基于数据仓库的口腔医院决策支持系统的建立

目的：设计基于数据仓库的口腔医院决策支持系统,为口腔医院的日常运营管理提供决策支持.方法：由数据仓库提取医院信息系统中的数据,通过联机分析处理技术为医院决策提供有效的支持.结果：口腔医院决策支持系统为医院管理中的各种具体工作提供了分析数据,也为医院改进工作和建设发展提供了决策支持.结论：数据仓库、联机分析处理以及数据挖掘相互结合在一起可以为医院决策提供信息化支持.     

医院统计人员胜任力评价指标体系构建研究北大核心CSCD

目的构建医院统计人员胜任力评价指标体系。方法采用文献法、行为事件访谈法和核检表法收集和筛选医院统计人员胜任力评价指标,根据胜任力评价指标编制胜任力重要度调查问卷,采用SPSS13.0对问卷调查数据进行探索性因子分析,确定评价指标体系的框架结构,运用层次分析法确定医院统计人员胜任力评价指标的权重。结果构建的医院统计人员胜任力评价指标体系包括5个一级指标和24个二级指标。结论构建的医院统计人员胜任力评价指标体系概括了医院对统计人员的能力要求,可以作为提高医院统计人员胜任力的依据。     

基于商业智能的医院决策支持系统的应用

商业智能是利用数据仓库、联机分析处理和数据挖掘等核心技术,将大量的存储数据进行提取、整理、分析,为决策者进行经营决策提供支持的技术。基于商业智能的医院决策支持系统是从不同的角度抽象出反映医院管理及医疗、教育、科研业务的各类指标,集成医院运行产生的宝贵数据,全面、系统、实时地复用各类业务数据,建立相关的知识模型和知识库,满足支持决策的信息需求,实现通过信息技术辅助决策的功能。     

基于数据仓库技术维护过期数据的研究

目前处理数据的方法主要是以清除、保留为主，过期数据很少得到再利用。文章在处理过期数据的过程中．与数据仓库技术结合起来，使整个处理过程清晰，同时从多层面对历史数据进行分析，有效提高医院的数据利用率和信息处理能力，还可为医院决策者提供有效的决策支持。     

医院统计人员胜任力评价指标体系构建研究

目的构建医院统计人员胜任力评价指标体系。方法采用文献法、行为事件访谈法和核检表法收集和筛选医院统计人员胜任力评价指标,根据胜任力评价指标编制胜任力重要度调查问卷,采用SPSS13.0对问卷调查数据进行探索性因子分析,确定评价指标体系的框架结构,运用层次分析法确定医院统计人员胜任力评价指标的权重。结果构建的医院统计人员胜任力评价指标体系包括5个一级指标和24个二级指标。结论构建的医院统计人员胜任力评价指标体系概括了医院对统计人员的能力要求,可以作为提高医院统计人员胜任力的依据。     

我院统计工作滞后的原因分析

目的通过分析医院统计工作现状，使领导对统计工作有进一步的了解，为促进医院统计工作的发展提供参考。方法从源头数据质量、统计队伍人员素质、领导重视程度等3个方面进行分析。结果原始资料不准确、统计人员素质较低、领导莺视不够，都直接影响统计工作质量，影响统计报表准确性。结论为避免数据错误，应实现统计病案联网，统计人员应加强业务学习，并在院领导的支持下，更好地发挥统计工作作用。     

卫生统计工作在我院的现状分析

目的通过分析我院统计工作的现状,使领导对统计工作有进一步的了解,为促进医院统计工作的发展提供参考.方法从统计手段落后、统计人员业务能力、领导重视不够等三个方面进行分析.结果原始资料不准确、统计人员素质较低、领导重视不够,都直接影响统计工作质量,影响统计报表准确性.结论为避免数据错误,应实现统计病案联网,统计人员应加强业务学习,并在院领导的支持下,更好地发挥统计工作作用.     

医院信息智能统计分析系统设计与实现

目的开发医院信息智能统计分析系统,用于医院各信息系统集成后产生的海量数据管理,为医院管理者提供决策支持。方法利用商业智能技术整合医院业务数据,搭建数据中心;结合现代统计分析方法,建立综合查询分析平台,并在此基础上进行深度的数据分析和决策支持,使管理工作更加便捷和高效。结果整合以医院信息系统(HIS)数据库为主的医疗信息、物资管理信息、人事管理信息等资源,建立统一的报表分析中心,实现了全院信息系统的无缝链接。该系统不仅能够为医护人员工作提供数据分析依据,而且能够在此基础上构建医院绩效考评体系,进而为领导决策提供支持。结论开发一个快捷、高效、易用的智能统计分析平台,可以快速提升医院信息化管理水平。     

医院信息化技术在卫生经济管理中的作用

信息化技术是医院加强卫生经济管理研究,提高卫生经济管理水平的重要手段。利用信息化技术,可以保障网络数据的真实性与准确性,更充分利用网络数据,搭建统一数据平台,为医院的卫生经济管理提供权威、丰富、高效的决策支持数据,为医院发展打下良好基础。     

浅谈加强医院统计分析的措施

统计分析工作是医院统计工作的重要组成部分。医疗改革的不断深入，新生事物的不断涌现，医院的业务内容不断丰富。统计人员面临的问题是，如何充分利用统计资料及其他相关资料，对医院的经济活动进行分析和控制，为医院决策提供科学的依据。     

数据仓库与数据挖掘在医院信息系统中的应用

目的：从医院信息管理系统的大量数据中，发现医院运作的基本规律，预测医院发展的趋势，更好地为广大患者服务。方法：以Oracle Warehouse Builder为构建数据仓库的技术平台，利用数据挖掘工具Data Miner．对医院信息系统采用关系数据库进行建模，对数据仓库采用多维数据库模型中的星型模型进行建模。对医院中的患者结构和流动情况、费用构成、医疗工作量及经济效益等信息进行分析。结果：为医院的管理和决策提供支持。结论：数据仓库和数据挖掘在医院信息系统中有着广泛的应用，取得了明显的经济效益和管理效益。     

基于HIS数据仓库智能挖掘的决策支持系统研究

本文基于数据仓库和数据挖掘技术,结合我院实际情况和智能化管理需求进行决策支持系统的研究。通过建立功能明确、维度丰富、粒度细化、展现灵活的数据仓库,进行相关主题的联机分析（OLAP）和数据挖掘（DM）。该系统的应用能够充分利用数据仓库面向主题数据特点,很好地帮助医院进行辅助决策,实现医院的自动化质量监控和智能化临床决策管理。     

What Is the Best Way to Estimate Hospital Quality Outcomes? A Simulation Approach

Objective

To test the accuracy of alternative estimators of hospital mortality quality using a Monte Carlo simulation experiment.

Data Sources

Data are simulated to create an admission-level analytic dataset. The simulated data are validated by comparing distributional parameters (e.g., mean and standard deviation of 30-day mortality rate, hospital sample size) with the same parameters observed in Medicare data for acute myocardial infarction (AMI) inpatient admissions.

Study Design

We perform a Monte Carlo simulation experiment in which true quality is known to test the accuracy of the Observed-over-Expected estimator, the Risk Standardized Mortality Rate (RSMR), the Dimick and Staiger (DS) estimator, the Hierarchical Poisson estimator, and the Moving Average estimator using hospital 30-day mortality for AMI as the outcome. Estimator accuracy is evaluated for all hospitals and for small, medium, and large hospitals.

Data Extraction Methods

Data are simulated.

Principal Findings

Significant and substantial variation is observed in the accuracy of the tested outcome estimators. The DS estimator is the most accurate for all hospitals and for small hospitals using both accuracy criteria (root mean squared error and proportion of hospitals correctly classified into quintiles).

Conclusions

The mortality estimator currently in use by Medicare for public quality reporting, the RSMR, has been shown to be less accurate than the DS estimator, although the magnitude of the difference is not large. Pending testing and validation of our findings using current hospital data, CMS should reconsider the decision to publicly report mortality rates using the RSMR.     

Variation in Hospital Length of Stay: Do Physicians Adapt Their Length of Stay Decisions to What Is Usual in the Hospital Where They Work?

OBJECTIVE: To test the hypothesis that physicians who work in different hospitals adapt their length of stay decisions to what is usual in the hospital under consideration. DATA SOURCES: Secondary data were used, originating from the Statewide Planning and Research Cooperative System (SPARCS). SPARCS is a major management tool for assisting hospitals, agencies, and health care organizations with decision making in relation to financial planning and monitoring of inpatient and ambulatory surgery services and costs in New York state. STUDY DESIGN: Data on length of stay for surgical interventions and medical conditions (a total of seven diagnosis-related groups [DRGs]) were studied, to find out whether there is more variation between than within hospitals. Data (1999, 2000, and 2001) from all hospitals in New York state were used. The study examined physicians practicing in one hospital and physicians practicing in more than one hospital, to determine whether average length of stay differs according to the hospital of practice. Multilevel models were used to determine variation between and within hospitals. A t-test was used to test whether length of stay for patients of each multihospital physician differed from the average length of stay in each of the two hospitals. PRINCIPAL FINDINGS: There is significantly (p<.05) more variation between than within hospitals in most of the study populations. Physicians working in two hospitals had patient lengths of stay comparable with the usual practice in the hospital where the procedure was performed. The proportion of physicians working in one hospital did not have a consistent effect for all DRGs on the variation within hospitals. CONCLUSION: Physicians adapt to their colleagues or to the managerial demands of the particular hospital in which they work. The hospital and broader work environment should be taken into account when developing effective interventions to reduce variation in medical practice.     

Why do patients bypass the nearest hospital? An empirical analysis for orthopaedic care and neurosurgery in the Netherlands

Using data for 2003, we find that both for non-emergency orthopaedic care (38%) and neurosurgery (54%) numerous Dutch patients did not visit the nearest hospital. Our estimation results show that extra travel time negatively influences the probability of hospital bypassing. Good waiting time performance by the nearest hospital also significantly decreases the likelihood of a bypass decision. Patients seem to place a lower negative value on extra travel time for orthopaedic care than for neurosurgery. The valuation of shorter waiting time also varies between these two types of hospital care. A good performance of the nearest hospital on waiting time decreases the likelihood of a bypass decision most for neurosurgery. In both samples, patients are more likely to bypass the nearest hospital when it is a university medical centre or a tertiary teaching hospital. Patient attributes, such as age and social status, are also found to significantly affect hospital bypassing. From our analysis it follows that both patient and hospital care heterogeneity should be taken into account when assessing the substitutability of hospitals.     

医保决策支持系统的设计和应用

目的:利用商业智能技术对医院的医疗保险数据进行分析并辅助决策支持.方法:通过数据仓库(data warehouse,DW),数据萃取、转换、载入(extract-transform-load,ETL)工具,联机分析处理(online analytical process,O-LAP)和数据的多维度分析与展示技术,形成统一的数据视图和综合决策分析支持系统.结果:系统将对医保数据的综合分析结果以报表和仪表盘的形式进行展示.结论:决策支持系统有效地提高了医院对医保数据的查询能力,特别是分析能力,有较大的发展前景.     

DRGs and documentation: the information problem.

Diagnosis Related Groups (DRGs) are becoming a new "information standard" for hospital comparisons in Europe. The availability of countrywide uniform medical record abstracts increased largely between 1982 and 1987 among EEC countries, following the definition of the European Minimum Basic Data Set. Diagnoses could easily be compared with reference to the ICD-9-CM code, while procedures have required mapping because the national coding schemes and because of the lack of an appropriate WHO classification for procedures. Financial data remain another area that should benefit from international comparisons. Data reliability depends largely on information uses and feedback to users. The AIM program of the EEC provides a good opportunity to initiate hospital data comparisons and to begin international research on indexes of severity, intensity and quality of care. The European scale is needed to compare hospital costs and outcomes with results from the U.S.A.     

ARIMA模型在门诊人次预测中的应用

目的 探讨ARlMA模型在门诊人次预测中的应用,阐述建模过程,建立预测模型,验证模型的适用性,为医院管理决策服务.方法 数据源于HIS集成统计与管理决策支持系统门诊报表,采集范围选自1999年～2005年逐月门诊人次数据,其中1999年～2004年各月数据用于建立时间序列模型,2005年数据用于验证所建立的模型,统计软件用SPSS13.0完成.结果 通过模型识别、参数估计、检验诊断、模型评价,建立ARIMA(1,0,1)(0,1,1)12模型,具有较高地拟和精度,全年门诊人次相对误差是6.84%,各月相对误差在-3.15%～9.80%之间.实际值都在预测的95%上下限范围之内.讨论 本研究验证了ARIMA模型适用于门诊人次预测,同时在预测门诊人次时也要考虑到数据量、就医环境、患者满意度等因素.     

Rating exposure control using Bayesian decision analysis

A model is presented for applying Bayesian statistical techniques to the problem of determining, from the usual limited number of exposure measurements, whether the exposure profile for a similar exposure group can be considered a Category 0, 1, 2, 3, or 4 exposure. The categories were adapted from the AIHA exposure category scheme and refer to (0) negligible or trivial exposure (i.e., the true X 0.95 < or =1%OEL), (1) highly controlled (i.e., X 0.95 < or =10%OEL), (2) well controlled (i.e., X 0.95 < or =50%OEL), (3) controlled (i.e., X 0.95 < or =100%OEL), or (4) poorly controlled (i.e., X0.95 > or =1%OEL) exposures. Unlike conventional statistical methods applied to exposure data, Bayesian statistical techniques can be adapted to explicitly take into account professional judgment or other sources of information. The analysis output consists of a distribution (i.e., set) of decision probabilities: e.g., 1%, 80%, 12%, 5%, and 2% probability that the exposure profile is a Category 0, 1, 2, 3, or 4 exposure. By inspection of these decision probabilities, rather than the often difficult to interpret point estimates (e.g., the sample 95th percentile exposure) and confidence intervals, a risk manager can be better positioned to arrive at an effective (i.e., correct) and efficient decision. Bayesian decision methods are based on the concepts of prior, likelihood, and posterior distributions of decision probabilities. The prior decision distribution represents what an industrial hygienist knows about this type of operation, using professional judgment; company, industry, or trade organization experience; historical or surrogate exposure data; or exposure modeling predictions. The likelihood decision distribution represents the decision probabilities based on an analysis of only the current data. The posterior decision distribution is derived by mathematically combining the functions underlying the prior and likelihood decision distributions, and represents the final decision probabilities. Advantages of Bayesian decision analysis include: (a) decision probabilities are easier to understand by risk managers and employees; (b) prior data, professional judgment, or modeling information can be objectively incorporated into the decision-making process; (c) decisions can be made with greater certainty; (d) the decision analysis can be constrained to a more realistic "parameter space" (i.e., the range of plausible values for the true geometric mean and geometric standard deviation); and (e) fewer measurements are necessary whenever the prior distribution is well defined and the process is fairly stable. Furthermore, Bayesian decision analysis provides an obvious feedback mechanism that can be used by an industrial hygienist to improve professional judgment. For example, if the likelihood decision distribution is inconsistent with the prior decision distribution then it is likely that either a significant process change has occurred or the industrial hygienist's initial judgment was incorrect. In either case, the industrial hygienist should readjust his judgment regarding this operation.