standardized picklist impacts clinician data entries is unknown.

Interruptive clinical decision support systems, both within and outside of electronic health records, are a resource that should be used sparingly and monitored closely. Excessive use of interruptive alerting can quickly lead to alert fatigue and decreased effectiveness and ignoring of alerts. In this review, we discuss the evidence for effective alert stewardship as well as practices and methods we have found useful to assess interruptive alert burden, reduce excessive [...]

Reduction in unnecessary services is one strategy for increasing the value of health care. Reference laboratory, or send-out, tests are associated with considerable costs. We investigated whether displaying cost and turnaround time (TAT), or time-to-result, for reference laboratory tests at the time of order entry in the electronic health record (EHR) system would impact provider ordering practices.

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.

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 [...]

Safe care of central venous access devices (CVAD) requires clinicians be able to identify key CVAD properties from insertion until safe removal. Our objective was to design and evaluate interfaces to improve CVAD documentation quality and information retrieval.

To evaluate whether a natural language processing (NLP) algorithm could be adapted to extract, with acceptable validity, markers of residential instability (ie, homelessness and housing insecurity) from electronic health records (EHRs) of 3 healthcare systems.

A key component for precision medicine is a good prediction algorithm for patients' response to treatments. We aim to implement machine learning (ML) algorithms into the response-adaptive randomization (RAR) design and improve the treatment outcomes.