Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of hospital medicine
Gender differences in secure chat and electronic health records use among hospital-based physicians
BACKGROUND: Digital messaging within electronic health records (EHRs) is central to inpatient communication. While intended to enhance efficiency, these tools may contribute to unequal digital workloads. OBJECTIVE: To evaluate gender-based differences in EHR and secure messaging use among hospital-based physicians. METHODS: Retrospective observational study at a single academic tertiary care hospital using EHR metadata from July 2023 to June 2024. Participants included a total of 205 internal medicine and medicine-pediatrics physicians (108 senior residents, 97 faculty) serving as primary clinicians on hospitalist shifts. Measures included daily number of messages sent/received, time spent in EHR and secure chat, and hours worked, stratified by gender and role. RESULTS: Physicians worked a median of 9.4 h/day, spending 40.7% of their time in the EHR and 6.3% in secure chat. Women and men work similar hours (9.5 vs. 9.3, p = .40) but women spent more time in the EHR (248 vs. 222 min/day; p < .001) and secure chat (38 vs. 34 min, p < .001) and exchanged more daily messages (62 vs. 53 sent; 56 vs. 48 received, both p < .001). Patterns were consistent across residents and faculty. Among faculty, gender differences persisted after a new scheduling model, which reduced overall work hours. CONCLUSIONS: Women physicians engaged more in digital communication than men, despite similar hours worked, and these differences persisted after a workflow change. These findings underscore the need for equity-informed strategies that both mitigate excess burden and recognize the potential value of proactive communication in hospital settings.
3 July 2026
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Development of Machine Learning Models to Predict Candidemia in Hospitalized Adult Patients
PURPOSE: We aimed to develop a generalizable machine learning model that leverages electronic medical record (EMR) data to predict candidemia using information available on or before the date of blood culture collection. METHODS: This study included adult patients admitted to Shin Koga Hospital (April 2014-March 2022) who underwent blood cultures at least 14 days after admission. We prepared two datasets: a "Complete case dataset" (13 variables, no missing values) and a "Imputed full-variable dataset" (36 variables, single imputation). We evaluated the discriminative performance of four machine learning models, including extreme gradient boosting, random forest, least absolute shrinkage and selection operator (LASSO) logistic regression, and logistic regression using stratified 5-fold cross-validation by comparing their area under the receiver operating characteristic curve (AUROC). The model with the highest mean AUROC was selected, assessed on the entire dataset, and internally validated using 1,000 bootstrap replicates. RESULTS: A total of 919 patients were included, of whom 47 (5.1%) had candidemia. The LASSO logistic regression demonstrated the best performance and was selected as the final model. The AUROC (95% CI) was 0.880 (0.836-0.924) for the Complete case dataset and 0.913 (0.878-0.948) for the Imputed full-variable dataset. The optimism-corrected AUROCs were 0.851 and 0.876, respectively. CONCLUSION: We developed two versions of the LASSO logistic regression model using only information automatically extracted from EMR, considering real-world data availability. These models have the potential to serve as practical tools to support early diagnosis and therapeutic intervention for candidemia.
3 July 2026
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