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Hospitals are looking to AI and other innovative applications to help alleviate provider burden and dissatisfaction associated with clinical documentation in oncology. Ambient artificial intelligence (AI) scribes are a promising technology to address these issues. However, they generally have not been optimized for oncology. This study aimed to evaluate an ambient AI scribe application with oncology providers to determine opportunities and potential challenges.This prospective pilot study of a scribe application [...]

The global aging population is rapidly increasing, and the prevalence of age-related cognitive conditions, such as mild cognitive impairment (MCI), is becoming more common. This condition, which represents intermediate stages between normal aging and dementia, underscores the importance of early detection and timely intervention to address the growing demand for health services. Traditional cognitive assessments have limitations, such as the consistency of results, prompting the need for innovative technology-based solutions.This [...]

Major depression in adolescents is recognized as a serious psychiatric illness and a risk factor for suicide. It is important for providers to screen for depression and suicidality and have clinical decision support (CDS) for evaluation, risk assessment, and initial management. Our efforts focused on leveraging the electronic health record (EHR) to ensure that high-risk adolescents received appropriate interventions before leaving the office.This study aims to improve notification of positive [...]

Interruptive alerts can negatively impact clinical workflows and contribute to alert fatigue, provider frustration, and burnout. Given that interruptive alert overriding is a heterogeneous and recurring phenomenon, occurring across different organizational contexts with varying characteristics and circumstances, we hypothesize a pragmatic approach with multimodal interventions to address malfunctioning alert populations and maintain those contributing to better patient care.This study aimed to develop a systematic approach to screen, identify, and correct [...]

In the emergency department-initiated buprenorphine for opioid use disorder (EMBED) trial, a clinical decision support (CDS) tool had no effect on rates of buprenorphine initiation in emergency department (ED) patients with opioid use disorder. The Agency for Healthcare Research and Quality (AHRQ) recently released a CDS Performance Measure Inventory to guide data-driven CDS development and evaluation. Through partner co-design, we tailored AHRQ inventory measures to evaluate EMBED CDS performance and [...]

To address biased representation in Electronic Health Records (EHRs) across subpopulations (SPs), which leads to predictive models underperforming for underrepresented groups, we propose a framework to enhance equitable predictive performance.

Use of secure messaging through the patient portal has increased in recent years. We compile evidence of how patient-initiated secure messaging has been measured, factors associated with use, and effects on individual and organization-level outcomes.

Large language models (LLMs) have demonstrated high levels of performance in clinical information extraction compared to rule-based systems and traditional machine-learning approaches, offering scalability, contextualization, and easier deployment. However, most studies rely on proprietary models with privacy concerns and high costs, limiting accessibility. We aim to evaluate 14 publicly available open-source LLMs for extracting clinically relevant findings from free-text echocardiogram reports and examine the feasibility of their implementation in information [...]

Existing ophthalmology studies for clinical phenotypes identification in real-world datasets (RWD) rely exclusively on structured data elements (SDE). We evaluated the performance, generalizability, and fairness of multimodal ensemble models that integrate real-world SDE and free-text data compared to SDE-only models to identify patients with glaucoma.

Artificial Intelligence (AI) is transforming healthcare by improving diagnostics, treatment recommendations, and resource allocation. However, its implementation also raises ethical concerns, particularly regarding biases in AI algorithms trained on inequitable data, which may reinforce health disparities. This article introduces the AI COmmunity-based Ethical Dialogue and DEcision-making (CODE) framework to embed ethical deliberation into AI development, focusing on Electronic Health Records (EHRs).