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.     

Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: Evidence from a U.S.bank application

Benchmarking is a widely cited method to identify and adopt best-practices as a means to improve performance. Data envelopment analysis (DEA) has been demonstrated to be a powerful benchmarking methodology for situations where multiple inputs and outputs need to be assessed to identify best-practices and improve productivity in organizations. Most DEA benchmarking studies have excluded quality, even in service-sector applications such as health care where quality is a key element of performance. This limits the practical value of DEA in organizations where maintaining and improving service quality is critical to achieving performance objectives. In this paper, alternative methods incorporating quality in DEA benchmarking are demonstrated and evaluated. It is shown that simply treating the quality measures as DEA outputs does not help in discriminating the performance. Thus, the current study presents a new, more sensitive, quality-adjusted DEA (Q-DEA), which effectively deals with quality measures in benchmarking. We report the results of applying Q-DEA to a U.S. bank's 200-branch network that required a method for benchmarking to help manage operating costs and service quality. Q-DEA findings helped the bank achieve cost savings and improved operations while preserving service quality, a dimension critical to its mission. New insights about ways to improve branch operations based on the best-practice (high-quality low-cost) benchmarks identified with Q-DEA are also described in the paper. This demonstrates the practical need and potential benefits of Q-DEA and its efficacy in one application, and also suggests the need for further research on measuring and incorporating quality into DEA benchmarking. The review process of this paper was handled by the Edit-in-Chief Peter Hammer.     

Competition among parties and power: an empirical analysis

This study develops a bi-objective method for burn-in decision makings with a view to achieving an optimal trade-off between the cost and the performance measures. Under the proposed method, a manufacturer specifies the relative importance between the cost and the performance measures. Then a single-objective optimal solution can be obtained through optimizing the weighted combination of these two measures. Based on this method, we build a specific model when the performance objective is the survival probability given a mission time. We prove that the optimal burn-in duration is decreasing in the weight assigned to the normalized cost. Then, we develop an algorithm to populate the Pareto frontier in case the manufacturer has no idea about the relative weight.     

Choice of modelling technique for evaluating health care interventions

Economic evaluation, such as cost effectiveness analysis, provides a method for comparing healthcare interventions. These evaluations often use modelling techniques such as decision trees, Markov processes and discrete event simulations (DES). With the aid of examples from coronary heart disease, the use of these techniques in different health care situations is discussed. Guidelines for the choice of modelling technique are developed according to the characteristics of the health care intervention.The choice of modelling technique is shown to depend on the acceptance of the modelling technique, model ‘error’, model appropriateness, dimensionality and ease and speed of model development. Generally decision trees are suitable for acute interventions but they cannot model recursion and Markov models are suitable for simple chronic interventions. It is further recommended that population based models be used in order to provide health care outcomes for the likely cost, health benefits and cost effectiveness of the intervention. The population approach will complicate the construction of the model. DES will allow the modeller to construct more complex, dynamic and accurate systems but these may involve a corresponding increase in development time and expense. The modeller will need to make a judgement on the necessary complexity of the model in terms of interaction of individuals and model size and whether queuing for resources, resource constraints or the interactions between individuals are significant issues in the health care system.     

Using a model of the performance measures in Soft Systems Methodology (SSM) to take action: a case study in health care

This paper uses a case study of a multidisciplinary colorectal cancer team in health care to explain how a model of performance measures can lead to debate and action in Soft System Methodology (SSM). This study gives a greater emphasis and role to the performance measures than currently given in typical SSM studies. Currently, the concept of performance measurement in SSM is in the form of a set of criteria used to judge the performance of the SSM model (the purposeful activity model). These performance criteria are definitions of efficacy, efficiency, effectiveness, ethicality and elegance. However, the use of performance measures within SSM is not clear and therefore criticized by some as nebulous. This paper uses a case study to explain how to involve the stakeholders in deriving a performance measurement model (PMM), which is a more detailed expansion of the performance criteria. The paper concludes with some reflections about how the PMM can fit in the SSM cycle, with two modes of practice put forward.     

Benchmarking physician practice patterns with DEA: A multi-stage approach for cost containment

Physician practice patterns in a Health Maintenance Organization (HMO) are analyzed using single and multi-stage applications of Data Envelopment Analysis (DEA). Best practice (BP) patterns are identified, which can serve as benchmark targets for inefficient physicians. Results suggest three health policy — resource utilization control strategies:

1. If managed care organizations could motivate primary care physicians to adopt the practice styles of the best practice primary care physicians, substantial clinical resource savings could be achieved, ranging from 12% to over 30% in the HMO that is the focus of this study;
2. some specialists who practice as primary care physicians (PCPs) provide more efficient care than some general practitioner PCPs, modifying the current perception that reducing specialists is the most effective way to achieve low cost practice patterns; and
3. groups of physicians in the HMO exhibit different resource use patterns, which may present opportunities to manage high cost groups as another path to contain costs.

The results suggest specific new paths which may prove effective at reducing health care costs within managed care organizations, the health care providers most likely to dominate the U.S. health system in the future. A multi-stage DEA technique is used to locate specific types of inefficient physicians. Methods to test the clinical viability of using DEA to realize the potential cost savings and extensions of this research are discussed.     

Practical guide for improving performance

Research has established that health care is not error free. The question facing perioperative units, as well as all health care services, is how to minimize the human factors that impact quality and safety. This two-part series on performance improvement in perioperative services was intended to help answer this question. Achieving performance excellence starts with a supportive work culture. The human issues of teamwork, communication, and leadership are crucial to achieving performance excellence. Next, perioperative caregivers must accept that all people make mistakes so systems and processes can be designed to be more "forgiving" of errors. Last, a planned and systematic approach must be used to measure, analyze, and improve performance. Successful implementation of performance improvement calls for strong partnerships between physicians, managers, and staff members. Performance excellence requires that everyone work together to ensure that perioperative care is safe, effective, appropriate, customer focused, and efficient.     

Matching patient and physician preferences in designing a primary care facility network

This paper introduces an integer programming model for planning primary care facility networks, which accounts for the interests of different stakeholders while maximizing access to health care. Physician allocation to health-care facilities is explicitly modelled, which allows consideration of physician incentives in the planning phase. An illustrative case study in the Turkish primary care system is presented to show the implications of focusing on patient or physician preferences in the planning phase. A discussion of trade-offs between the different stakeholder preferences and some recommendations for modelling choices to match these preferences are provided. In the context of this case, we found that using an access measure that decays with distance, and incorporating nearest allocation constraints improves performance for all stakeholders. We also show that increasing the number of physicians may have adverse affects on access measures when physician preferences are addressed.     

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.     

On comparing the performance of primary care providers

In the past few years primary health care has been characterised as central to the development of the National Health Service in the United Kingdom. Furthermore, performance assessment is seen as a way of achieving care of high standards. Performance indicators and targets are being developed to assess primary care providers, and to develop financial incentives. However, the number of studies that have compared the performance of primary care providers is limited and the existing approaches to evaluation are open to improvement.     

Value for Money and Prison Perimeters—Goal-programming for Gaols

A computer model has been developed to address the problem of attaining the most cost-effective balance when providing perimeter security at any given prison. The generalized analysis of security of perimeters (GASP) model evaluates the cost and performance of different prison perimeters in terms of several measures. The modelling process takes account not only of the site-specific problems such as perimeter shape, but also of the policy input of trade-offs between the various output measures of cost and effectiveness. Goal-programming and multi-criteria decision-making techniques have been used.     

Prison Perimeter Cost-Effectiveness

This paper describes a study which was carried out for Home Office Prison Department. The problem under review was that of providing cost-effective Perimeter Security for Prisons. This involved identifying performance measures for prison perimeters. First performance and cost measures had to be found for individual elements of a perimeter (for example, walls, cameras). Next, a mathematical formulation was developed to assess the cost-effectiveness of perimeter cross-sections as a whole. The mathematical model has formed the basis of a computer model. This paper describes the development and use of the computer model and outlines areas of development for the future.     

Ordering arc-reversal operations when eliminating variables in lazy AR propagation

This paper considers the problem of ordering arc-reversal operations and breaking ties in cost measures when eliminating variables in Lazy AR Propagation (LPAR). In particular, the paper presents the BreakTies algorithm for breaking ties in cost measures when selecting the next arc to reverse in a variable elimination operation. BreakTies is based upon using a sequence of cost measures instead of randomly selecting an arc to reverse when multiple arcs share the same cost. The paper reports on an experimental evaluation of LPAR for belief update in Bayesian networks considering six sequences of five cost measures for breaking ties using BreakTies. The experimental results show that using BreakTies to select the next arc to reverse in a variable elimination operation can improve performance of LPAR.     

A Markovian queueing model for ambulance offload delays

Ambulance offload delays are a growing concern for health care providers in many countries. Offload delays occur when ambulance paramedics arriving at a hospital Emergency Department (ED) cannot transfer patient care to staff in the ED immediately. This is typically caused by overcrowding in the ED. Using queueing theory, we model the interface between a regional Emergency Medical Services (EMS) provider and multiple EDs that serve both ambulance and walk-in patients. We introduce Markov chain models for the system and solve for the steady state probability distributions of queue lengths and waiting times using matrix-analytic methods. We develop several algorithms for computing performance measures for the system, particularly the offload delays for ambulance patients. Using these algorithms, we analyze several three-hospital systems and assess the impact of system resources on offload delays. In addition, simulation is used to validate model assumptions.     

Optimising key performance indicator adherence with application to emergency department congestion

Many operational queueing systems must adhere to systems of Key Performance Indicators (KPIs), each comprising a waiting time limit and a level of compliance specifying the minimal fraction of customers that must meet the standard. KPIs are frequently employed as measures of system performance in health care settings. The primary flaw with KPIs is that they represent a system of constraints with no objective function. KPIs say nothing about customers who exceed their limit, so long as such occurrences are sufficiently rare, when in fact customers who miss their time limit in a health care setting are of greater importance, not lesser. We address this flaw by minimising the mean number of customers present who have exceeded their respective limits; we consider also weighted averages of the numbers in excess for each class. We then show that one logical service discipline to achieve this goal is the Accumulating Priority Queueing (APQ) discipline. We carry out numerical examples to investigate the utility of our method. We then apply the optimisation approach to the case of an Emergency Department in Southern Ontario, Canada.     

Impact of policy and process design on the performance of intake and treatment processes in mental health care: a system dynamics case study

For most of its history, medical care has been a customized process. Because customized processes might not lead to optimal performance, health-care managers are considering business process redesign, implementation of standardized (brief) therapies, stepped care, and policy changes to solve supply chain management issues. We develop a system dynamics model to support the management of intake and treatment processes in mental health care. We first model the existing situation in the care unit (Base Scenario of customized care) and simulate the impact of policy changes (assigning scarce personnel resources to different care activities) and redesign of intake and treatment processes (implementing brief therapies and stepped care). We show that shifting personnel resources between intake and treatment activities does not substantially improve performance. We found better results of process redesign and stepped care (+42% client intake rate and +18% revenues per day).     

System modeling and performance analysis of the power saving class type II in BWA networks

For reducing the energy consumption of the Mobile Station in mobile Broadband Wireless Access networks, IEEE 802.16 offers three kinds of sleep mode operations called power saving classes type I, type II and type III. In order to investigate mathematically the inherent relationships between the performance measures and the system parameters, we propose in this paper a novel method for modeling the sleep mode with the power saving class type II in IEEE 802.16 and analyzing the performance of this sleep mode. Considering the attractive feature that some data frames can be transmitted during the listening state, we present a queueing model with two kinds of busy mechanisms to capture the working principle of the sleep mode operations with the power saving class type II. With the first and higher derivatives of the probability generating functions, we can give the averages and the standard deviations for the system performance using the diffusion approximation for the operating process of the system. We also propose methods for measuring the system performance in terms of the switching ratio, the energy saving ratio, and the average response time of data frames, as well as giving the expressions for these performance measures. Numerical results are provided with analysis and simulation to show the average performance measures, standard deviations and the cost function with different system loads. Moreover, we construct a cost function with the aim of determining the optimal time length of the sleep window to minimize the cost function.     

Trends in labor efficiency among American hospital markets

The health care sector is one of the most labor intensive sectors of the economy. As a major player in health service delivery, hospitals must closely examine their largest cost — labor expenses. This study evaluates trends in the efficiency of health care labor among urban hospital markets. More specifically, it assesses the hypothesis that technological changes and market and regulatory pressures have increased aggregate labor efficiency. Using data envelopment analysis (DEA), this study evaluates labor efficiency in 1989 and 1993 in 298 metropolitan statistical areas (MSAs) with two or more hospitals. Results of this study suggest that between 1989 and 1993, hospital markets generally demonstrated higher labor inefficiencies. The U.S. health care system could save approximately $16.6 billion in 1993 by eliminating hospitals' excessive use of health care provider labor.     

Primary care network development: the regulator’s perspective

Our research is motivated by the proliferation of primary care models in Ontario, Canada. Currently, primary care is mainly provided by facilities belonging to six models of care. These models are remunerated by various schemes—a mixture of fee-for-service, capitation and salary. In addition, they provide different levels of care and several are better adjusted than others to treat complex health needs. The proposed mixed integer programming model allows the regulator to test the outcomes of locating different types of primary care facilities on the overall cost, accessibility and appropriateness of provided care. The network design is fitted to the heterogeneity of the population residing in a defined geographical area, directly using an index (deprivation index) that was found to correlate with increased health needs and barriers to care. The model capabilities are illustrated on the geographical area of Kingston, Ontario.