Fiona C Sampson, Richard Pilbery, Esther Herbert, Steve Goodacre, Fiona Bell, Rob Spaight, Andy Rosser, Peter Webster, Mark Millins, Andy Pountney, Joanne Coster, Jacqui Long, Rachel O'Hara, Alexis Foster, Jamie Miles, Janette Turner, Aimee Boyd
{"title":"哪些因素可预测救护车向急诊科发出的预先警报?对英国 3 家救护车服务机构的常规数据进行分析。","authors":"Fiona C Sampson, Richard Pilbery, Esther Herbert, Steve Goodacre, Fiona Bell, Rob Spaight, Andy Rosser, Peter Webster, Mark Millins, Andy Pountney, Joanne Coster, Jacqui Long, Rachel O'Hara, Alexis Foster, Jamie Miles, Janette Turner, Aimee Boyd","doi":"10.1101/2023.12.07.23299650","DOIUrl":null,"url":null,"abstract":"Objective\nAmbulance clinicians use pre-alert calls to advise emergency departments (EDs) of the arrival of patients requiring immediate review or intervention. Consistency of pre-alert practice is important in ensuring appropriate EDs response. We used routine data to describe pre-alert practice and explore factors affecting variation in practice.\nMethods\nWe undertook an observational study using a linked dataset incorporating 12 months' ambulance patient records, ambulance clinician data and emergency call data for three UK ambulance services. We used LASSO regression to identify candidate variables for multivariate logistic regression models to predict variation in pre-alert use, analysing clinician factors (role, experience, qualification, time of pre-alert during shift), patient factors (NEWS2 score, clinical working impression, age, sex) and hospital factors (receiving ED, ED handover delay status). Results\nFrom the dataset of 1,363,274 patients conveyed to ED, 142,795 (10.5%) were pre-alerted, of whom only a third were for conditions with clear pre-alert pathways (e.g. sepsis, STEMI, major trauma). Casemix (illness acuity score, clinical diagnostic impression) was the strongest predictor of pre-alert use but male patient gender, clinician role, receiving hospital, and hospital turnaround delay at receiving hospitals were also statistically significant predictors, after adjusting for casemix. There was no evidence of higher pre-alert rates in the final hour of shift.\nConclusions\nPre-alert decisions are determined by factors other than illness acuity and clinical diagnostic impression. Research is required to determine whether our findings are reproducible elsewhere and why non-clinical factors (e.g. patient gender) may influence pre-alert practice.","PeriodicalId":501290,"journal":{"name":"medRxiv - Emergency Medicine","volume":"6 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"What factors predict ambulance pre-alerts to the emergency department? Analysis of routine data from 3 UK ambulance services.\",\"authors\":\"Fiona C Sampson, Richard Pilbery, Esther Herbert, Steve Goodacre, Fiona Bell, Rob Spaight, Andy Rosser, Peter Webster, Mark Millins, Andy Pountney, Joanne Coster, Jacqui Long, Rachel O'Hara, Alexis Foster, Jamie Miles, Janette Turner, Aimee Boyd\",\"doi\":\"10.1101/2023.12.07.23299650\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Objective\\nAmbulance clinicians use pre-alert calls to advise emergency departments (EDs) of the arrival of patients requiring immediate review or intervention. Consistency of pre-alert practice is important in ensuring appropriate EDs response. We used routine data to describe pre-alert practice and explore factors affecting variation in practice.\\nMethods\\nWe undertook an observational study using a linked dataset incorporating 12 months' ambulance patient records, ambulance clinician data and emergency call data for three UK ambulance services. We used LASSO regression to identify candidate variables for multivariate logistic regression models to predict variation in pre-alert use, analysing clinician factors (role, experience, qualification, time of pre-alert during shift), patient factors (NEWS2 score, clinical working impression, age, sex) and hospital factors (receiving ED, ED handover delay status). Results\\nFrom the dataset of 1,363,274 patients conveyed to ED, 142,795 (10.5%) were pre-alerted, of whom only a third were for conditions with clear pre-alert pathways (e.g. sepsis, STEMI, major trauma). Casemix (illness acuity score, clinical diagnostic impression) was the strongest predictor of pre-alert use but male patient gender, clinician role, receiving hospital, and hospital turnaround delay at receiving hospitals were also statistically significant predictors, after adjusting for casemix. There was no evidence of higher pre-alert rates in the final hour of shift.\\nConclusions\\nPre-alert decisions are determined by factors other than illness acuity and clinical diagnostic impression. Research is required to determine whether our findings are reproducible elsewhere and why non-clinical factors (e.g. patient gender) may influence pre-alert practice.\",\"PeriodicalId\":501290,\"journal\":{\"name\":\"medRxiv - Emergency Medicine\",\"volume\":\"6 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"medRxiv - Emergency Medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2023.12.07.23299650\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Emergency Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2023.12.07.23299650","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
What factors predict ambulance pre-alerts to the emergency department? Analysis of routine data from 3 UK ambulance services.
Objective
Ambulance clinicians use pre-alert calls to advise emergency departments (EDs) of the arrival of patients requiring immediate review or intervention. Consistency of pre-alert practice is important in ensuring appropriate EDs response. We used routine data to describe pre-alert practice and explore factors affecting variation in practice.
Methods
We undertook an observational study using a linked dataset incorporating 12 months' ambulance patient records, ambulance clinician data and emergency call data for three UK ambulance services. We used LASSO regression to identify candidate variables for multivariate logistic regression models to predict variation in pre-alert use, analysing clinician factors (role, experience, qualification, time of pre-alert during shift), patient factors (NEWS2 score, clinical working impression, age, sex) and hospital factors (receiving ED, ED handover delay status). Results
From the dataset of 1,363,274 patients conveyed to ED, 142,795 (10.5%) were pre-alerted, of whom only a third were for conditions with clear pre-alert pathways (e.g. sepsis, STEMI, major trauma). Casemix (illness acuity score, clinical diagnostic impression) was the strongest predictor of pre-alert use but male patient gender, clinician role, receiving hospital, and hospital turnaround delay at receiving hospitals were also statistically significant predictors, after adjusting for casemix. There was no evidence of higher pre-alert rates in the final hour of shift.
Conclusions
Pre-alert decisions are determined by factors other than illness acuity and clinical diagnostic impression. Research is required to determine whether our findings are reproducible elsewhere and why non-clinical factors (e.g. patient gender) may influence pre-alert practice.