The variables predictive of ambulance non-conveyance of patients in the Western Cape, South Africa

IF 1.4 4区 医学 Q3 EMERGENCY MEDICINE
Faisal Binks , Anneli Hardy , Lee A Wallis , Willem Stassen
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引用次数: 0

Abstract

Introduction

Emergency medical service (EMS) resources are limited and should be reserved for incidents of appropriate acuity. Over-triage in dispatching of EMS resources is a global problem. Analysing patients that are not transported to hospital is valuable in contributing to decision-making models/algorithms to better inform dispatching of resources. The aim is to determine variables associated with patients receiving an emergency response but result in non-conveyance to hospital.

Methods

A retrospective cross-sectional study was performed on data for the period October 2018 to September 2019. EMS records were reviewed for instances where a patient received an emergency response but the patient was not transported to hospital. Data were subjected to univariate and multivariate regression analysis to determine variables predictive of non-transport to hospital.

Results

A total of 245 954 responses were analysed, 240 730 (97.88 %) were patients that were transported to hospital and 5 224 (2.12 %) were not transported. Of all patients that received an emergency response, 203 450 (82.72 %) patients did not receive any medical interventions. Notable variables predictive of non-transport were green (OR 4.33 (95 % CI: 3.55–5.28; p<0.01)) and yellow on-scene (OR 1.95 (95 % CI: 1.60–2.37; p<0.01).

Incident types most predictive of non-transport were electrocutions (OR 4.55 (95 % CI: 1.36–15.23; p=0.014)), diabetes (OR 2.978 (95 % CI: 2.10–3.68; p<0.01)), motor vehicle accidents (OR 1.92 (95 % CI: 1.51–2.43; p<0.01)), and unresponsive patients (OR 1.98 (95 % CI: 1.54–2.55; p<0.01)). The highest treatment predictors for non-transport of patients were nebulisation (OR 1.45 (95 % CI: 1.21–1.74; p<0.01)) and the administration of glucose (OR 4.47 (95 % CI: 3.11–6.41; p<0.01)).

Conclusion

This study provided factors that predict ambulance non-conveyance to hospital. The prediction of patients not transported to hospital may aid in the development of dispatch algorithms that reduce over-triage of patients, on-scene discharge protocols, and treat and refer guidelines in EMS.

Abstract Image

Abstract Image

南非西开普省救护车不运送病人的预测变量。
简介:紧急医疗服务(EMS)资源有限,应为适当视力的事件保留。EMS资源调度中的过度分类是一个全球性问题。分析未被送往医院的患者有助于建立决策模型/算法,更好地为资源调度提供信息。目的是确定与接受紧急响应但导致无法送往医院的患者相关的变量。方法:对2018年10月至2019年9月期间的数据进行回顾性横断面研究。对EMS记录进行了审查,以防患者收到紧急响应,但患者未被送往医院。对数据进行单变量和多变量回归分析,以确定预测未被送往医院的变量。结果:共分析了245954例反应,240730例(97.88%)患者被送往医院,5224例(2.12%)患者未被送往医院。在所有接受紧急响应的患者中,203450名(82.72%)患者没有接受任何医疗干预。预测非转运的显著变量为绿色(OR 4.33(95%CI:3.55-5.28;P结论:本研究提供了预测救护车非转运到医院的因素。对未转运到医院患者的预测可能有助于开发调度算法,减少对患者的过度分诊、现场出院协议以及EMS中的治疗和转诊指南。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.40
自引率
7.70%
发文量
78
审稿时长
85 days
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