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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.

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.

To retrieve and appraise studies of deployed artificial intelligence (AI)-based sepsis prediction algorithms using systematic methods, identify implementation barriers, enablers, and key decisions and then map these to a novel end-to-end clinical AI implementation framework.

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 [...]

We sought to develop and evaluate an electronic health record (EHR) genetic testing tracking system to address the barriers and limitations of existing spreadsheet-based workarounds.

To examine the real-world safety problems involving machine learning (ML)-enabled medical devices.

The impacts of missing data in comparative effectiveness research (CER) using electronic health records (EHRs) may vary depending on the type and pattern of missing data. In this study, we aimed to quantify these impacts and compare the performance of different imputation methods.

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."

Severe infection can lead to organ dysfunction and sepsis. Identifying subphenotypes of infected patients is essential for personalized management. It is unknown how different time series clustering algorithms compare in identifying these subphenotypes.