急诊红区患者滞留状况、影响因素及对策研究

发布时间：2018-01-10 12:10

本文关键词：急诊红区患者滞留状况、影响因素及对策研究　出处：《山东大学》2016年硕士论文　论文类型：学位论文

【摘要】：研究目的：收集某三甲医院急诊红区患者临床信息,分析其滞留状况和影响因素,并针对性提出应对策略,为加快患者分流、提高急诊服务质量提供参考依据,为临床急诊医学管理者制定相应措施提供决策证据。研究方法：通过医疗机构信息系统(简称HIS)和急诊患者入抢救室评估与护理记录单,前瞻性收集2014年7月至2015年12月进入急诊红区的所有患者信息,包括就诊时间、日期、月份、性别、年龄、入院方式、主诊科室、初步诊断个数、病情、是否涉及交通事故、是否涉及多科室、分流去向、滞留时间、治疗依从性、付费方式、陪人等。用EXCEL表格建立数据库,应用SPSS 19.0软件进行统计分析,计数资料采用百分比、构成比或率表示,计量资料正态分布者采用均数±标准差表示,非正态分布者以中位数和(或)四分位数(IQR)表示。非正态分布的计量资料多组比较采用H检验(Kruskal-Wallis),两组比较采用U检验(Mann-Whitney),率的比较采用卡方检验,描述性分析患者整体滞留状况,并对不同就诊时间、不同年龄段、不同主诊科室和不同分流去向的患者滞留状况进行亚组分析。采用二分类Logistic回归方法分析影响红区患者滞留时间的相关因素,并运用Kalpan-Meier分析法绘制部分影响因素的分流曲线。研究结果：1.2014年7月至2015年12月急诊红区共收治患者7849例,滞留时间范围为(0.5-729)h(小时),中位滞留时间为2.8h,四分位滞留时间为(1.6-5.6)h,其中滞留时间大于6h的有1821例(23.2%),滞留时间大于24h的有565例(7.2%),滞留时间大于72h的有220例(2.8%)。7849例患者中,在0：01-8：00就诊的患者滞留时间最长,ERZLOS(急诊红区滞留时间)为3.1(1.7-6.3)h；年龄越大滞留时间越长,65岁的患者滞留时间最长,ERLOS为3.5(1.9-8.1)h；内科患者滞留时间最长,ERZLOS为3.3(1.7.7.1)h；自动出院的患者滞留时间最长,ERZLOS为5.1(2.0-12.0)h。2.单因素Logistic回归结果显示：就诊时间段、就诊月份、性别、年龄、120送入、初诊个数、主诊科室、付费方式、陪人、治疗依从性、病情、是否涉及多科室、分流去向等13个研究因素与滞留时间超过6h相关。3.多因素L ogistic回归结果显示：就诊时间段、就诊月份、性别、年龄、120送入、初诊个数、主诊科室、治疗依从性、病情、是否涉及多科室、分流去向等11个研究因素是导致患者滞留时间大于6h的影响因素,而付费方式与陪人不是。4.患者就诊时间、病情、治疗依从性、是否涉及多科室和分流去向是影响患者滞留时间大于6h的主要因素。其中在0：01-8：00时间段就诊的患者滞留的可能性最大；患者病情越重滞留率越高；患者治疗依从性越差滞留率越高；涉及多科室的患者滞留的可能性是不涉及多科室的5.950倍；留观的患者滞留的危险性最小,自动出院的患者滞留的危险性较大。研究结论：该家医院急诊红区患者滞留状况不容乐观,滞留时间大于6h的比例偏高,部分患者滞留时间过长。其中影响红区患者滞留时间的因素有就诊时间段、就诊月份、性别、年龄、120送入、初诊个数、主诊科室、治疗依从性、病情、是否涉及多科室、分流去向等。该院急诊科需要根据主要影响因素采取针对性措施,以加快红区患者分流。
[Abstract]:Objective: To study the clinical information collection of a hospital emergency red zone patients, analyze the factors of retention status and influence, and put forward the corresponding strategies, in order to speed up the triage of patients, to provide reference to improve the quality of emergency service, formulate corresponding measures to provide decision-making evidence for the management of clinical emergency medicine. Methods: through the information system of medical institutions (HIS) and emergency patients in resuscitation room assessment and nursing records, prospectively collected from July 2014 to December 2015 to enter the red zone emergency information for all patients, including visiting time, date, month, gender, age, admission to hospital, the main departments, the number of initial diagnosis, illness, is involved in a traffic accident, whether involving department, shunt whereabouts, retention time, treatment compliance, payment methods, to accompany people. Using EXCEL database table, using SPSS 19 software for statistical analysis, count data The percentage, constituent ratio, normal distribution measurement data are expressed by the mean and standard deviation, non normal distribution to the median and (or) four quantile (IQR). The non normal distribution of measurement data were compared with H test (Kruskal-Wallis), the two groups were compared with U test (Mann-Whitney), were compared using the chi square test, descriptive analysis with overall retention status, and the different treatment time, different ages, different departments and different main shunt to patients with retention of subgroup analysis. Using two classification Logistic regression analysis of factors related to patients with retention time of the red zone. Kalpan-Meier analysis method was used to draw curves of several factors influencing shunt. Results: 1.2014 years from July to December 2015 the emergency red zone treated 7849 cases of patients, the retention time range (0.5-729) H (H), median retention Time is 2.8h, four bit residence time (1.6-5.6) h, the residence time of more than 6h in 1821 cases (23.2%), the residence time of more than 24h in 565 cases (7.2%), the residence time of more than 72h in 220 cases (2.8%) of.7849 patients, 0:01-8:00 patients in retention time the longest, the ERZLOS (residence time of emergency red zone) was 3.1 (1.7-6.3) H; the older the retention time is longer, the 65 year old patient retention time is the longest, 3.5 ERLOS (1.9-8.1) H patients; retention time is the longest, 3.3 ERZLOS (1.7.7.1) H patients discharged from left; lag for the longest time, ERZLOS was 5.1 (2.0-12.0) the results of single factor Logistic regression showed that h.2. treatment time, treatment month, gender, age, number 120 into the newly diagnosed, attending, Department, payment, accompany, treatment compliance, illness, whether involving multiple departments, 13 factors of shunt placement and the residence time of more than 6h.3 L. Multivariate ogistic regression showed that: treatment time, treatment month, gender, age, number 120 into the newly diagnosed, diagnosis, main, treatment compliance, illness, whether involving multiple departments, 11 factors are factors leading to shunt in patients with retention time of more than 6h, and the mode of payment and people with not.4. patients, the disease, treatment compliance, whether involving multiple departments and residence time are the main factors to shunt effects were greater than 6h. The possibility of retention in the 0:01-8:00 time period of treatment of patients with the largest; patients more weight retention rate is high; the compliance of patients with worse retention rate is the high possibility involves many departments; retention of patients is 5.950 times does not involve multiple departments; retention stay patients with minimal risk retention, automatic discharge of patients greaterdanger. Conclusions: the home Hospital emergency patients with red retention is not optimistic, the residence time of more than 6h high proportion, with residence time is too long. The factors affecting the retention time of red zone with treatment time, treatment month, gender, age, number 120 into, newly diagnosed, attending the Department, treatment compliance, illness, whether involving many departments, to shunt. The hospital emergency department to take targeted measures according to the main factors, in order to speed up the red zone with shunt.

【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：R472.2

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