智能化的语言云预检分诊系统在急诊分诊中的应用

目的：探讨系统化预检分诊模式对急诊分诊质量的影响。方法：选取2017年1月至2018年6月期间我院急诊就诊的7282例患者作为研究对象,将其分为对照组(四级分诊模式,2017年1月至2017年9月,3641例)和观察组(系统化预检分诊模式,2017年10月至2018年6月,3641例),观察和比较两组分诊准确率,抢救成功率、死亡率、候诊意外发生率,以及患者满意度。结果：与对照组相比,观察组分诊准确率明显增高(P<0.05);与对照组相比,观察组抢救成功率显著提高,死亡率及候诊意外发生率均明显降低(P<0.05);与对照组相比,观察组患者满意度大幅改善(P<0.05)。结论：系统化预检分诊模式应用于急诊分诊过程后,分诊护士借助急诊预检快速分诊评估单,能够较为准确地评估患者病情并正确分级分诊,缩短预检分诊护士的分诊时间,提高满意度。     

网络智能信息化预检分诊系统在急诊中的应用

目的:探讨网络智能信息化预检分诊系统在急诊中的应用方法及效果。方法:随机选取2014年1月1日~2016年12月31日急诊就诊患者500例为对照组,采用传统的急诊预检分诊方法;随机选取2017年1月1日~2019年12月31日急诊就诊患者500例为观察组,采用网络智能信息化预检分诊系统进行预检分诊。比较两组分诊准确率、分诊所需时间及患者就医满意度。结果:两组分诊准确率、分诊所需时间、患者就医满意度比较差异有统计学意义(P<0.05,P<0.01)。结论:网络智能信息化预检分诊系统可有效提高患者分诊准确率,缩短分诊时间,提高就医满意度。     

儿童急诊分诊信息系统的创建与应用研究

目的:创建基于五级预检分诊标准的儿童急诊分诊信息系统,并探讨其应用效果。方法:将2017年3月—2017年5月在医院急诊科接受治疗的10704例儿童设为对照组,以传统的“一看、二问、三查”分诊法为主;将2019年6月—2019年8月在医院急诊科接受治疗的10366例儿童设为试验组,采用基于五级预检分诊标准的急诊分诊信息系统进行分诊。比较两组就诊儿童分诊时间、分诊一致性、就诊儿童家属的满意度、医护人员的满意度。结果:对比医生和护士分诊级别,对照组患儿的Kappa值是0.58,试验组达到0.98;试验组就诊儿童的分诊时间明显短于对照组(P<0.01);在医护人员满意度、就诊儿童家属满意度方面,试验组明显高于对照组(P<0.01)。结论:基于五级预检分诊标准的儿童急诊分诊信息系统有利于减少急诊分诊时间,确保分诊有效性、准确性,明显提升就诊儿童家属、医护人员的满意度。     

智能护理信息系统的标准化急诊预检分诊模式在急诊胸痛患者中的应用

目的：探讨智能护理信息系统的标准化急诊预检分诊模式在急诊胸痛患者中的应用效果。方法：选取2020年10月1日～2021年7月1日收治的50例急诊胸痛患者为研究对象,按照随机数字表法分为观察组和对照组各25例,对照组实施常规预检分诊,观察组实施智能护理信息系统的标准化急诊预检分诊模式;比较两组抢救指标(包括开放静脉通路时间、预检分诊时间、口服给药时间、首份心电图时间、床边POCT+检验报告时间)、临床消化[包括首次医疗接触到PCI介入手术球囊扩张时间(FMC-to-B)、从送到医院到接受正规治疗总时间(D-to-B)、肌钙蛋白I获得时间]、急诊工作效率[包括进门至球囊扩张(D2B)时间、入门至出院(DIDO)时间、急诊处置时间、候诊时间]、预检分诊准确率及不良反应发生情况。结果：观察组开放静脉通路时间、预检分诊时间、口服给药时间、首份心电图时间、FMC-to-B、D-to-B、D2B、DIDO、床边POCT+检验报告时间、肌钙蛋白I获得时间、急诊处置时间、候诊时间均短于对照组(P<0.05);观察组急性心肌梗死及主动脉夹层诊断正确率高于对照组(P<0.05);观察组不良反应发...     

急诊预检分诊系统的应用及效果评价

目的:采用急诊预检分诊系统对急诊患者进行预检分诊并进行效果评价。方法:采用方便抽样法抽取2012年1、4、8月每月100名共计300名来我院急诊就诊患者作为对照组,同样方法抽取2013年1、4、8月间来我院急诊就诊的300例患者作为实验组,实验组采用急诊预检分诊系统对患者进行预检分诊。对两组危重症患者的等候时间、意外发生率、患者和医护人员的满意度进行比较。结果:实验组危重患者就诊等候时间明显缩短,急诊患者或家属满意度较对照组高,差异有统计学意义(P0.05);实验组危重患者在候诊期间无一发生意外;医护人员对急诊预检分诊系统满意度较高。结论:采用急诊预检分诊系统能改善急诊就诊环境,缩短危重患者等候时间,降低危重患者在候诊过程中的意外发生率,使急诊就诊患者的满意度提高,急诊医护人员对此系统也有较高的评价,可在临床推广应用。     

改良智能化急诊分级分诊系统在急诊脑卒中患者中的应用及效果评价

目的 探讨改良智能化急诊分级分诊系统在急诊脑卒中患者中的应用及效果。方法 选取2019年3月-2021年4月我院急诊收治的400例脑卒中患者作为研究对象。根据救治时间分为对照组和观察组,各200例。对照组给予智能化急诊分级分诊系统,观察组采用改良智能化急诊分级分诊系统。比较两组急诊救治时间、并发症情况、美国国立卫生研究院卒中量表(NIHSS)评分、不良事件发生率、患者满意度。结果 观察组候诊时间、入院至溶栓治疗时间、急诊滞留时间均短于对照组(P<0.05);观察组并发症发生率为1.50%,显著低于对照组6.50%(P<0.05);观察组治疗后NIHSS评分明显低于对照组(P<0.05);观察组不良事件发生率为0.50%,显著低于对照组3.50%(P<0.05);观察组患者满意度为97.93%,显著高于对照组87.96%(P<0.05)。结论 改良智能化急诊分级分诊系统在急诊脑卒中患者中应用可显著缩短患者入院至溶栓治疗时间,降低不良事件及并发症发生率,提高患者满意度。     

基于急诊检伤与急迫度分级量表标准下急诊分诊系统在急腹症患者中的应用

目的探讨基于急诊检伤与急迫度分级量表(TTAS)标准下急诊分诊系统在急腹症患者中的应用效果。方法以TTAS为基础制定急诊分诊标准及分诊信息系统,并应用于急腹症患者中,比较TTAS实施前(2017年6月～2018年5月)和实施后(2018年6月～2019年6月)患者病情评估时间、候诊时间、救治时间、确诊准确率、救治成功率及患者满意率。结果实施后患者病情评估时间、候诊时间、救治时间短于实施前(P0.05),确诊准确率、救治成功率及患者满意率高于实施前(P0.05)。结论 TTAS标准下急诊分诊系统的应用能有效缩短急腹症患者急诊分诊时间,提高患者分诊准确率及满意率。     

急诊预检分诊系统概况与进展

急诊预检是对急诊患者进行快速分类以确定治疗或进一步处理的优先次序过程.预检工作直接关系到急诊服务质量、患者救治效果和患者对医院的满意度.     

计算机在急诊分诊中的应用

１９９４年３月开始，我院将计算机用于急诊分诊工作。根据急诊的特点，采用了模块式多层次结构，完成了输入数据，查询信息，统计工作，打印结果，修改数据，数据库索引六大功能，实现了输入速度快，查询方便灵活的目的，促进了急诊分诊工作的科学化、系统化，提高了急诊分诊的工作效率。     

标准化急诊预检系统的使用效果

目的采用标准化急诊预检系统对急诊预检进行干预并观察其效果。方法采用便利抽样法抽取2008年7-9月某院急诊就诊的300例患者作为对照组,采用常规预检系统进行预检;同法抽取2009年7-9月某院急诊就诊的300例患者作为实验组,采用标准化预检系统进行预检。以自行设计的满意度调查表对两组急诊患者进行开放式问卷调查,对其基本资料、需求内容以及满意度进行相关分析。结果实验组危重患者就诊等待时间缩短、抢救成功率提高,与对照组比较,差异有统计学意义（P〈0.01或P〈0.05）;对照组有2例患者在候诊期间发生意外事件,而实验组无一例患者在候诊期间发生意外事件。实验组患者满意度较高,与对照组比较,差异有统计学意义（均P〈0.05）。结论采用标准化的急诊预检系统进行预检使危重患者等待救治时间缩短,候诊期间意外事件发生率显著下降,明显提高了危重患者抢救成功率及急诊患者满意度。该系统可在综合医院急诊科推广应用。     

Refining Emergency Severity Index Triage Criteria

Objectives: The Emergency Severity Index (ESI) version 3 is a five‐level triage acuity scale with demonstrated reliability and validity. Patients are rated from ESI level 1 (highest acuity) to ESI level 5 (lowest acuity). Clinical experience has demonstrated two levels of ESI level 2 patients: those who require immediate intervention and those who are stable to wait for at least ten minutes. Studies have found that few patients are rated ESI level 1, and it has been suggested that revisions to the ESI might result in appropriate reclassification of some sickest level 2 patients as level 1. The purpose of this study was to identify level 2 patients who might be reclassified as level 1 patients. Methods: This was a multisite, prospective study. The authors identified ESI level 2 patients who required immediate, lifesaving intervention and calculated chi‐square statistics and odds ratios for variables that predicted which ESI level 2 patients actually received immediate intervention. Results: Immediate lifesaving interventions were provided for 117 (20.2%) of the 589 patients included in the study. Seventeen predictors of the need for immediate intervention were identified. The strongest predictor was the triage nurse's judgment of the need for immediate intervention, especially airway and medications. Conclusions: Specific clinical findings at triage for a subset of ESI level 2 patients were associated with immediate delivery of lifesaving interventions. Revisions to the ESI level 1 criteria may be beneficial.     

校正改良式早期预警系统在心胸专科急诊分诊中的应用

目的将改良式早期预警(modified early warning score,MEWS)与胸科专科预检标准相结合,构建适用于心胸专科急诊分诊的校正MEWS系统,探讨其对于心胸专科急诊预检分诊工作的影响。方法便利抽样法选取上海交通大学附属胸科医院2015年9-12月急诊就诊患者8994例为对照组,2016年1-3月急诊就诊患者9138例为观察组。对照组患者按常规的急诊分诊流程处理,观察组患者实施校正MEWS系统的评分结果进行预检及分区分级处置,比较两组患者急诊分诊时间及分诊正确率、高危胸痛患者识别率、应急处理率,医生、护士及患者满意率。结果两组患者的分诊时间、分诊正确率、有效识别高危胸痛患者、应急处理率、患者满意率经比较,观察组患者均优于对照组,差异均有统计学意义(均P0.05)。结论校正MEWS评分系统便于急诊护士快速准确地分诊,同时可有效识别胸痛高危患者,提高心胸专科急诊预检分诊工作的可操作性及准确率,有助于急诊患者在最短时间内得到规范、科学、适当、合理、及时的救治。     

Emergency Triage: Comparing a Novel Computer Triage Program with Standard Triage

Background: Emergency department (ED) triage prioritizes patients based on urgency of care; however, little previous testing of triage tools in a live ED environment has been performed. Objectives: To determine the agreement between a computer decision tool and memory‐based triage. Methods: Consecutive patients presenting to a large, urban, tertiary care ED were assessed in the usual fashion and by a blinded study nurse using a computerized decision support tool. Triage score distribution and agreement between the two triage methods were reported. A random subset of patients was selected and reviewed by a blinded expert panel as a consensus standard. Results: Over five weeks, 722 ED patients were assessed; complete data were available from 693 (96%) score pairs. Agreement between the two methods was poor (κ= 0.202; 95% confidence interval [95% CI] = 0.150 to 0.254); however, agreement improved when using weighted κ (0.360; 95% CI = 0.305 to 0.415) or “within one” level κ (0.732; 95% CI = 0.644 to 0.821). When compared with the expert panel, the nurse triage scores showed lower agreement (0.263; 95% CI = 0.133 to 0.394) than the tool (κ= 0.426; 95% CI = 0.289 to 0.564). There was a significant down‐triaging of patients when patients were triaged without the computerized tool. Admission rates also differed between the triage systems. Conclusions: There was significant discrepancy by nurses using memory‐based triage when compared with a computer tool. Triage decision support tools can mitigate this drift, which has administrative implications for EDs.     

No Relationship between Emergency Department Activity and Triage Categorization

Objective: To examine the consistency of Australasian National Triage Scale (NTS) categorization in a large hospital ED, especially in relation to daily activity.
Methods: This was a prospective, observational study of the relationship between NTS category, presenting features, and disposition in a large Australian adult ED. The "admission rate" was defined as the percentage of presentations whose dispositions were recorded as admitted to hospital, transferred to another hospital for admission, or died in the ED. A "busy" weekday was defined as one during which >140 presentations were recorded in the 24 hours from midnight. For a "busy" weekend day, a figure of 100 presentations in 24 hours was used. "Nonbusy" days were defined as those during which presentation numbers were less than or equal to these thresholds.
Results: Data describing triage and disposition were available for 94,681 presentations in the 2-year period, representing 100% of ED presentations. "Busy" weekday admission rates in the 5 triage categories were 93.2%, 67.6%, 43.6%, 15.4%, and 1.6%, respectively. "Nonbusy" weekday admission rates were 91.4%, 68.3%, 43.7%, 15.6%, and 2.0%. Weekend days had a higher admission rate in NTS category 4 and 5 patients, but none of the differences between "busy" and "nonbusy" days reached statistical significance at the 0.05 level. Admission rates varied according to the time of day of presentation and increased with age, but did not change significantly over the 2 years of the study.
Conclusions: In this ED, triage categorization according to the Australasian NTS does not vary with daily activity and has been consistent over time. Further study in other settings is required, particularly to identify variation dependent on the presenting population.     

Optimizing triage consistency in Australian emergency departments: The Emergency Triage Education Kit

Objective: The Emergency Triage Education Kit was designed to optimize consistency of triage using the Australasian Triage Scale. The present study was conducted to determine the interrater reliability of a set of scenarios for inclusion in the programme. Methods: A postal survey of 237 paper‐based triage scenarios was utilized. A quota sample of triage nurses (n = 42) rated each scenario using the Australasian Triage Scale. The scenarios were analysed for concordance and agreement. The criterion for inclusion of the scenarios in the programme was κ ≥ 0.6. Results: Data were collected during 2 April to 14 May 2007. Agreement for the set was κ = 0.412 (95% CI 0.410–0.415). Of the initial set: 92/237 (38.8%, 95% CI 32.6–45.3) showed concordance ≥70% to the modal triage category (κ = 0.632, 95% CI 0.629–0.636) and 155/237 (65.4%, 95% CI 59.3–71.5) showed concordance ≥60% to the modal triage category (κ = 0.507, 95% CI 0.504–0.510). Scenarios involving mental health and pregnancy presentations showed lower levels of agreement (κ = 0.243, 95% CI 0.237–0.249; κ = 0.319, 95% CI 0.310–0.328). Conclusion: All scenarios that showed good levels of agreement have been included in the Emergency Triage Education Kit and are recommended for testing purposes; those that showed moderate agreement have been incorporated for teaching purposes. Both scenario sets are accompanied by explanatory notes that link the decision outcome to the Australasian College for Emergency Medicine Guidelines on the Implementation of the Australasian Triage Scale. Future analysis of the scenarios is required to identify how task‐related factors influence consistency of triage.     

Reliability of Computerized Emergency Triage

Objectives: Emergency department (ED) triage prioritizes patients based on urgency of care. This study compared agreement between two blinded, independent users of a Web-based triage tool (eTRIAGE) and examined the effects of ED crowding on triage reliability.
Methods: Consecutive patients presenting to a large, urban, tertiary care ED were assessed by the duty triage nurse and an independent study nurse, both using eTRIAGE. Triage score distribution and agreement are reported. The study nurse collected data on ED activity, and agreement during different levels of ED crowding is reported. Two methods of interrater agreement were used: the linear-weighted κ and quadratic-weighted κ .
Results: A total of 575 patients were assessed over nine weeks, and complete data were available for 569 patients (99.0%). Agreement between the two nurses was moderate if using linear κ (weighted κ = 0.52; 95% confidence interval = 0.46 to 0.57) and good if using quadratic κ (weighted κ = 0.66; 95% confidence interval = 0.60 to 0.71). ED overcrowding data were available for 353 patients (62.0%). Agreement did not significantly differ with respect to periods of ambulance diversion, number of admitted inpatients occupying stretchers, number of patients in the waiting room, number of patients registered in two hours, or nurse perception of busyness.
Conclusions: This study demonstrated different agreement depending on the method used to calculate interrater reliability. Using the standard methods, it found good agreement between two independent users of a computerized triage tool. The level of agreement was not affected by various measures of ED crowding.     

Implementation of Prehospital Dispatch Protocols That Triage Low-acuity Patients to Advice-line Nurses

Abstract

Introduction. Although EMS agencies have been designed to efficiently provide medical assistance to individuals, the overuse of 9-1-1 as an alternative to primary medical care has resulted in the need for new methods to respond to this increasing demand. Our study analyzes the efficacy of classifying specific low-acuity calls that can be transferred to an advice-line nurse for further medical instruction. The objectives of our study were to analyze the impact of implementing this protocol and resultant patient feedback regarding the transfer to an advice-line nurse. Methods. We collected data for retrospective review from April 2011 to April 2012 from a single municipal EMS agency with an average annual call volume of approximately 90,000. Medical Priority Dispatch System response codes were assigned to calls based on patient acuity. Patients classified under Omega response codes were assessed for eligibility of transfer to nurse advice lines. Exclusion criteria included the following: if the call was placed by a third-party caller; if the patient refused to be transferred to the advice-line nurse; anytime the MPDS system was not used; if the patient was referred from a skilled nursing facility, school, or university nursing office, or physician's office. Telephone surveys were conducted for those patients who spoke to an advice-line nurse and did not receive an ambulance response 24 hours after calling 9-1-1 to obtain patient feedback. Results. The database included 1660 patients initially classified as Omega and eligible for transfer to an advice-line nurse. After applying the exclusion criteria, 329 (19.8%) patients were ultimately transferred to an advice-line nurse and 204 (12.3%) received no ambulance response. Of those patients who were not transported by ambulance 118 (57.8%), patients completed telephone follow-up, with 104 (88.1%) reporting the nontransport option met their health-care needs and 108 (91.5%) responding they would accept the transfer again for a similar complaint. Conclusion. We identified an average of two patients per day as eligible for transfer to the nurse advice line, with less than one patient successfully completing the Omega protocol per day. While impact was limited, there was a decrease in ambulance response.