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This study aimed to develop and validate a targeted set of clinical rules embedded within a clinical decision support system to support safer antimicrobial dosing in patients with impaired renal function and to assist antimicrobial stewardship activities.

Parents of children with rare and serious illnesses often have unmet information needs. Large language models (LLMs) can help parents seek medical information. However, few studies have observed parents' use of LLMs or how they would use it in conjunction with their patient portal.

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).

Sepsis remains a leading cause of death for burn patients, yet the condition is hard to spot early. Hospitals generally rely on the International Classification of Diseases (ICD) codes for surveillance, but these codes are assigned late and often miss active cases.

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.

This study evaluated the usability of easy-list (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.

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.