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25/Nov/2025
25/Nov/2025
DOI: 10.31744/einstein_journal/2025AO1265
Highlights ■ The sample included 555 patients from three hospitals (both public and private), representing diverse care settings. ■ Physician and software predictions showed moderate correlation with observed intensive care unit length of stay, highlighting limitations in absolute prediction accuracy. ■ Categorizing length of stay into time periods (<2, 2-5, >5 days) improved prediction accuracy to approximately 60% for both physicians and Epimed Monitor Performance®. ■ Epimed Monitor Performance® identified patients at risk of prolonged intensive care unit stay with […]
Keywords: Critical care; Data science; Databases, factual; Intensive care units; Length of stay; Prediction algorithm; Quality improvement; Quality of Health Care; Registries; Risk Factors