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Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness. Guidelines recommend screening for infants with gestational age at birth < 31 weeks or birthweight ≤ 1500g. However, ensuring timely screening during readmissions after birth is challenging.

Cancer staging is integral to ensuring cancer patients receive appropriate risk adapted therapy. Discrete cancer staging using a structured staging form helps ensure accurate staging, provides a single source of truth for staging information and allows for reporting to regulatory authorities. Our institution created pediatric oncology specific discrete staging forms that have been shared with the broader Epic community. By November 2023, baseline utilization of the staging form for patients [...]

Objectives Improve concordance between patient end-of-life preferences and code status orders by incorporating data from a state registry with Clinical Decision Support (CDS) within the electronic health record (EHR) to preserve patient autonomy and ensure that patients receive care that aligns with their wishes. Methods Leveraging a Health Information exchange (HIE) interface between the New York State Medical Orders for Life-Sustaining Treatment (eMOLST) registry and the EHR of our academic [...]

Observational data have been actively used to estimate treatment effect, driven by the growing availability of electronic health records (EHRs). However, EHRs typically consist of longitudinal records, often introducing time-dependent confounding that hinder the unbiased estimation of treatment effect. Inverse probability of treatment weighting (IPTW) is a widely used propensity score method since it provides unbiased treatment effect estimation and its derivation is straightforward. In this study, we aim to [...]

Despite the recent adoption of large language models (LLMs) for biomedical information extraction (IE), challenges in prompt engineering and algorithms persist, with no dedicated software available. To address this, we developed LLM-IE: a Python package for building complete IE pipelines.

To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better compliance with the FAIR principles for data stewardship.

[This corrects the article DOI: 10.1093/jamiaopen/ooaf007.].

Recent advances in deep learning show significant potential in analyzing continuous monitoring electronic health records (EHR) data for clinical outcome prediction. We aim to develop a Transformer-based, Encounter-level Clinical Outcome (TECO) model to predict mortality in the intensive care unit (ICU) using inpatient EHR data.

The use of large language models (LLMs) is growing for both clinicians and patients. While researchers and clinicians have explored LLMs to manage patient portal messages and reduce burnout, there is less documentation about how patients use these tools to understand clinical notes and inform decision-making. This proof-of-concept study examined the reliability and accuracy of LLMs in responding to patient queries based on an open visit note.

There is limited knowledge on how providers and patients in the emergency department (ED) use electronic health records (EHRs) to facilitate the diagnostic process. While EHRs can support diagnostic decision-making, EHR features that are not user-centered may increase the likelihood of diagnostic error. We aimed to identify how EHRs facilitate or impede the diagnostic process in the ED and to identify opportunities to reduce diagnostic errors and improve care quality.