Faisal Binks , Anneli Hardy , Lee A Wallis , Willem Stassen
{"title":"The variables predictive of ambulance non-conveyance of patients in the Western Cape, South Africa","authors":"Faisal Binks , Anneli Hardy , Lee A Wallis , Willem Stassen","doi":"10.1016/j.afjem.2023.09.006","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><p>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.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>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).</p><p>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)).</p></div><div><h3>Conclusion</h3><p>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.</p></div>","PeriodicalId":48515,"journal":{"name":"African Journal of Emergency Medicine","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10551619/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"African Journal of Emergency Medicine","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211419X23000496","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"EMERGENCY MEDICINE","Score":null,"Total":0}
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.