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To improve the identification of patients with health-related social needs (HRSNs) in the emergency department (ED), we developed and integrated a risk prediction score into an existing Fast Healthcare Interoperability Resources (FHIR)-based clinical decision support (CDS).

Acute kidney injury (AKI) is common in intensive care unit (ICU) patients and is associated with high mortality, prolonged ICU stays, and increased costs. Early prediction is crucial for timely intervention and improved outcomes. Various prediction models, including machine learning, deep learning, and dynamic prediction frameworks, have been developed, but their modeling approaches, data utilization, and clinical applicability require further investigation. This review comprehensively assesses the modeling methods, data utilization [...]

Conversational Health Agents (CHAs) are interactive systems providing healthcare services, such as assistance and diagnosis. Current CHAs, especially those utilizing Large Language Models (LLMs), primarily focus on conversation aspects. However, they offer limited agent capabilities, specifically needing more multistep problem-solving, personalized conversations, and multimodal data analysis. We aim to overcome these limitations.

To assess the effect of a digital scribe among pediatric providers on documentation time, cognitive load, burnout, and caregiver satisfaction.

We aim to leverage more comprehensive phenotypic and genotypic clinical data to enhance the treatment response predictions.

To determine whether a novel digital tool, the Community Vulnerability Compass (CVC), built using large datasets, can accurately measure neighborhood- and individual-level social determinants of health (SDOH) at scale. Existing SDOH indexes fall short of this dual requirement.

Falls are a leading cause of morbidity and mortality among older adults. Common methods for identifying fall-related ED visits within both claims and electronic health record datasets rely on diagnosis code-based definitions, which underestimate the true prevalence of falls. This study applies a natural language processing (NLP) algorithm to ED provider notes to identify patients presenting due to falls and compares the characteristics of NLP-identified cases to those identified through [...]

Data governance, the policies, and procedures for managing data, is a critical factor for secondary use of clinical data for research.

To develop and apply a reproducible methodology for evaluating generative artificial intelligence (AI) powered systems in health care, addressing the gap between theoretical evaluation frameworks and practical implementation guidance.

We used clinical decision support (CDS) to promote compliance with the 21st Century Cures Act's mandate that, with few exceptions, patients be granted timely access to their clinical notes.