References
Diagnostic accuracy of early warning system scores in the prehospital setting
Abstract
The use of prehospital early warning scores in ambulance services is widely endorsed to promptly identify patients at risk of clinical deterioration. Early warning scores enable clinicians to estimate risk based on clinical observations and vital signs, with higher scores indicating an elevated risk of adverse outcomes. Local healthcare systems establish threshold values for these scores to guide clinical decision-making, triage, and response, necessitating a careful balance between identifying critically unwell patients and managing the challenge of prioritisation. Given the limited evidence for optimal early warning scores in emergency department and prehospital care settings, a systematic review was carried out by
The use of prehospital early warning scores (EWS) in ambulance services settings is widely advocated, their aim being to identify patients at risk of clinical deterioration, early in their clinical course (Lindskou et al, 2023). Early warning scores allow the clinician to calculate a risk score for an individual patient (Ann and Rupert, 2012). This score is based upon their clinical observations and vital signs at the time of assessment, with the resulting score providing an indication as to their risk (Martín-Rodríguez et al, 2020). Higher scores are indicative of a higher risk of adverse outcome and deterioration, and serve to identify patients requiring an increased clinical response (Pirneskoski et al, 2019). Early warning scores can be applied across a range of conditions and may be generic in nature, although tools also exist for specific conditions such as sepsis (Maciver, 2021). Local healthcare systems set threshold values for the resultant score to guide clinical decision-making, triage, and response decisions (Goodacre et al, 2023). Care must be taken to maintain a balance, ensuring that the risks of overlooking potentially critically unwell patients are weighed against the challenge of prioritising too many patients and overwhelming healthcare systems (Goodacre et al, 2023).
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