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Background Sepsis remains a leading cause of death for burn patients, yet the condition is hard to spot early. Hospitals generally rely on International Classification of Diseases (ICD) codes for surveillance, but these codes are assigned late and often miss active cases. Objectives We developed and validated a scalable, electronic health record (EHR)-based digital phenotype that improves identification of burn-related sepsis compared with ICD codes alone. Methods We performed a [...]

This study evaluated the usability of EzList, a Brazilian digital platform developed to optimize elective surgical waitlist management.

Many institutions have reported poor mapping of laboratory results to Logical Observation Identifiers Names and Codes (LOINC). A human in the loop machine learning approach for clinical terminology management may help.

The coronavirus disease 2019 (COVID-19) pandemic spurred a tremendous increase in the adoption and use of remote patient monitoring (RPM) for hypertension (HTN) management. However, limited evidence exists on the associations between frequency of utilization and uncontrolled blood pressure (BP).

Hospital supply rooms are dense, vertically complex environments where clinicians may lose time locating items. Existing text-based inventory logs require mental translation from alphanumeric codes to physical space, adding cognitive load during time-sensitive tasks. Despite growing use of digital twins in health care, this technology has been underreported in supply room navigation.

This study evaluates the effect of speech recognition software Dragon Medical One (DMO) on physician documentation burden and EHR workflows using Epic Signal usage metrics.

To support the artificial intelligence (AI) lifecycle in an integrated academic health system, we implemented a modular monitoring system to oversee electronic health record (EHR) vendor-provided clinical predictive models. This case study describes the lessons learned using this modular system to support sustainable oversight of the deployed predictive models across their lifecycle.

Admission history and physical (H&P) notes influence inpatient documentation, risk adjustment, and reimbursement. In high-acuity settings, time constraints and fragmented chart data contribute to under-documentation of comorbidities. Generative AI systems that draft admission notes from existing electronic health record data may improve documentation completeness, but real-world inpatient performance remains insufficiently characterized.

Ensuring data quality and maintaining remote accessibility remain key challenges in clinical trials conducted in low-resource settings. Although several software platforms and digital tools attempt to address these challenges, no single, comprehensive solution adequately meets the complex demands of decentralized and intermittently connected trial environments. Consequently, context-specific tools must be developed and adapted to ensure reliable performance under such constraints.