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Electronic health records (EHRs) have revolutionized clinical practice, but clinicians and institutions have not yet fully optimized their use. Inconsistent documentation of ophthalmic test results can increase potential medicolegal risks if providers bill for tests without properly documenting clinical interpretations.To address this, we developed and implemented a logic tool in Epic (Epic Systems, Verona, Wisconsin, United States) that prompts clinicians to document diagnostic test interpretations as discrete data before closing [...]

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 [...]

Our objective is to compare different pre-training strategies for electronic health record (EHR) foundation models.

To develop and test a novel machine learning approach for monitoring impact of computerized clinical decision support (CDS) tools on clinicians' electronic health record (EHR) activities.

Human-centered design (HCD) methods in machine learning generally focus on workflow, user interfaces, and data visualizations, but there is the potential to apply these methods to inform the model development and testing process.This study aimed to demonstrate the potential of HCD methods to support the design and testing of machine learning models developed for clinical decision-making.In preparing for formative user testing of clinician facing representations of a machine learning model [...]

The current study examined the moderating effect of self-control on the relationship between attitudes toward cybersecurity and risky online behaviors among direct care nurses.A cross-sectional study collected data from 260 direct care nurses in Saudi Arabia using a self-reported questionnaire. Hierarchical multiple regression analysis and simple slope analysis examined the moderation effect of self-control on the relationship between attitudes toward cybersecurity and risky online behaviors.We found that a better attitude [...]

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.

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.

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).

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.