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While most health-care providers now use electronic health records (EHRs) to document clinical care, many still treat them as digital versions of paper records. As a result, documentation often remains unstructured, with free-text entries in progress notes. This limits the potential for secondary use and analysis, as machine-learning and data analysis algorithms are more effective with structured data.

Does a Tree-of-Thought prompt and reconsideration of Isabel Pro's differential improve ChatGPT-4's accuracy; does increasing expert panel size improve ChatGPT-4's accuracy; does ChatGPT-4 produce consistent outputs in sequential requests; what is the frequency of fabricated references?

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.

Little is known about how clinical decision support (CDS) tools can support care teams in changing clinical decisions to account for patients' social risks. We piloted a suite of electronic health record (EHR)-based CDS tools designed to facilitate social risk-informed care decisions to assess how the tools were used in practice and how they could be improved.

Usage of autotext or "dotphrases" is ubiquitous among provider workflows in electronic health records (EHRs). Yet, little is known about the impact of these tools in inpatient settings and among resident physicians. We aimed to evaluate the association between autotext usage and documentation time among resident physicians in an academic medical center using the Cerner EHR.

Delirium is a severe condition, often underreported and linked to adverse outcomes such as increased mortality and prolonged hospitalization. Despite its significance, delirium prediction is often hindered by underreporting and inconsistent labeling, highlighting the need for models trained on reliably labeled data (ground truth). This review examines (i) practices for determining labels in delirium prediction models and (ii) how study designs affect label quality, aiming to identify key considerations for [...]

This work evaluated algorithmic bias in biomarkers classification using electronic pathology reports from female breast cancer cases. Bias was assessed across 5 subgroups: cancer registry, race, Hispanic ethnicity, age at diagnosis, and socioeconomic status.

The objective of the study was to determine, after medication review, the patient risk score threshold that would distinguish between stays with prescriptions triggering pharmacist intervention (PI) and stays with prescriptions not triggering PI.

Data-sharing policies are rapidly evolving toward increased data sharing. However, participants' perspectives are not well understood and could have an adverse impact on participation in research. We evaluated participants' preferences for sharing specific types of data with specific groups, and strategies to enhance trust in data-sharing practices.

At a large quaternary health system, tissue specimens were frequently sent to the microbiology laboratory with an incorrect wound culture order meant for swab specimens due to poor electronic health record (EHR) menu design. Wound cultures were also requested in chronic wound cases with a low index of suspicion for acute infection.This study aimed to present a case report on specific changes to the design of the electronic test menu [...]