医院参与型院前急救管理的实践探讨

我院是一家三级甲等医院,急诊科于2002年独立建制,经过多年的发展,建立了较完善的院前急救-院内急诊-急危重症监护病房（Emergency Intensive Care Unit,EICU）的急诊医疗服务体系（Emergency Medical Service System,EMSS）。目前急诊科发展成自主型的急诊模式,急诊水平不断提高,院前急救的模式是医院参与型院前急救模式,     

我国综合医院急诊科建设现状及要解决的问题

近20年来，我国急诊医学有了长足发展，急诊在保障人们生命和健康方面做出了巨大贡献。大部分医院的急诊科都已完成了从依赖型向支援型的转变，个别医院建立了自主型急诊科。急诊的功能定位及业务范围也日趋明确。但急诊在教育、管理、科研、职称晋升等方面的问题尚需进一步解决。     

郑州市急诊医疗服务体系的实践和探索

目的研究郑州市急诊医疗服务体系的功能和效果.方法回顾性分析2002年7月至2004年6月郑州市"120"指挥中心派急救车出诊情况和25家急救站的急诊救治情况.结果急诊出车逐渐增多,市内出车半径在5Km以内,平均8min到达现场.各种创伤是急诊出车的第一位原因.25家急救站中自主型模式一个,以内科为主的半自主型模式7个,通道型模式17个.严重多发伤可在自主型模式急救站进行确定性手术治疗,明显缩短了受伤到确定性手术的时间.结论急诊医疗服务体系包括院前急救、院内急诊抢救和重症监护治疗.布局合理的急救站为急诊病人提供了快捷的院前急救,迅速转运危重病人到自主型模式急救站可提高抢救成功率.     

急诊科发展模式与管理探讨

一、选择适合院情的急诊科模式 急诊科模式一般可分三种:(1)依赖型。只有行政管理,无独立完成医疗任务的能力,一切依赖其他科室。(2)支援型。组织落实,人员相对固定或大部分固定。能独立完成一定量的医疗任务,但专科需要帮助;(3)自主型。应由具有相当数量高技术水平的人员组成。通常主任1人,一般由外科或内科的学科带头人担任,其他应有一专多能的技术骨干5名。即普外、内科、神经外科、骨科、麻醉科各配1名。此外还应配有一定数量的低年资医师。能独立完成急诊医疗、教学和科研任务等。     

自主型模式急诊科的功能与管理

目的探讨自主型模式急诊科的功能和管理.方法回顾性分析我院急救中心2002年1月至2003年12月的临床资料,急诊出车3 284次,急诊门诊接诊48 350例次,急诊抢救660例,收住急诊病房1896例,收住其它专科病房3 998例,急诊病房住院病人手术592例,其中危重病人手术54例.结果距医院5公里以内的出诊,10分钟以内可到达现场,经现场急救后,转运途中无死亡,急诊抢救成功率92%;一般手术无死亡,危重病手术死亡7例,死亡率13%.结论自主型模式急诊科集院前急救、院内急救、确定性急诊手术、重症监护治疗为一体,有利于急救程序的管理,明显提高了危重病抢救成功率,同时也培养和发展了一支急诊医学队伍.     

大型综合医院急诊学科建设和发展的相关思考

就急诊医学与急诊科的相互关系、急诊科在大型综合医院发展中的地位、急诊学科建设中的困难及可持续发展方向等问题进行了深入剖析,认为大型综合医院急诊学科建设是构建城市医疗服务体系的重要环节,选择合适的运作模式、创建和维护自主团队、持续优化流程以及医疗、科研、教学并重等方面对学科建设和发展影响重大.急诊学科必将成为大型综合医院...     

基层医院急诊建设及管理的探讨

本文探讨基层医院急诊建设、急诊管理及急救人才培养策略。从不同方面阐述了基层医院急诊科目前存在的诸多问题：如没有固定编制,开展工作以“完全依赖型”为主,劳动强度大、待遇低、纠纷多、易受暴力威胁,职称晋升困难,人员流动性大等。针对这些问题,提出了切实可行的解决办法：基层医院建立“支援型”急诊科,以现代急诊医学新的“三环理论”指导急诊日常工作,积极开展院前急救,加强院内急救力量,建立急诊重症监护病房,千方百计拓展和延伸急诊服务,方便患者;建立健全规章制度,严格落实,确保安全;加强人才培养,提高职业素质,建设全科医学模式急诊科,提高抢救成功率,增加社会效益。为基层医院急诊建设及管理提供经验。     

发展急诊专业探索新型急诊模式

发展急诊专业探索新型急诊模式马克吉黄建群作者单位：１１０００１沈阳市，中国医科大学附一院一、急诊医学领域亟待解决的课题“急诊不急”是当前的一大问题，是群众关心的焦点，也是医患纠纷的“多发区”。由于各种原因，我院的急诊科一直是医院医疗工作中的薄弱环节，...     

重视急诊医学专业科室的建设与发展

为探讨医院急诊医学专业科室的建设与发展,本阐述了提高对急诊专业的认识;急诊科医疗模式的转变;急诊专业人员的培养;急诊服务观念的更新;加强医院急诊科建设。为急诊医学及其科室的建设与发展提供经验。     

加强急诊科管理 提高急救医疗质量

加强急诊科管理提高急救医疗质量万建华李继光作者单位：１１０００１沈阳市，中国医科大学附属第一医院随着医疗体制的改革，医院内部医疗结构的调整，急诊模式的转变也势在必行。为适应现代急诊急救医学的发展，更好地满足急诊患者的医疗要求，我们在改变急诊模式方面做...     

Modeling overdispersed longitudinal binary data using a combined beta and normal random-effects model

Background

In medical and biomedical areas, binary and binomial outcomes are very common. Such data are often collected longitudinally from a given subject repeatedly overtime, which result in clustering of the observations within subjects, leading to correlation, on the one hand. The repeated binary outcomes from a given subject, on the other hand, constitute a binomial outcome, where the prescribed mean-variance relationship is often violated, leading to the so-called overdispersion.

Methods

Two longitudinal binary data sets, collected in south western Ethiopia: the Jimma infant growth study, where the child’s early growth is studied, and the Jimma longitudinal family survey of youth where the adolescent’s school attendance is studied over time, are considered. A new model which combines both overdispersion, and correlation simultaneously, also known as the combined model is applied. In addition, the commonly used methods for binary and binomial data, such as the simple logistic, which accounts neither for the overdispersion nor the correlation, the beta-binomial model, and the logistic-normal model, which accommodate only for the overdispersion, and correlation, respectively, are also considered for comparison purpose. As an alternative estimation technique, a Bayesian implementation of the combined model is also presented.

Results

The combined model results in model improvement in fit, and hence the preferred one, based on likelihood comparison, and DIC criterion. Further, the two estimation approaches result in fairly similar parameter estimates and inferences in both of our case studies. Early initiation of breastfeeding has a protective effect against the risk of overweight in late infancy (p = 0.001), while proportion of overweight seems to be invariant among males and females overtime (p = 0.66). Gender is significantly associated with school attendance, where girls have a lower rate of attendance (p = 0.001) as compared to boys.

Conclusion

We applied a flexible modeling framework to analyze binary and binomial longitudinal data. Instead of accounting for overdispersion, and correlation separately, both can be accommodated simultaneously, by allowing two separate sets of the beta, and the normal random effects at once.     

固定效应模型与混合效应模型在测量误差估计中的应用

探讨疾病危险因素暴露剂量的测量误差评价方式，以云南锡业公司所属锡矿井下作业环境有害物质监测资料为例，分别进行固定效应和混合效应的方差成分模型拟合，估计资料的测量误差大小，固定疚伯方差分析结果显示氡、氡子体和粉尘的测量信度均接近１，混合效应模型也显示资料的测量质量较佳（信度系数估计值均〉０．８）。提示固定效应模型和混合效应模型均可应用于测量质量的评价，应用根据资料的特性选择正确的统计模型，以得到较为     

面向卫生决策支持的模型库设计与构建

从卫生决策需求出发,设计了面向卫生决策支持的模型库系统。首先分析了卫生决策对模型库建设的需求,根据对当前模型库建设思路的调研,提出了卫生决策模型的选择原则和模型种类,制定了面向对象的卫生决策模型库设计方案,并对卫生决策模型库的调用过程和应用领域分别进行阐述。     

用季节自回归模型SAR（p）预测药品销售量

对于具有季节变动的时间序列,使用自回归模型进行预测是不适宜的。作此采用了季节自回归模型,对黑龙江省药品销售量进行预测,获得了满意的效果,此模型的建模和选模完全可籍助专用软件自动进行,使用非常方便。     

Semiparametric probit model for informative current status data

Semiparametric probit models have recently attracted some attention for regression analysis of failure time data partly due to the popularity of the normal distribution and its special features. In this paper, we discuss the fitting of such models to informative current status data, which often occur in many areas such as medical studies and whose analysis has also recently attracted a lot of attention. For inference, a sieve maximum likelihood approach is developed and the methodology is further generalized to a class of generalized semiparametric probit models. A simulation study is conducted to assess the finite sample properties of the presented approach and indicates that it works well in practical situations. An application that motivated this study is provided.     

Marginalized multilevel hurdle and zero‐inflated models for overdispersed and correlated count data with excess zeros

Count data are collected repeatedly over time in many applications, such as biology, epidemiology, and public health. Such data are often characterized by the following three features. First, correlation due to the repeated measures is usually accounted for using subject‐specific random effects, which are assumed to be normally distributed. Second, the sample variance may exceed the mean, and hence, the theoretical mean–variance relationship is violated, leading to overdispersion. This is usually allowed for based on a hierarchical approach, combining a Poisson model with gamma distributed random effects. Third, an excess of zeros beyond what standard count distributions can predict is often handled by either the hurdle or the zero‐inflated model. A zero‐inflated model assumes two processes as sources of zeros and combines a count distribution with a discrete point mass as a mixture, while the hurdle model separately handles zero observations and positive counts, where then a truncated‐at‐zero count distribution is used for the non‐zero state. In practice, however, all these three features can appear simultaneously. Hence, a modeling framework that incorporates all three is necessary, and this presents challenges for the data analysis. Such models, when conditionally specified, will naturally have a subject‐specific interpretation. However, adopting their purposefully modified marginalized versions leads to a direct marginal or population‐averaged interpretation for parameter estimates of covariate effects, which is the primary interest in many applications. In this paper, we present a marginalized hurdle model and a marginalized zero‐inflated model for correlated and overdispersed count data with excess zero observations and then illustrate these further with two case studies. The first dataset focuses on the Anopheles mosquito density around a hydroelectric dam, while adolescents’ involvement in work, to earn money and support their families or themselves, is studied in the second example. Sub‐models, which result from omitting zero‐inflation and/or overdispersion features, are also considered for comparison's purpose. Analysis of the two datasets showed that accounting for the correlation, overdispersion, and excess zeros simultaneously resulted in a better fit to the data and, more importantly, that omission of any of them leads to incorrect marginal inference and erroneous conclusions about covariate effects. Copyright © 2014 John Wiley & Sons, Ltd.     

Multiplicative and additive models with external controls in a cohort study of cancer mortality

The use of additive and multiplicative hazard models is examined for a cohort study of 2696 women followed up for 12 years. The multiplicative model implied that women with a haemoglobin level less than 12 g/dl were at higher risk from cancer, and the additive model showed that this risk was confined to women after the menopause. Despite difficulties in fitting and in interpretation, additive model can be useful in the analysis of cohort studies.     

A spatio-temporal absorbing state model for disease and syndromic surveillance

Reliable surveillance models are an important tool in public health because they aid in mitigating disease outbreaks, identify where and when disease outbreaks occur, and predict future occurrences. Although many statistical models have been devised for surveillance purposes, none are able to simultaneously achieve the important practical goals of good sensitivity and specificity, proper use of covariate information, inclusion of spatio-temporal dynamics, and transparent support to decision-makers. In an effort to achieve these goals, this paper proposes a spatio-temporal conditional autoregressive hidden Markov model with an absorbing state. The model performs well in both a large simulation study and in an application to influenza/pneumonia fatality data.     

努力创建现代继续护理教育管理模式

邵逸夫医院自1994年起在美国护理专家的帮助和指导下,借鉴90年代美国继续护理教育管理模式,创建了一套中西方结合的继续护理教育管理模式。经过三年的实践和中西双方管理者的共同努力,取得了一定的成效。文中从继续护理教育的组织、管理、效果观察等方面进行了阐述。     

Sample size calculations for evaluating mediation

This paper presents sample size calculations for testing the mediation of the effect of a primary predictor by an intermediate variable. This problem is related to validating surrogate markers and to testing the effect of a primary predictor in the presence of confounders. For those problems, proposals for sample size calculation exist in the literature and can be adapted to the problem of mediation. Methods based on the variance inflation factor in linear regression provide exact sample size calculations for the linear model and approximations for the logistic, Poisson, and Cox models. We propose another procedure based on simulation of the underlying data structure, with applications to the logistic and Cox models. For the Poisson model, a new analytic method is also proposed. The behavior of the different proposals is investigated by means of simulation studies. Copyright © 2008 John Wiley & Sons, Ltd.