Performance Monitoring and Competence Assessment in Health Services

Health and health service monitoring is among the most promising research area today and the world work towards efficient and cost effective health care. This paper deals with monitoring health service performance using more than one performance outcome variable (multi-attribute processes), which is common in most health services. Although monitoring whether a health service changes or improves over time is important this is well covered in the current literature. Therefore this paper focuses on comparing similar health services in terms of their performance. The proposed procedure is based on an appropriate control chart. The paper deals with firstly the case when no risk adjustment is required because the health services being compared treat the same patient case-mix which does not vary over time. Secondly it deals with comparing health services where risk adjustment is required because the patient case-mix they service do differ because they service either very different geographical locations or service very different demographics of the same population. The technology developed in this paper could be used for example to assess and compare health practitioners’ competence over time, i.e. to decide if two doctors are equivalent in terms of their outcome performances. The waiting time random variable associated with the run length distribution of the control charts (as well as to competence testing) is studied using a Markov Chain embedding technique. Numerical results are provided that exhibit the value of the proposed procedures.     

Simulation based analysis of patient arrival to health care systems and evaluation of an operations improvement scheme

Various demands of different patients over both medical resource and time domains in health care systems raise requests of strategies for balanced system capacity from an operations perspective. In this paper, a quantitative modeling technique with both patient arrival and associated treatment process integrated are used to characterize health care system performance and evaluate system efficiency. The patient arrival process is described as a dynamic random Poisson process and patient treatments are characterized as consumption processes of various health care resources over time with a view of the “product line” used. The waiting time of patients and usage of health care resources are proposed as system performance measures based on their means, variances, and confidence intervals. A simulation considering patients with several various diseases is given to find a mechanism of conflicting factors in decisions of balanced system capacity, and an operation scheme of “evenly balanced load for bottlenecks” is obtained based on analysis of simulation outputs. Simul8 provides the software environment for the simulation.     

A primer: Health care databases,diagnostic coding,severity adjustment systems and improved parameter estimation

Data on outcomes of medical care are becoming much more available in health care organizations and systems of care. This will create new opportunities for operations researchers to make contributions to health care policy and management. To provide some background to those new to the health care area, in this article we do the following: (1) provide a brief exposure to major administrative databases that are available and useful for analyzing outcomes data; (2) discuss the strengths and limitations of the diagnostic information contained in these databases; (3) describe several systems that use this information, or in some cases information from the medical record, to determine patient severity, thus providing a basis for severity-adjustment before considering outcomes; and (4) finally, provide an overview of some recent advances in obtaining improved parameter estimates from large databases.     

The efficiency of CUSUM schemes for monitoring the coefficient of variation

Continuous surveillance of the coefficient of variation is a quality control issue worthy of consideration in several manufacturing and service‐oriented companies. In this paper, we present a new method to monitor the squared coefficient of variation by means of two one‐sided cumulative sum‐type control charts. We study the run length properties of the proposed charts using a Markov chain approach. Several tables are given in order to show the sensitivity of the proposed charts for different deterministic shift sizes and their performance for the random shift size condition. The results show that the proposed control charts have attractive performance compared with some competing charts and are better in many cases. An illustrative example is discussed on a real dataset. Copyright © 2016 John Wiley & Sons, Ltd.     

Comparison of control charts for Poisson count data in health‐care monitoring

Statistical surveillance is a noteworthy endeavor in many health‐care areas such as epidemiology, hospital quality, infection control, and patient safety. For monitoring hospital adverse events, the Shewhart u‐control chart is the most used methodology. One possible issue of the u‐chart is that in health‐care applications the lower control limit (LCL) is often conventionally set to zero as the adverse events are rare and the sample sizes are not sufficiently large to obtain LCL greater than zero. Consequently, the control chart loses any ability to signal improvements. Furthermore, as the area of opportunity (sample size) is not constant over time, the in‐control and out‐of‐control run length performances of the monitoring scheme are unknown. In this article, on the basis of a real case and through an intensive simulation study, we first investigate the in‐control statistical properties of the u‐chart. Then we set up several alternative monitoring schemes with the same in‐control performances and their out‐of‐control properties are studied and compared. The aim is to identify the most suitable control chart considering jointly: the ability to detect unexpected changes (usually worsening), the ability to test the impact of interventions (usually improvements), and the ease of use and clarity of interpretation. The results indicate that the exponentially weighted moving average control chart derived under the framework of weighted likelihood ratio test has the best overall performance.     

Distribution-free monitoring of univariate processes

We consider statistical process control (SPC) of univariate processes when observed data are not normally distributed. Most existing SPC procedures are based on the normality assumption. In the literature, it has been demonstrated that their performance is unreliable in cases when they are used for monitoring non-normal processes. To overcome this limitation, we propose two SPC control charts for applications when the process data are not normal, and compare them with the traditional CUSUM chart and two recent distribution-free control charts. Some empirical guidelines are provided for practitioners to choose a proper control chart for a specific application with non-normal data.     

Using phase-type models to cost stroke patient care across health,social and community services

Stroke disease places a heavy burden on society, incurring long periods of time in hospital and community care, and associated costs. Also stroke is a highly complex disease with diverse outcomes and multiple strategies for therapy and care. Previously a modeling framework has been developed which clusters patients into classes with respect to their length of stay (LOS) in hospital. Phase-type models were then used to describe patient flows for each cluster. Also multiple outcomes, such as discharge to normal residence, nursing home, or death can be permitted. We here add costs to this model and obtain the Moment Generating Function for the total cost of a system consisting of multiple transient phase-type classes with multiple absorbing states. This system represents different classes of patients in different hospital and community services states. Based on stroke patients’ data from the Belfast City Hospital, various scenarios are explored with a focus on comparing the cost of thrombolysis treatment under different regimes. The overall modeling framework characterizes the behavior of stroke patient populations, with a focus on integrated system-wide costing and planning, encompassing hospital and community services. Within this general framework we have developed models which take account of patient heterogeneity and multiple care options. Such complex strategies depend crucially on developing a deep engagement with the health care professionals and underpinning the models with detailed patient-specific data.     

Managerial Analysis and Decision-Making in Outpatient Health Clinics

This paper discusses a generalized model of an outpatient clinic which duplicates many real-life complexities, e.g. different facilities, the patient routes through the clinic, number of observers in each facility, etc. The validation tests proved that the output generates distributions which are not statistically different from the observed distributions for The Pennsylvania State Outpatient Clinic. The model is relatively fast and efficient, and one typical day can be simulated in less than one second of the computer processing time on IBM System 370/168. With slight modifications, it is capable of being transferred to many different types of health care delivery systems, e.g. hospitals, health maintenance organizations and to prehospital emergency care systems.The paper describes some basic measures of effectiveness for outpatient clinics, and using these measures evaluates various operating procedures and policies.     

Distribution‐free precedence control charts with improved runs‐rules

Distribution‐free (nonparametric) control charts are helpful in applications where we do not have enough information about the underlying distribution. The Shewhart precedence charts is a class of Phase I nonparametric charts for location. One of these charts, called the median precedence chart (Med chart hereafter), uses the median of the test sample as the charting statistic, whereas another chart, called the minimum precedence chart (Min chart hereafter), uses the minimum. In this paper, we first study the comparative performance of the Min and the Med charts, respectively, in terms of their in‐control and out‐of‐control run‐length properties in an extensive simulation study. It is seen that neither chart is best as each has its strength in certain situations. Next, we consider enhancing their performance by adding some supplementary runs‐rules. It is seen that the new charts present very attractive run‐length properties, that is, they outperform their competitors in many situations. A summary and some concluding remarks are given. Copyright © 2016 John Wiley & Sons, Ltd.     

Steady-state-optimal adaptive control charts based on variable sampling intervals

Variable sampling interval (VSI) control charts vary the sampling rate adaptively as a function of the data coming from the process in order to reduce the detection delay of process changes. Zero-time performance refers to the detection delay of a process change that is present during the onset of the chart at time zero. Steady-state performance refers to the detection delay of a process change that occurs after the chart has been operating for some time. The zero-time performance of a VSI control chart can differ considerably from the chart's steady-state performance, which is generally more important than the zero-time performance. We develop an efficient quadratic-programming algorithm for the construction and investigation of steady-state-optimal sampling policies for various VSI charts. We show that a steady-state-optimal VSI scheme is fundamentally different from the respective zero-time-optimal VSI scheme, and recommend VSI policies based on two sampling intervals for the various types of control charts considered.     

基于多维风险视角的政府卫生保健资源配置决策行为研究

研究在疾病风险和医学治疗风险同时共存的情形下,政府卫生保健资源的优先配置行为决策问题.当存在治疗风险(患病风险)时,分析病人患病的不确定性(治疗的不确定性)对政府卫生保健资源的配置效应,同时给出配置更多的卫生资源到更高风险病人群体的社会规划者的风险偏好条件.当这两类风险是局部的或正象限依赖的风险时,研究两种来源的风险对政府卫生保健资源的配置的联合影响.将之前学者提出的卫生保健资源的配置模型扩展到两类风险共存的情形,同时对于不确定下的卫生保健资源配置决策问题提供新的见解.     

An evaluation of the multivariate dispersion charts with estimated parameters under non‐normality

Various charts such as |S|, W, and G are used for monitoring process dispersion. Most of these charts are based on the normality assumption, while exact distribution of the control statistic is unknown, and thus limiting distribution of control statistic is employed which is applicable for large sample sizes. In practice, the normality assumption of distribution might be violated, while it is not always possible to collect large sample size. Furthermore, to use control charts in practice, the in‐control state usually has to be estimated. Such estimation has a negative effect on the performance of control chart. Non‐parametric bootstrap control charts can be considered as an alternative when the distribution is unknown or a collection of large sample size is not possible or the process parameters are estimated from a Phase I data set. In this paper, non‐parametric bootstrap multivariate control charts |S|, W, and G are introduced, and their performances are compared against Shewhart‐type control charts. The proposed method is based on bootstrapping the data used for estimating the in‐control state. Simulation results show satisfactory performance for the bootstrap control charts. Ultimately, the proposed control charts are applied to a real case study.     

A new control chart in contaminated data of t‐Student distribution for individual observations

Control charts are the most popular tool for monitoring production quality. In traditional control charts, it is usually supposed that the observations follow a multivariate normal distribution. Nevertheless, there are many practical applications where the normality assumption is not fulfilled. Furthermore, the performance of these charts in the presence of measurement errors (outliers) in the historical data has been improved using robust control charts when the observations follow a normal distribution. In this paper, we develop a new control chart for t‐Student data based on the trimmed T² control chart () through the adaptation of the elements of this chart to the case of this distribution. Simulation studies show that a control chart performs better than T² in t‐Student samples for individual observations. Copyright © 2012 John Wiley & Sons, Ltd.     

Control charts for the Pareto distribution

The Pareto distribution plays an important role in various areas of researchIn this paper, the average run length(ARL) unbiased control charts, which monitor the shape and threshold parameters of the Pareto distribution respectively, are proposed when the incontrol parameters are knownThe effects of parameter estimation on the performance of the proposed control charts are also studiedResults show that the control charts with the estimated parameters are not suitable to be used in the known parameter case, thus the ARL-unbiased control charts for the shape and threshold parameters with the desired ARL0, which consider the variability of the parameter estimates, are further developedThe performance of the proposed control charts is investigated in terms of the ARLFinally, an example is given to illustrate the proposed control charts.     

自相关过程的质量控制图

常规控制图应用的基本假设是从过程得到的测量值彼此独立,但许多连续型的制造业生产过程(例如化学和制药)往往存在自相关,此时常规控制图容易虚发警报。基于数据的样本自相关函数,本文改进了常规控制图的控制界限,使之适用于自相关过程,并运用常规X-s控制图和本文修正的X控制图对一个实际案例进行了比较分析,结果表明本文修正的X控制图可正确地判断过程是否处于受控状态。     

两种不同系统因素影响下完全检验的最优设计

本文研究了两种不同系统因素影响下完全检验的质量最优问题 ,采用有两种不同控制界限的控制图来控制生产过程 ,并根据完全检验的特点 ,提出了一种新的最优模型。 Hui(1 991 )提出的最优模型是本文的最优模型的特殊情况 ,并且本文建立的最优模型还可推广到两种以上不同的系统因素下影响的情况     

联合监测过程位置参数和尺度参数的非参数Shewhart控制图

用于检测生产服务过程的传统控制图多数都假定过程的分布是已知的。这些控制困经常是在正态分布的假设下构建的,然而在服务质量实时监控中数据往往是非正态的。在这种情况下,基于正态分布假设的控制图的结果是不可靠的。为了解决这个问题,通常考虑非参数方法,因为在过程分布未知情况下,非参数控制图比参数图更加稳健有效。本文提出一个新的基于Van der Waerden和Klotz检验的Lepage型非参数Shewhart控制图(称为LPN图)用于同时检测未知连续过程分布的位置参数和尺度参数。文中给出了LPN图在不同参数下的控制限。依据运行长度分布的均值,方差和分位数,分析了LPN图在过程受控和失控时的性能,并与其他一些现有的非参数控制图进行比较。基于蒙特卡洛的模拟结果表明,LPN图对非正态分布具有很好的稳健性,并且在不同的过程分布下对检测位置参数和尺度参数,尤其对检测尺度参数的漂移都具有很好的性能。最后通过监控出租车服务质量说明LPN图在实际中的应用。     

固定时间域可变采样间隔的统计控制图

本文从提高统计控制图对过程波动检测能力和方便管理的角度出发 ,对可变采样间隔(VSI)控制图进行改进 ,提出了针对连续过程质量控制应用需要的固定时间域可变采样间隔(VSIFT)控制图。文章详细介绍了VSIFT均值极差控制图、VSIFTEWMA控制图的设计 ,并分别评价了它们对过程异常状态的检测能力     

Application of discrete-event simulation in health care clinics: A survey

In recent decades, health care costs have dramatically increased, while health care organisations have been under severe pressure to provide improved quality health care for their patients. Several health care administrators have used discrete-event simulation as an effective tool for allocating scarce resources to improve patient flow, while minimising health care delivery costs and increasing patient satisfaction. The rapid growth in simulation software technology has created numerous new application opportunities, including more sophisticated implementations, as well as combining optimisation and simulation for complex integrated facilities. This paper surveys the application of discrete-event simulation modeling to health care clinics and systems of clinics (for example, hospitals, outpatient clinics, emergency departments, and pharmacies). Future directions of research and applications are also discussed.     

Variable sampling interval Shewhart control charts for monitoring the multivariate coefficient of variation

In many industrial manufacturing processes, the ratio of the variance to the mean of a quantity of interest is an important characteristic to ensure the quality of the processes. This ratio is called the coefficient of variation (CV). A lot of control charts have been designed for monitoring the CV of univariate quantity in the literature. However, the CV control charts for multivariate quantity have not received much attention yet. In this paper, we investigate a variable sampling interval (VSI) Shewhart control chart for monitoring multivariate CV. The time between two consecutive samples is allowed to vary according to the previous value of the multivariate CV, which will help the chart to detect the process shifts faster. The comparison with the fixed sampling interval Shewhart chart is implemented to highlight the advantage of the VSI method. Finally, an illustrative example is demonstrated on real data.