门诊患者医疗服务感受度影响因素研究

??????? 目的 了解门诊患者对就医服务感受度影响因素,以期提高患者满意度。 方法 现场问卷调查法、专家访谈法。 结果 患者在门诊过程中最为关注的就诊流程、等候时间、就医环境、医德医风四个因素,非常看重医护人员服务规范,服务流程便捷流畅。病情不同患者对等候时间要求不同,对医院熟悉程度与就诊环境感受度不同。结论 在门诊服务中为患者提供便捷流畅、规范化的服务,同时应注重患者的不同服务需求。     

后医改时期医院后勤社会化管理存在的问题与对策

随着医改的深化与推进,医院后勤既有的供给式服务模式弊端日渐突显,旧观念与新形势的冲突引发一系列新型后勤管理问题。因此,要从根本上“治愈”医院后勤管理的顽疾,必须从转变思想开始,进而改革现有的运行机制、人才任用与培养模式和建立完整而有力的规章制度。     

探索医疗供给侧改革之路——从“互联网+”开始

中国医疗供给侧改革之路任重而道远,在当前逐步推进革新的医疗体制下,如何利用“互联网+”开启医疗供给侧改革之路,为国家13亿多人口的医疗问题提供有效的解决办法,是一项重要的改革课题。

     

教学医院急诊日夜就诊病例分析

目的 探讨急诊就诊患者在不同时段的就诊规律,为急诊科管理提供依据。方法 收集门急诊就诊信息系统2009年10月1日—2010年4月30日急诊病例资料。采用多阶段随机抽样方法,共抽取7 634份急诊病例资料。结果 急诊患者中男性多于女性,急诊儿科中位年龄3岁,急诊内科、急诊外科中位年龄32岁。就诊科别以急诊儿科患者占多数。费用类别以自费患者为主。16：00—<00：00时上夜班为就诊高峰时段,在高峰时段,急诊儿科、医疗保险患者增加较明显,但疾病轻重分级以4级及5级轻症病例为主。结论 在就诊高峰时段,要相应调整排班,加强导诊和分诊,可缓解急诊科拥挤现象。学科建设中要加强急诊儿科医师的培养。     

基于Malmquist指数的我国省际医院生产效率分析

目的 分析我国医院效率的动态变化及来源。方法 利用马奎斯特指数及分解。结果 2010—2014年间我国医院的马奎斯特指数呈上升趋势。结论 目前我国医院的全要素生产率改善主要来自纯技术效率的变化,说明我国医院逐步从以前粗放式发展阶段迈向精耕细作的新阶段,规模效率大于1,表明组建医药集团经营的可行性。     

心脏外科术后随访数据库的建立及完善

目的 构建新型、双向交互式、远程医疗随访平台,完善心脏术后临床数据的收集,并为患者提供可靠优质的随访服务。方法 运用现代通信、计算机及网络技术,开发与移动网兼容的随诊数据库及软件,以术后随访率、术后重要指标的随访质量,作为评估随访系统的标准。结果 系统正式运行1年余,共录入术后患者1392例,总随访率80.1%,其中超声心动图1065人次,心电图953人次,胸片567人次,国际标准化比值3856人次。结论 三爱医疗随访平台能有效地进行临床资料的收集,并将医疗服务延伸,给患者带来更多的帮助。     

多部门联动对抗菌药物的管理

抗菌药物的不合理使用是造成我国细菌耐药的主要原因,由于不合理用药带来的医疗费用增高是导致看病贵的原因之一。不重视抗菌药物使用的技术培训,缺乏相关专业人员的合作,公众缺乏抗菌药物使用的科学知识等是抗菌药物不合理应用的主要原因。加强抗菌药物管理,合理使用抗菌药物成为卫生行政部门及各级医疗机构迫在眉睫的任务。抗菌药物的管理需要多部门、多学科的合作,才能形成一个合理有效的管理机制。     

病种质量管理方式探讨 ——以剖宫产为例

目的 以剖宫产为例研究病种管理方式,探讨病种纳入标准。方法 采用统计学分析法,对不同指征剖宫产按照住院天数、住院费用进行分组,并对其住院费用的影响因素进行分析。结果 5组不同指征剖宫产数据根据住院费用指标可分为3组,按照住院天数指标分类则可归纳为2组,并且经分析发现,影响不同指征剖宫产术住院总费用的因素主要为药费、住院天数。结论 可以依据医疗资源消耗的相似性制定病种纳入标准,扩大病种质量控制体系范围;此外,通过控制关键影响因素来加强病种质量管理,降低患者医疗费用。     

基于德尔菲法的广东省医师多点执业实施框架构建

目的 构建符合广东省医疗卫生现状和特点的医师多点执业实施基本框架。方法 采用Delphi法对备选指标进行3轮专家咨询。结果 预调查和2轮咨询专家积极性系数分别为100%、96.67%、100%;专家的权威系数为0.70;正式两轮咨询一级指标权重权重比例接近,P＜0.005 ;正式两轮咨询专家一致性系数分别为0.161、0.191。结合专家对指标评价总分的排序和各指标重要性、可操作性变异系数,最终筛选25个指标。结论 从公立医院的角度,构建了一个较为科学、合理的医师多点执业实施框架。     

预约挂号爽约病人情况调查与对策探讨

目的 了解爽约人群的特点,分析爽约的原因,提出改进措施,提高预约诊疗服务质量。方法 通过立意抽样的方法,采用自行设计的问卷对2009年10—12月有爽约行为的人进行电话调查。 结果 爽约人群以上班族为主,且以本市居民为主,主要原因是因交通受阻。结论 建议加大社会宣传力度,增加预约者取号提示,增设退号专线方便退号,缩短爽约者预约卡停用时间等措施,降低爽约率,提高预约诊疗服务品质。     

Extraction of Pharmacokinetic Evidence of Drug–Drug Interactions from the Literature

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F1≈0.93, MCC≈0.74, iAUC≈0.99) and sentences (F1≈0.76, MCC≈0.65, iAUC≈0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.     

Drug discovery beyond the 'rule-of-five'

Although a very useful guideline for orally bioavailable small-molecule drug design, the 'rule-of-five' (also known as 'Lipinski's rule of drug-likeness') has to some extent been overemphasized. Firstly, only 51% of all FDA-approved small-molecule drugs are both used orally and comply with the 'rule-of-five'. This does not even include the increasing number of biologicals of which several have reached 'blockbuster' status. Secondly, it does not cover natural product and semisynthetic natural product drugs, which constitute over one-third of all marketed small-molecule drugs. A more balanced and programmatic approach to drug discovery should be more productive than to rely on an overemphasis of 'rule-of-five' compliance. Rather it should consider proactively the development of parenteral drugs in parallel to oral drugs and to consider the development of therapeutic antibodies in parallel to small-molecule drugs. These are particularly relevant for efforts against 'first-in-class' and/or particularly challenging targets such as proteases and those involving protein-protein interactions. In addition, more effort should be invested in natural product research. Emerging novel technologies such as synthetic biology (genetic engineering of living organisms to produce small-molecule therapeutics) may address several challenging issues of natural product-based drug discovery including synthetic feasibility and ligand efficiency.