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To describe the application of nudges within electronic health records (EHRs) and their effects on inpatient care delivery, and identify design features that support effective decision-making without the use of interruptive alerts.

Identifying consumer health informatics (CHI) literature is challenging. To recommend strategies to improve discoverability, we aimed to characterize controlled vocabulary and author terminology applied to a subset of CHI literature on wearable technologies.

As the real-world electronic health record (EHR) data continue to grow exponentially, novel methodologies involving artificial intelligence (AI) are becoming increasingly applied to enable efficient data-driven learning and, ultimately, to advance healthcare. Our objective is to provide readers with an understanding of evolving computational methods and help in deciding on methods to pursue.

The 21st Century Cures Act (Cures Act) information blocking regulations mandate timely patient access to their electronic health information. In most healthcare systems, this technically requires immediate electronic release of test results and clinical notes directly to patients. Patients could potentially be distressed by receiving upsetting results through an electronic portal rather than from a clinician. We present a case from 2018, several years prior to the implementation of the [...]

To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions.

Clinical encounter data are heterogeneous and vary greatly from institution to institution. These problems of variance affect interpretability and usability of clinical encounter data for analysis. These problems are magnified when multisite electronic health record (EHR) data are networked together. This article presents a novel, generalizable method for resolving encounter heterogeneity for analysis by combining related atomic encounters into composite "macrovisits."

The study tests a community- and data-driven approach to homelessness prevention. Federal policies call for efficient and equitable local responses to homelessness. However, the overwhelming demand for limited homeless assistance is challenging without empirically supported decision-making tools and raises questions of whom to serve with scarce resources.

Screening for chronic kidney disease (CKD) requires an estimated glomerular filtration rate (eGFR, mL/min/1.73 m2) from a blood sample and a proteinuria level from a urinalysis. We developed machine-learning models to detect CKD without blood collection, predicting an eGFR less than 60 (eGFR60 model) or 45 (eGFR45 model) using a urine dipstick test.

Clinical prediction models providing binary categorizations for clinical decision support require the selection of a probability threshold, or "cutpoint," to classify individuals. Existing cutpoint selection approaches typically optimize test-specific metrics, including sensitivity and specificity, but overlook the consequences of correct or incorrect classification. We introduce a new cutpoint selection approach considering downstream consequences using net monetary benefit (NMB) and through simulations compared it with alternative approaches in 2 use-cases: (i) [...]