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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.

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.

Health-related social needs (HRSN) significantly influence health outcomes, yet healthcare organizations face persistent challenges tracking referrals to community-based organizations and interpreting referral outcomes across fragmented clinical and social care systems. Prior studies report low referral fulfillment rates, but much of this evidence derives from single organizations, manual data collection, or incentivized documentation workflows, limiting insight into how information infrastructure shapes what is observable at scale.

Patients living with dementia (PLWD) require attention to social determinants of health (SDoH), but social information is often unavailable or incomplete during care encounters, and clinicians report uncertainty about how to act on this information.

In our recent study we showed that GPT's ability to perform OPS-code extraction from operational reports was equivalent to coding neurosurgeons. In this study we aim to evaluate the effect of context enhancement on GPT-5's code extraction abilities.