Early Prediction Of Hospital Referral Need From Urgent Care Clinics Using Non-Laboratory Clinical And Nursing Indicators: A Systematic Review

Abstract

Background: Early identification of patients requiring hospital referral from urgent care clinics is essential to reduce preventable deterioration and optimize patient flow. While laboratory investigations provide diagnostic precision, they are not always available in time-sensitive or resource-limited settings.

Objectives: This systematic review aimed to evaluate existing evidence on the predictive accuracy of non-laboratory clinical and nursing indicators—including vital signs, triage scores, and nursing judgment—for early hospital referral and admission prediction across urgent and emergency care settings.

Methods: Eleven peer-reviewed studies (2008–2026) were reviewed following PRISMA 2020 guidelines. Eligible studies included observational, interventional, and AI-based models using non-laboratory variables to predict deterioration or referral outcomes. Data were synthesized narratively due to methodological heterogeneity.

Results: Across included studies, predictive accuracy (AUC) ranged from 0.76 to 0.93. Early warning systems (NEWS2, MEWS) demonstrated strong performance in prehospital and ED cohorts (Martin-Rodriguez et al., 2019; Alam et al., 2015). AI-enhanced telemedicine models (Luque-Reigal et al., 2026) and EMR-based algorithms (Kishore et al., 2023) achieved superior discrimination (AUC > 0.9). Simplified pediatric and geriatric triage tools showed moderate reliability (Vadakkeveedan et al., 2025; Guan et al., 2025). Nursing judgment and clinical gestalt improved model interpretability and referral precision (Alghamdi et al., 2023).

Conclusions: Non-laboratory indicators and clinician-assisted models offer accurate, scalable, and resource-efficient solutions for predicting hospital referral needs. Integrating these approaches into urgent care workflows can enhance early detection, reduce preventable mortality, and support value-based healthcare transformation.