门诊预约挂号的实践与效果

挂号服务是医院对外服务的前沿,挂号服务质量直接关系到医院的医疗氛围和门诊患者的满意度。门诊预约挂号解决门诊就医"三长一短"的现象[1-2]。本院结合医院实际情况和为了满足患者的需求,自2009年11月以来实施了门诊预约挂号的服务,经过1年多的实践,取得较好的效果,现将方法和结果报道如下。     

我院实行挂号-导医制的体会

我院是一所县级医院，日门诊量400余人次，门诊科室分工细，主要面向农村病人，因医院实行财务人员挂号而病人就医能力差，往往跳过导医环节，造成挂错号，排错队，找错医生，延误就医时间，形成就医难，对医院服务质量不满意。因而结合我院特点，于2005年开始实行挂号-导医制度，形成病人-导医-医生服务链条，解决了问题，收到了满意效果。现将体会介绍如下：     

提高门诊电脑挂号质量的体会

目的提高门诊电脑挂号质量,配合医院信息系统的顺利实施.方法尽快提高挂号员的技术水平,提高网络速度,增强组织协调,增设分诊台、咨询员等方法.结果挂号速度有了大幅度的提高,有效保证了服务质量.结论分析现存的不足,练就过硬的挂号技术,建立协调有力的管理组织是提高挂号质量的关键.     

门诊预约挂号信息系统的应用

门诊挂号是病人到医院就诊所必经的一个环节 ,随着互联网络的迅速发展 ,利用互联网不断扩大医院信息和医疗服务的影响力 ,成为医院服务发展的一个新方向。从预约挂号系统的特点、预约挂号流程等方面介绍电话和网上预约挂号的做法。     

不同挂号模式在口腔内科门诊应用的效果

目的 探讨口腔内科门诊不同挂号模式的优劣.方法 抽取2011年5月以来我院3种挂号模式的患者208例,其中现场挂号患者84例,预约复诊患者84例,预约初诊患者40例.比较现行3种挂号模式的候诊时间.结果 现场挂号、预约复诊、预约初诊3种挂号模式的候诊时间分别为(48.36 ±50.18),(10.12±8.68),(24.00±19.04)min,差异有统计学意义(F =27.83,P＜0.01).3种挂号模式两两比较,预约复诊和预约初诊挂号患者的候诊时间短于现场挂号的患者(t值分别为7.41,3.78;P均＜0.01),预约复诊患者的候诊时间短于预约初诊的患者(t=2.16,P＜0.05).结论 预约挂号与现场挂号模式相比,患者的候诊时间明显缩短.预约挂号是解决门诊患者“挂号时间长、交费时间长、候诊时间长、诊疗时间短”问题的有效措施.     

影响门诊挂号质量的原因及对策

目的优化门诊挂号服务流程,提高门诊服务质量和工作效率,方便患者就医。方法采用回顾性调查法,对我院近两年的门诊挂号质量缺陷进行分析,总结缺陷的原因及缺陷发生的时间分布。结果门诊挂号质量缺陷主要体现在患者、护士、医院流程等方面,缺陷主要发生在上午工作时间段。结论创新服务流程、强化人员培训、加强环境建设、合理调配人员、提供预约挂号服务等是改善门诊挂号质量的针对性措施。     

痴呆患者就医延迟现状及影响因素的研究进展

目的 对国内外痴呆患者就医延迟现状及影响因素进行综述,为今后开展痴呆患者就医相关研究提供参考,为制定促进痴呆患者就医的有效干预措施提供依据。方法 通过检索中国知网、万方、维普、PubMed、Embase、Web of Science等数据库,回顾国内外痴呆患者就医相关文献,对纳入的相关研究内容进行提取与归纳。结果 不同国家和地区的痴呆患者就医延迟的时间均较长,出现症状至就医的时间在1.77~2.40年。就医延迟的影响因素包括：痴呆类型、疾病症状、疾病相关知识及信息、病耻感、就医态度、医疗保健系统的形象、认知相关基层医疗服务资源和跨文化差异。患者年龄、家族史、受教育程度、居住状况、财务状况对就医延迟的影响尚无一致结论。结论 痴呆患者就医延迟现象严重、影响因素繁杂,未来的研究应进一步考虑因素间的相互作用关系,为构建干预方案提供理论基础。     

涉外门诊就医的现状综述

随着社会经济的发展，城市国际化进程越来越快，越来越多的人来我国学习、工作、旅游或定居，涉外门诊就医现状也逐渐成为一个新的问题，同时对国内（指大陆地区，以下均同）的医护人员也提出了更高的要求。本文就涉外门诊就诊现状进行综述，从而为改进涉外门诊医疗水平提供参考。     

专职预约队伍开展预约挂号的实施效果

目的探索建立专职预约队伍实施预约挂号服务的效果。方法成立护理专职预约队伍,进行人员培训,规范预约流程,进行广泛宣传。结果预约挂号量增加,病人爽约率下降,病人满意度提高,病人挂号、门诊预约类投诉由每月4～5起降至0起。结论专职预约队伍的建立提高了门诊护理服务质量,使挂号就诊有序、节时,满足了病人对门诊护理服务的需求。     

门诊挂号过程中存在的问题分析与对策

目的：提高挂号窗口服务质量,满足患者就诊需要,从而提高挂号质量。方法：通过抽取门诊患者满意度调查表和意见投诉本中客观反映的挂号过程中存在的问题,就存在的问题进行分析并提出相应的对策。结果：缩短了患者的排队时间,提高了分诊准确率;提高了挂号成功率;减少了纠纷与投诉的发生,患者满意度调查达到95%以上。结论：挂号质量和工作效率的提高,有利于建立良好的医患关系,对于增强医院的核心竞争力起到了重要的作用。     

TransMorph: Transformer for unsupervised medical image registration

In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationships in an image. Recently, Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications. Transformers may be a strong candidate for image registration because their substantially larger receptive field enables a more precise comprehension of the spatial correspondence between moving and fixed images. Here, we present TransMorph, a hybrid Transformer-ConvNet model for volumetric medical image registration. This paper also presents diffeomorphic and Bayesian variants of TransMorph: the diffeomorphic variants ensure the topology-preserving deformations, and the Bayesian variant produces a well-calibrated registration uncertainty estimate. We extensively validated the proposed models using 3D medical images from three applications: inter-patient and atlas-to-patient brain MRI registration and phantom-to-CT registration. The proposed models are evaluated in comparison to a variety of existing registration methods and Transformer architectures. Qualitative and quantitative results demonstrate that the proposed Transformer-based model leads to a substantial performance improvement over the baseline methods, confirming the effectiveness of Transformers for medical image registration.     

A survey of medical image registration

The purpose of this paper is to present a survey of recent (published in 1993 or later) publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods. The statistics of the classification show definite trends in the evolving registration techniques, which will be discussed. At this moment, the bulk of interesting intrinsic methods is based on either segmented points or surfaces, or on techniques endeavouring to use the full information content of the images involved.     

An algorithmic overview of surface registration techniques for medical imaging

This paper presents a literature survey of automatic 3D surface registration techniques emphasizing the mathematical and algorithmic underpinnings of the subject. The relevance of surface registration to medical imaging is that there is much useful anatomical information in the form of collected surface points which originate from complimentary modalities and which must be reconciled. Surface registration can be roughly partitioned into three issues: choice of transformation, elaboration of surface representation and similarity criterion, and matching and global optimization. The first issue concerns the assumptions made about the nature of relationships between the two modalities, e.g. whether a rigid-body assumption applies, and if not, what type and how general a relation optimally maps one modality onto the other. The second issue determines what type of information we extract from the 3D surfaces, which typically characterizes their local or global shape, and how we organize this information into a representation of the surface which will lead to improved efficiency and robustness in the last stage. The last issue pertains to how we exploit this information to estimate the transformation which best aligns local primitives in a globally consistent manner or which maximizes a measure of the similarity in global shape of two surfaces. Within this framework, this paper discusses in detail each surface registration issue and reviews the state-of-the-art among existing techniques.     

三维多模态医学图像自动配准方法研究

提出一种综合应用图像分割与互信息的医学图像自动配准方法.首先采用门限法和数学形态学方法进行预处理,再用k-means方法进行分割,之后采用基于互信息的Powell优化方法配准.将该方法用于磁共振图像(MRI)和正电子发射断层扫描(PET)临床医学图像配准,得到较满意的效果.     

Supervised quality assessment of medical image registration: Application to intra-patient CT lung registration

A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal multi-feature model, classifies local alignments into three quality categories: correct, poor or wrong alignment. We establish a reference registration error set as basis for training and testing of the method. It consists of image registrations obtained from different non-rigid registration algorithms and manually established point correspondences of automatically determined landmarks. We employ a set of different classifiers and evaluate the performance of the proposed image features based on the classification performance of corresponding single-feature classifiers. Feature selection is conducted to find an optimal subset of image features and the resulting multi-feature model is validated against the set of single-feature classifiers. We consider the setup generic, however, its application is demonstrated on 51 CT follow-up scan pairs of the lung. On this data, the proposed method performs with an overall classification accuracy of 90%.     

Nonrigid registration of 3D tensor medical data

New medical imaging modalities offering multi-valued data, such as phase contrast MRA and diffusion tensor MRI, require general representations for the development of automated algorithms. In this paper we propose a unified framework for the registration of medical volumetric multi-valued data using local matching. The paper extends the usual concept of similarity between two pieces of data to be matched, commonly used with scalar (intensity) data, to the general tensor case. Our approach to registration is based on a multiresolution scheme, where the deformation field estimated in a coarser level is propagated to provide an initial deformation in the next finer one. In each level, local matching of areas with a high degree of local structure and subsequent interpolation are performed. Consequently, we provide an algorithm to assess the amount of structure in generic multi-valued data by means of gradient and correlation computations. The interpolation step is carried out by means of the Kriging estimator, which provides a novel framework for the interpolation of sparse vector fields in medical applications. The feasibility of the approach is illustrated by results on synthetic and clinical data.     

基于配准与ASM方法的医学图像分割

目的随着医学图像数据的急剧增长,建立从医学图像中自动分割特定解剖结构的算法。方法首先,获取的脑图像体数据集通过与参考体数据集的配准,使对应层图像包含与参考数据相似的解剖结构;然后利用训练得到的统计形状模型自动定位、分割指定的解剖结构。结果实验表明这种算法能取得良好的分割结果。结论本文提出的基于互信息的图像配准和统计形状模型的分割算法,能够实现从体数据中自动定位解剖结构所在的图像位置并分割出目标结构。     

MAP MRF joint segmentation and registration of medical images

The problems of segmentation and registration are traditionally approached individually, yet the accuracy of one is of great importance in influencing the success of the other. In this paper, we aim to show that more accurate and robust results may be obtained through seeking a joint solution to these linked processes. The outlined approach applies Markov random fields in the solution of a maximum a posteriori model of segmentation and registration. The approach is applied to synthetic and real MRI data.