Prediction of in-hospital mortality and length of stay using an early warning scoring system: clinical audit

TLDR

The study evaluated how implementing a standardised early warning scoring system affected physiological monitoring and patient outcomes for all acute admissions at entry to care. A sequential audit of 848 patients over two 11‑day periods recorded respiratory rate, oxygen saturation, temperature, blood pressure, heart rate, consciousness, mortality, length of stay, critical‑care transfer, and staff satisfaction. Documentation of vital signs improved, the score correlated with in‑hospital mortality and length of stay, mortality fell after implementation, and staff reported heightened awareness and earlier interventions, confirming the system’s predictive and documentation benefits.

Abstract

This aim of this study was to assess the impact of the introduction of a standardised early warning scoring system (SEWS) on physiological observations and patient outcomes in unselected acute admissions at point of entry to care. A sequential clinical audit was performed on 848 patients admitted to a combined medical and surgical assessment unit during two separate 11-day periods. Physiological parameters (respiratory rate, oxygen saturation, temperature, blood pressure, heart rate, and conscious level), in-hospital mortality, length of stay, transfer to critical care and staff satisfaction were documented. Documentation of these physiological parameters improved (P<0.001–0.005) with the exception of oxygen saturation (P=0.069). The admission early warning score correlated both with in-hospital mortality (P<0.001) and length of stay (P=0.001). Following the introduction of the scoring system, inpatient mortality decreased (P=0.046). Staff responding to a questionnaire indicated that the scoring system increased awareness of illness severity (80%) and prompted earlier interventions (60%). A standardised early warning scoring system improves documentation of physiological parameters, correlates with in-hospital mortality, and helps predict length of stay.