Uniform pricing and subsidy coordination mechanism in a two-tier healthcare system under a co-payment policy

Recently, the lengthy waiting time in public hospitals (called the public system) under the free healthcare policy has become a serious problem. To address this issue, motivated by the Japanese healthcare system, this paper investigates a two-tier co-payment healthcare system under a uniform pricing and subsidy coordination mechanism. In such a setting, the public system and the private system (i.e., the private hospitals) compete for market share with different objectives, whereas the government uniformly sets the service price and the subsidy rate to maximize social welfare under a total budget constraint. Compared with two free healthcare policy cases implemented in the Canadian and Australian healthcare systems respectively in terms of social welfare, the results show that when the market demand (or the patient service quality sensitivity) is sufficiently high (sufficiently low), the uniform pricing and subsidy coordination mechanism is better and worse otherwise; and when the patient's waiting sensitivity (or the total government budget) is in an appropriate middle range (sufficiently low or high), the mechanism can outperform than the free policy cases.     

Two‐tier healthcare service systems and cost of waiting for patients

Waiting has been a significant concern for healthcare services. We address this issue in the context of a two‐tier service system in this study. A two‐tier healthcare service system consists of two different service providers, typically one public service provider and one private service provider. In a baseline model, the two service providers are modeled by two queue servers, which charge each patient a common fixed fee for the service. Then, we study a queue model in which one service provider offers a subsidy or charges a premium while the other maintains the fixed service fee. This system provides a mechanism to segment patients along their waiting time cost through price discrimination. We analyze the problem from both the perspective of minimizing total waiting cost for all patients and the perspective of maximizing social gain for the public service provider or profit for the private service provider. We show that this model can significantly alleviate the burden of waiting for patients. The study addresses the design, the efficiency, and the implementation of two‐tier healthcare service systems. Copyright © 2017 John Wiley & Sons, Ltd.     

Locating specialized service capacity in a multi-hospital network

Multi-hospital systems have become very common in today’s healthcare environment. However, there has been limited published research examining the opportunities and challenges of pooling specialized services to a subset of hospitals in the network. Therefore, this paper considers how hospital networks with multiple locations can leverage pooling benefits when deciding where to position specialized services, such as magnetic resonance imaging (MRI), transplants, or neonatal intensive care. Specifically, we develop an optimization model to determine how many and which of a hospital network’s hospitals should be set up to deliver a specialized service. Importantly, this model takes into account both financial considerations and patient service levels. Computational results illustrate the value of optimally pooling resources across a subset of hospitals in the network versus two alternate approaches: (1) delivering the service at all locations and requiring each site to handle its own demand, or (2) locating the service at one hospital that handles all network demand.     

Computationally efficient evaluation of appointment schedules in health care

We consider the problem of evaluating and constructing appointment schedules for patients in a health care facility where a single physician treats patients in a consecutive manner, as is common for general practitioners, clinics and for outpatients in hospitals. Specifically, given a fixed-length session during which a physician sees K patients, each patient has to be given an appointment time during this session in advance. Optimising a schedule with respect to patient waiting times, physician idle times, session overtime, etc. usually requires a heuristic search method involving a huge number of repeated schedule evaluations. Hence, our aim is to obtain accurate predictions at very low computational cost. This is achieved by (1) using Lindley’s recursion to allow for explicit expressions and (2) choosing a discrete-time (slotted) setting to make those expressions easy to compute. We assume general, possibly distinct, distributions for the patients’ consultation times, which allows to account for multiple treatment types, emergencies and patient no-shows. The moments of waiting and idle times are obtained and the computational complexity of the algorithm is discussed. Additionally, we calculate the schedule’s performance in between appointments in order to assist a sequential scheduling strategy.     

Analysis of a two-phase queueing system with a fixed-size batch policy

We consider a single-server, two-phase queueing system with a fixed-size batch policy. Customers arrive at the system according to a Poisson process and receive batch service in the first-phase followed by individual services in the second-phase. The batch service in the first-phase is applied for a fixed number (k) of customers. If the number of customers waiting for the first-phase service is less than k when the server completes individual services, the system stays idle until the queue length reaches k. We derive the steady state distribution for the system’s queue length. We also show that the stochastic decomposition property can be applied to our model. Finally, we illustrate the process of finding the optimal batch size that minimizes the long-run average cost under a linear cost structure.     

Optimal location of public health centres which provide free and paid services

A model and a heuristic are presented for finding the most effective location of public health centres providing non-vital services in competition with existing private health centres. While private centres provide only services to customers who can pay for them, public centres provide both paid services to affluent customers, and subsidised services to customers belonging to low-income groups (a hierarchical structure). While low-income customers are assigned to fixed public centres, high-income customers can choose which centre to patronise. To find the solution of this problem, the equilibrium between maximum coverage of low-income population (within a pre-specified distance), and an adequate capture of high-income population must be found. Thus, in the public service, the revenues obtained from paid services are used to partly cover the costs of the subsidised services, and the number of centres that can be located depends on how many high-income clients can be captured. Capture of a high-income client happens when a public centre is located closer to the client than any of the existing private centres. Computational experience with optimal, as well as special heuristic, methods for solving this problem is described.     

Analyzing online B2B exchange markets: Asymmetric cost and incomplete information

This research applies the discriminating auction to analyze the online B2B exchange market in which a single buyer requests multiple items and several suppliers having equal capacity and asymmetric cost submit bids to compete for buyer demand. In the present model, we examine the impact of asymmetric cost and incomplete information on the participants in the market. Given the complete cost information, each supplier randomizes its price and the lower bound of the price range is determined by the highest marginal cost. In addition, the supplier with a lower marginal cost has a larger considered pricing space but ultimately has a smaller equilibrium one than others with higher marginal costs. When each supplier’s marginal cost is private information, the lowest possible price is determined by the number of suppliers and the buyer’s reservation price. Comparing these two market settings, we find whether IT is beneficial to buyers or suppliers depends on the scale of the bid process and the highest marginal cost. When the number of suppliers and the difference between the highest marginal cost and the buyer’s reservation price are sufficiently large, each supplier can gain a higher profit if the marginal costs are private information. On the contrary, when the highest marginal cost approaches the buyer’s reservation price, complete cost information benefits the suppliers.     

技术不确定环境下新产品策略的设计应用

技术的动态发展和企业间的竞争对企业新产品策略有很大影响,直接决定新产品的引进周期。本文在产业技术动态变化的随机环境下构建随机动态规划模型,关注产业技术进步、投资成本和产品市场竞争等影响因素,探讨企业进行新产品引进的周期选择,对新产品引进的周期和质量决策进行方法设计和应用举例。利用随机动态规划模型得出新产品引进的最优时间周期,用算例分析技术进步和产品研发成本对企业引进周期策略的影响,采取策略迭代的方法进行求解,发现技术进步较快时企业的新产品引进步伐也较快,研发成本的提高使企业的新产品引入步伐降低。     

The Economics of the Hiring of Private Wires in Great Britain—A Simulation Study

The economics of hiring private telephone wires is considered in terms of the required grade of service, and the cost of the alternative public service. A maximum waiting time limitation is imposed and a simulation to determine the relationship between traffic offered to the private wire system and that accepted, is described.     

Equilibrium analysis in batch-arrival queues with complementary services

This paper studies a batch-arrival queue with two complementary services. The two services are complementary and any customer has no benefit from obtaining just one of them. To the best of the authors’ knowledge, there are no works contributed to the batch-arrival queues on analysis of the equilibrium behaviors in queueing systems by now. The properties of batch-arrival queues, which is more practical and universal in reality, induce different Nash equilibria under competition or monopoly compared with the single-arrival queues. We observe the joint effect of batch joining rate and cost structure on the behavior of customers and graphically interpret the equilibrium solutions under competition. Moreover, we discuss the model under three types of price structures and give comparisons from customer and server points.     

Simulation optimization for an emergency department healthcare unit in Kuwait

This paper integrates simulation with optimization to design a decision support tool for the operation of an emergency department unit at a governmental hospital in Kuwait. The hospital provides a set of services for different categories of patients. We present a methodology that uses system simulation combined with optimization to determine the optimal number of doctors, lab technicians and nurses required to maximize patient throughput and to reduce patient time in the system subject to budget restrictions. The major objective of this decision supporting tool is to evaluate the impact of various staffing levels on service efficiency. Experimental results show that by using current hospital resources, the optimization simulation model generates optimal staffing allocation that would allow 28% increase in patient throughput and an average of 40% reduction in patients’ waiting time.     

Short-term and long-term competition between providers of shrink-wrap software and software as a service

Software as a service (SaaS) has moved quickly from a peripheral idea to a mainstream phenomenon. By bundling a software product with delivery and maintenance service, SaaS providers can effectively differentiate their products with traditional shrink-wrap software (SWS). This research uses a game theoretical approach to examine short- and long-term competition between SaaS and SWS providers. We analyze the factors that affect equilibrium outcomes, including user implementation costs, SaaS provider’s operation efficiency, and quality improvement over time. Bundling software with service lowers software implementation cost for users, and our results suggest that it increases equilibrium prices. In providing software services, SaaS providers have to incur significant operation cost. In the long run, service operation cost may significantly affect SaaS firm’s ability to improve its software quality.     

A comparison of shadow prices and reimbursement rates of hospital services

The purpose of this paper is to calculate shadow prices of hospital services and compare them to the reimbursement rates those hospitals receive. These shadow prices are calculated by estimating a multiple-output distance function and applying a dual Shephard's lemma, a technique suggested by Färe and Grosskopf [8]. In contrast to cost functions, distance functions require no price data and do not presume cost minimization. We apply this technique to a sample of California hospitals operating in 1986. We find that hospitals engaged in selective contracting for Medi-Cal patients exhibit closer agreement between relative shadow prices and relative reimbursement rates (Medi-Cal relative to private patients) than noncontracting hospitals.Contact author.     

Location of services and the impact on healthcare quality: insights from a simulation of a musculoskeletal physiotherapy service

Many healthcare systems are being redesigned to deliver local care with more services within the community. Relocation may enhance access but other aspects of healthcare quality should also be considered, notably waiting times and equity of care. This study examined a musculoskeletal physiotherapy service using a discrete-event simulation with simple heuristics to model patient behaviour. This combination provided an effective mechanism for incorporating the individuality of the patients in the flows along the patient pathways, subject to the varying availabilities of key resources. In particular, it captured the feedback that is critical in system performance, especially where waiting times are important. The model recognised the heterogeneity of patient attitudes and demonstrated how the behaviour of a relatively small proportion can affect the experience of all patients. The study suggested that, with careful operational management, more care could be delivered locally while exploiting many of the benefits of a centralised service.     

Selfish routing in public services

It is well observed that individual behaviour can have an effect on the efficiency of queueing systems. The impact of this behaviour on the economic efficiency of public services is considered in this paper where we present results concerning the congestion related implications of decisions made by individuals when choosing between facilities. The work presented has important managerial implications at a public policy level when considering the effect of allowing individuals to choose between providers. We show that in general the introduction of choice in an already inefficient system will not have a negative effect. Introducing choice in a system that copes with demand will have a negative effect.     

The use of relative priorities in optimizing the performance of a queueing system

Relative priorities in an n-class queueing system can reduce server and customer costs. This property is demonstrated in a single server Markovian model where the goal is to minimize a non-linear cost function of class expected waiting times. Special attention is given to minimizing server’s costs when the expected waiting time of each class is restricted.     

基于政府补贴的养老服务市场供给研究

随着我国老龄化速度加快,养老服务的有效供给问题是政府和学者关注的焦点。考虑政府财政补贴下,引入民办与公办养老服务的替代因子,构建民办养老机构与公办养老机构的服务动态供给模型,分析不同补贴方式和补贴力度对服务均衡供给量的影响,并进一步通过补贴乘数分析政府补贴对养老服务机构最优供给决策的影响程度。结果表明:政府对民办养老机构的财政补贴可以增加养老服务市场供给量,相较于运营补贴,政府建设补贴的政策效应更加明显;财政补贴降低了民办养老机构的建设成本和投资风险,刺激社会资本投入的积极性,民办养老服务供给增加幅度大于公办养老服务供给减少幅度,养老服务市场有效供给增加。在财政预算约束下,选择恰当的财政补贴方式,可以提高财政资源的配置效率,增加养老服务市场的有效供给,缓解养老服务财政压力。     

A stochastic appointment scheduling system on multiple resources with dynamic call-in sequence and patient no-shows for an outpatient clinic

This research focuses on the stochastic assignment system motivated by outpatient clinics, especially the physical therapy in rehabilitation service. The aim of this research is to develop a stochastic overbooking model to enhance the service quality as well as to increase the utilization of multiple resources, like therapy equipment in a physical therapy room, with the consideration of patients’ call-in sequence. The schedule for a single-service period includes a fixed number of blocks of equal length. When patients call, they are assigned to an appointment time for that block, and an existing appointment is not allowed to be changed. In each visit, a patient might require more than one resource and a probability of no-show. Two estimation methods were proposed for the expected waiting and overtime cost with multiple resources: Convolution Estimation Method and Joint Cumulative Estimation Method for the upper and lower bound value; respectively. A numerical example based on a physical therapy room was used to show that this stochastic model was able to schedule patients for better profitability compared with traditional appointment systems based on four prioritization rules. The workload in each appointment slot was more balanced albeit more patients were assigned to the first slot to fill up the empty room.     

An agent-based simulation model to evaluate alternative co-payment scenarios for contributing to health systems financing

Ageing populations, rapid technological progress and recent public budget cuts currently threaten the sustainability of public health systems. To meet growing needs with declining resources, decision-makers must identify new ways to avoid reducing the quality of services offered to citizens. This paper focuses on the so-called “co-payment” tools aimed to obtain additional resources for the public health budget directly from citizens. Whereas certain forms of co-payments have always been introduced within health systems to prevent moral hazard behaviours, other co-payment mechanisms are explicitly intended to help finance public healthcare systems. Literature and empirical findings do not agree about the final impact of such co-payment tools, particularly whether they can attain system sustainability and guarantee equitably delivered services. In this paper, we develop an agent-based simulation model which can be used by decision-makers as a decision support tool to compare different co-payment rules and evaluate their impact on the public budget and the health expense of different groups of citizens.