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Abandoned medication orders-those initiated but not signed-represent a potential safety risk and an indicator of electronic health record (EHR) inefficiency. This study explores inpatient medication abandonment across two large tertiary healthcare systems using different EHRs.Silent alerts were deployed to identify abandoned orders at Site 1 (June 2018-May 2019) and Site 2 (July 2020-May 2023). At Site 1, alerts triggered on all inpatient medication orders. At Site 2, alerts were part [...]

Identifying patient portals (PP) activation disparities, especially in electronic health record (EHR) activation workflows, can help facilitate equitable health care access.Our study aimed to assess whether the parent/guardian's preferred language was associated with being offered, activating, and using the PP and the methods used to offer activation codes.This retrospective cohort study examined PP offer, activation, and usage rates at a large freestanding children's hospital. Patients <12 years old with ambulatory visits from July 1, 2022, to June 30, 2023, without prior active proxy PP accounts were included. The primary independent variable was the self-reported parent/guardian preferred language (English/Spanish). Outcomes included the probability of being offered, overall and by specific offer methods, activation, and usage. Zou's modified multivariate Poisson regression models examined the association between preferred language and offer/activate/use status.Among 39,578 patients, 85.1% were patients with English as preferred language (PEPL) and 14.9% had Spanish as preferred language (PSPL). PSPL had a lower probability of being offered (adjusted relative risk ratio [aRR]: 0.65, 95% confidence interval [CI]: 0.63-0.67), activated (aRR: 0.72, 95% CI: 0.70-0.75), and used (aRR: 0.68, 95% CI: 0.65-0.72) a PP compared to PEPL. Specifically, PSPL had a lower probability of activating if ever offered via instant activation (aRR: 0.72, 95% CI: 0.69-0.75), parent/guardian with existing account (aRR: 0.73, 95% CI: 0.69-0.76), and had equal probability of activating if ever offered via letter (aRR: 0.42, 95% CI: 0.19-0.94) and clinician-assisted method (aRR: 0.99, 95% CI: 0.86-1.16), compared to PEPL.PSPL at a large, free-standing pediatric health system had a lower probability of PP offer, activation, and usage than PEPL. Activation methods were not universally effective across language groups, emphasizing the need for equitable workflow optimization. This study highlights an approach to analyzing health disparities in activation workflows to inform targeted interventions to improve equitable PP access.

The digital transformation of healthcare is reshaping care delivery among healthcare professionals, requiring nurses to develop digital competencies. These competencies are essential but often underdeveloped due to limited training and resources. Global initiatives emphasize integrating these competencies into nursing education, necessitating valid instruments to assess them.This systematic review aims to identify instruments measuring digital competence in nursing and to assess their measurement properties.This review was registered in PROSPERO (identifier: CRD42024522349) [...]

Interruptive clinical decision support (CDS) alerts are intended to standardize patient care and prevent harm. However, failures can occur even in organizations with mature CDS governance and advanced analytics. These breakdowns, marked by excessive firings, workflow disruption, and clinician dissatisfaction, can provide insights into systemic weaknesses in CDS design, testing, and monitoring processes.This study aimed to examine a CDS alert malfunction as a lens for identifying system-level gaps and propose [...]

Extracting social determinants of health (SDoHs) from medical notes depends heavily on labor-intensive annotations, which are typically task-specific, hampering reusability and limiting sharing. Here, we introduce SDoH-GPT, a novel framework leveraging few-shot learning large language models (LLMs) to automate the extraction of SDoH from unstructured text, aiming to improve both efficiency and generalizability.

Frequent premature ventricular complexes (PVCs) can lead to adverse health conditions such as cardiomyopathy. The linear correlation between PVC frequency and heart rate (as positive, negative, or neutral) on a 24-hour Holter recording has been proposed as a way to classify patients and guide treatment with beta-blockers. Our objective was to evaluate the robustness of this classification to measurement methodology, different 24-hour periods, and nonlinear dependencies of PVCs on heart [...]

We discuss challenges using computational modeling approaches for personalized prediction in clinical practice to predict treatment response for rare diseases treated by novel therapies using clinical oncology as an example context. Several challenges are discussed, including data scarcity, data sparsity, and difficulties in establishing interdisciplinary teams. Machine learning (ML), mechanistic modeling (MM), and hybrid modeling (HM) are discussed in the context of these challenges.

Electronic Health Records (EHRs) sampled from different populations can introduce unwanted biases, limit individual-level data sharing, and make the data and fitted model hardly transferable across different population groups. In this context, our main goal is to design an effective method to transfer knowledge between population groups, with computable guarantees for suitability, and that can be applied to quantify treatment disparities.

Accurately measuring patient similarity is essential for precision medicine, enabling personalized predictive modeling, disease subtyping, and individualized treatment by identifying patients with similar characteristics to an index patient. This study aims to develop an electronic health record-based patient similarity estimation framework to enhance personalized predictive modeling for Acute Kidney Injury (AKI), a complex and life-threatening condition where accurate prediction is critical for timely intervention.