Access to Long‐Term Care: The True Cause of Hospital Congestion?

Much attention has been paid to lengthy wait times in emergency departments (EDs) and much research has sought to improve ED performance. However, ED congestion is often caused by the inability to move patients into the wards while the wards in turn are often congested primarily due to patients waiting for a bed in a long‐term care (LTC) facility. The scheduling of clients to LTC is a complex problem that is compounded by the variety of LTC beds (different facilities and room accommodations), the presence of client choice and the competing demands of the hospital and community populations. We present a Markov decision process (MDP) model that determines the required access in order for the census of patients waiting for LTC in the hospitals to remain below a given threshold. We further present a simulation model that incorporates both hospital and community demand for LTC in order to predict the impact of implementing the policy derived from the MDP on the community client wait times and to aid in capacity planning for the future. We test the MDP policy vs. current practice as well as against a number of other proposed policy changes.     

The Impact of Variability and Patient Information on Health Care System Performance

In the delivery of health care services, variability in the patient arrival and service processes can cause excessive patient waiting times and poor utilization of facility resources. Based on data collected at a large primary care facility, this paper investigates how several sources of variability affect facility performance. These sources include ancillary tasks performed by the physician, patient punctuality, unscheduled visits to the facility's laboratory or X‐ray services, momentary interruptions of a patient's examination, and examination time variation by patient class. Our results indicate that unscheduled visits to the facility's laboratory or X‐ray services have the largest impact on a physician's idle time. The average patient wait is most affected by how the physician prioritizes completing ancillary tasks, such as telephone calls, relative to examining patients. We also investigate the improvement in system performance offered by using increasing levels of patient information when creating the appointment schedule. We find that the use of policies that sequence patients based on their classification improves system performance by up to 25.5%.     

How Long Should a Customer Wait for Service?

A major concern for service managers is the determination of how long a customer should wait to be served. Services, due to the customer's direct interaction with the process, must face a trade-off between minimizing the cost of having a customer wait and the cost of providing good service. A total cost model is presented for determining how long a customer should wait when these two conflicting cost components are considered. An integral part of this model includes a measure of customer satisfaction with waiting time which is used to develop a waiting cost function. The model is then applied to a major fast food chain, using data collected at several locations. Analysis of the data reveals that the “ideal” waiting time for this firm is significantly less than the current corporate waiting time policy. Thus, as indicated by the model, a corporate policy change is recommended to provide much faster service. The adoption of such a policy would result in increased labor costs, and would simultaneously increase the firm's overall profits. Although appearing contradictory, increases in current labor costs and long-term profits are both possible when management takes the long-range perspective suggested in this paper.     

Modeling Strategic Behavior in The Competitive Newsvendor Problem: An Experimental Investigation

This study experimentally investigates ordering behavior in the competitive newsvendor problem. We consider a duopoly market setting with two identical newsvendors selling the same perishable goods in a common market. Our experimental results show that average observed orders systematically deviate from the Nash equilibrium, and exhibit a similar pull‐to‐center pattern as in the classic non‐competitive newsvendor experiments: average orders fall below the Nash equilibrium in the high‐margin condition, and above the Nash equilibrium in the low‐margin condition. More importantly, the observed orders in the duopoly market are significantly higher than that in the non‐competitive newsvendor market, even in situations where standard inventory models predict no difference. We explain the ordering behavior using a strategic experience‐weighted attraction (EWA) model, which captures players' propensities for strategic thinking in game settings. Our empirical analysis suggests that the strategic EWA model generates more accurate predictions of future ordering behavior than an existing linear adaptive model without concerning strategic thinking. Further analysis shows that individuals are heterogeneous in their propensities to be a strategic player. Our research indicates the importance of modeling strategic behavior when analyzing behavioral decisions in competitive (game) environments.     

Design and Analysis of Hospital Admission Control for Operational Effectiveness

Variability in hospital occupancy negatively impacts the cost and quality of patient care delivery through increased emergency department (ED) congestion, emergency blockages and diversions, elective cancelations, backlogs in ancillary services, overstaffing, and understaffing. Controlling inpatient admissions can effectively reduce variability in hospital occupancy to mitigate these problems. Currently there are two major gateways for admission to a hospital: the ED and scheduled elective admission. Unfortunately, in highly utilized hospitals, excessive wait times make the scheduled gateway undesirable or infeasible for a subset of patients and doctors. As a result, this group often uses the ED gateway as a means to gain admission to the hospital. To better serve these patients and improve overall hospital functioning, we propose creating a third gateway: an expedited patient care queue. We first characterize an optimal admission threshold policy using controls on the scheduled and expedited gateways for a new Markov decision process model. We then present a practical policy based on insight from the analytical model that yields reduced emergency blockages, cancelations, and off‐unit census via simulation based on historical hospital data.     

Examining Pathways from Innovation Orientation to Patient Satisfaction: A Relational View of Healthcare Delivery*

The federal government and industry leaders view innovation as a potentially fruitful way to improve hospital performance, specifically patient satisfaction. However, translating a hospital's innovation orientation into improved performance is challenging given that important network participants—namely physicians—may possess different aims. Grounded in Relational RBV, this study tests a model linking innovation orientation to patient satisfaction through a pathway of knowledge‐sharing routines (physician partnering and customer relationship management) and complementary capabilities (hospital responsiveness). Further, this study investigates the moderating role of physician employment (a form of governance) by examining hospitals with high and low levels of employed physicians. Structural Equation Modeling results from a paired sample of primary survey and secondary data from 173 acute care hospitals in the USA reveal the following. Hospitals with high levels of employed physicians translate innovation orientation into patient satisfaction by using customer relationship management (CRM) programs to influence hospital responsiveness directly, ultimately leading to patient satisfaction. Hospitals with low levels of physician employment use CRM programs in a fully mediated fashion to inform physician partnering activities, which influence hospital responsiveness, driving patient satisfaction.     

Clinic Overbooking to Improve Patient Access and Increase Provider Productivity*

The problem of patient no‐shows (patients who do not arrive for scheduled appointments) is significant in many health care settings, where no‐show rates can vary widely. No‐shows reduce provider productivity and clinic efficiency, increase health care costs, and limit the ability of a clinic to serve its client population by reducing its effective capacity. In this article, we examine the problem of no‐shows and propose appointment overbooking as one means of reducing the negative impact of no‐shows. We find that patient access and provider productivity are significantly improved with overbooking, but that overbooking causes increases in both patient wait times and provider overtime. We develop a new clinic utility function to capture the trade‐offs between these benefits and costs, and we show that the relative values that a clinic assigns to serving additional patients, minimizing patient waiting times, and minimizing clinic overtime will determine whether overbooking is warranted. From the results of a series of simulation experiments, we determine that overbooking provides greater utility when clinics serve larger numbers of patients, no‐show rates are higher, and service variability is lower. Even with highly variable service times, many clinics will achieve positive net results with overbooking. Our analysis provides valuable guidance to clinic administrators about the use of appointment overbooking to improve patient access, provider productivity, and overall clinic performance.     

Life Is All about Timing: An Examination of Differences in Treatment Quality for Trauma Patients Based on Hospital Arrival Time

In health care, most quality transparency and improvement programs focus on the quality variation across hospitals, while we know much less about within‐hospital quality variation. This study examines one important factor that is associated with the fluctuation of quality of care in the same hospital—the timing of patient arrival. We analyze data from the National Trauma Data Bank and find that patients arriving at the hospital during off‐hours (6 PM–6 AM) receive significantly lower quality care than those who arrive during the daytime, as reflected in higher mortality rates, among other measures. More importantly, we try to uncover the mechanism for the quality variation. Interestingly, we find consistent evidence that the inferior care received during off‐hours is not likely due to unobserved heterogeneity, disruptions in circadian rhythms, or delays in receiving treatment. Instead, it is more likely due to the limited availability of high‐quality resources. This leads to a higher surgical complication rate, a higher likelihood of multiple surgeries, and longer patient length of stay in the intensive care unit. These findings have important implications for optimal resource allocation in hospitals to improve the quality‐of‐care delivery.     

Joint Pricing and Contingent Free-Shipping Decisions in B2C Transactions

We consider an online retailer's joint pricing and contingent free-shipping (CFS) decisions in both monopoly and duopoly structures, which is an important marketing-operations interface problem. We begin by investigating the impacts of a retailer's decisions on consumers' purchase behaviors, and show that the CFS strategy is useful to acquire the consumers with large order sizes. Then, we compute the probability of repeated purchases, and construct an expected profit function for an online retailer in the monopolistic setting. We find that the fixed shipping fees may have the largest impact on the retailer's profit among all shipping-related parameters, and the retailer can benefit more from homogeneous markets than from heterogeneous ones. Next, we consider the competition between two retailers in the duopoly structure, and analytically show that, if two retailers have identical fixed and variable shipping fees, then their equilibrium decisions are equal. In order to numerically find a Nash equilibrium for two retailers, we develop a simulation approach using Arena and OptQuest. Our simulation-based examples suggest that, as a result of the competition, the two retailers should decrease their profit margins but increase their CFS cutoff levels if they have the same fixed and also the same variable shipping fees.     

The development of strategic management in the non‐profit context: Intellectual capital in social service non‐profit organizations

Very little systematic research has examined the applicability of strategic management concepts including SWOT (strengths, weaknesses, opportunities and threats) analysis, industrial organization, resource‐based view and core competency, knowledge‐based view, Balanced Scorecard and intellectual capital (IC) through the lens of strategic management development in the non‐profit context. This paper aims to examine the above concepts in the light of the unique non‐profit environment and determine which one is most applicable to social service non‐profit organizations (SSNPOs) in the knowledge economy. Based on a review of the development of strategic management with a focus on the above concepts within the non‐profit context, this paper argues that the IC concept is more effective compared with the other concepts in the social service non‐profit sector. The paper is considered as a starting point and serves as a milestone in applying IC as a strategic management conceptual framework in the social service non‐profit sector. It helps to build a nascent body of literature suggesting that IC can be used as a competent strategic management conceptual framework in the social service non‐profit sector. A better understanding of the strategic management development in the non‐profit context also helps non‐profit leaders to appreciate that IC is the most appropriate strategic management concept in SSNPOs. The increased awareness of the IC concept in SSNPOs, as a result of this paper, will probably generate further research from both academic scholars and non‐profit practitioners.     

Profit Seeking vs. Survival Seeking: An Analytical Study of Supplier's Behavior and Buyer's Subsidy Strategy

Supplier default is common in emerging markets. Suppliers under the threat of default have different objectives from profit‐seeking companies. This paper analytically tests how profit‐seeking or survival‐seeking behavior, single‐period or two‐period consideration, and buyer's subsidy influence the supplier's and buyer's final utilities. The results show that under single‐period consideration, the supplier's survival‐seeking strategy in fact drives more start‐ups or small suppliers out of business when the competition becomes severe; under two‐period consideration, no matter which strategy (profit‐seeking or survival‐seeking) the supplier selects, the second‐period price and profit are always higher than those of the first period. Furthermore, we find that providing subsidy is an effective way for buyer to keep suppliers’ competition at a certain level on the behalf of buyer's interest. By numerically estimating the benefits associated with the cost of subsidy, we provide a basis for understanding the cost–benefit analysis of buyer's subsidy strategy.     

Accounting Performance and Capacity Investment Decisions: Evidence from California Hospitals

Capacity decisions involve trade‐offs between the cost of capacity and the opportunity costs of lost sales. Accounting researchers posit that accounting performance provides sufficient information about these trade‐offs and thus can be used to formulate simple rules to assist capacity decisions. Empirical research has not examined the role of accounting information in capacity investment decisions at the department level in a multiproduct firm in the presence of social costs. Empirical analyses using department‐level data from California hospitals for the period 1998–2005 show that hospitals are more likely to make capacity investments in departments with high accounting performance. However, in the presence of demand variability, the association between accounting performance and capacity investment is attenuated because of the resulting increase in noise in accounting performance measures. Thus, the weight on accounting performance as a decision tool for capital investments reduces when there is demand variability. Another factor that reduces the weight on accounting performance is capacity utilization. Higher capacity utilization can lead to turning away or rerouting of patients to other hospitals and negatively impacts reputation and quality of care, which increases the hospital's social costs. Hence, hospitals do not require high accounting performance before investing in a department with high capacity utilization. This empirical evidence of the role of accounting performance in capacity investment decisions fills a gap in the capacity investment literature and furthers our understanding of the interactions between accounting performance and the operational determinants of firms’ capacity investment behavior.     

Preparation of chemotherapy drugs: Planning policy for reduced waiting times

This study investigates the impact of pharmacy policies on patient waiting time in the Chemotherapy Day Unit of the Netherlands Cancer Institute—Antoni van Leeuwenhoek hospital. The project evaluated whether a reduction in waiting time resulting from medication orders being prepared in advance of patient appointments was justified, given that medications prepared in advance are wasted when patients arrive too sick for treatment. Within this context, we derive analytic expressions to approximate patient waiting times and wastage costs, allowing management to see the tradeoff between these two metrics for different policies. Using a case study and a simulation model, the approximations are evaluated. The use of analytic expressions allows the analysis to be easily repeated when medication costs change or when new medications/protocols are introduced. In the same vein, other hospitals with different patient case mixes can easily complete the analysis in their settings. Finally, the outcome from this study resulted in a new policy at the cancer center which is expected to decrease the waiting time by half, while only increasing pharmacy's costs by 1-2%.     

An Analysis of Dual‐Kanban Just‐In‐Time Systems in a Non‐Repetitive Environment

Recent advances in approaches and production technologies for the production of goods and services have made just‐in‐time (JIT) a strong alternative for use in intermittent and small batch production systems, especially when time‐based competition is the norm and a low inventory is a must. However, the conventional JIT system is designed for mass production with a stable master production schedule. This paper suggests supplementing the information provided by production kanbans with information about customer waiting lines to be used by operators to schedule production in each work‐station of intermittent and small batch production systems. This paper uses simulation to analyze the effect of four scheduling policy variables—number of kanbans, length of the withdrawal cycle, information about customer waiting lines, and priority rules on two performance measures—customer wait‐time and inventory. The results show that using information about customer waiting lines reduces customer wait‐time by about 30% while also reducing inventory by about 2%. In addition, the effect of information about customer waiting lines overshadows the effect of priority rules on customer wait‐time and inventory.     

Would Allowing Privately Funded Health Care Reduce Public Waiting Time? Theory and Empirical Evidence from Canadian Joint Replacement Surgery Data

This study develops a theoretical model and then, using Canadian joint replacement surgery data, empirically tests the relationship between government policies that promote privately funded health care and patients’ waiting time in the public health care system. Two policies are tested: one policy allows opt‐out physicians to extra‐bill private patients, and the other provides public subsidies to private patients. We find that both policies are associated with shorter public waiting time, and that the subsidy policy appears to be more effective in waiting time reduction than the extra‐billing policy. Our findings are consistent with a dominant demand‐side effect in that these policies would provide patients an option, and some incentive, to opt out of the public health system, shifting the demand from the public health system to the private care market.     

A Stochastic Inventory Model With Fast‐Ship Commitments

We present a multiperiod model of a retail supply chain, consisting of a single supplier and a single retailer, in which regular replenishment occurs periodically but players have the option to support fast delivery when customers experience a stockout during a replenishment period. Because expedited shipments increase the supplier's transportation cost, and possibly production/inventory costs, the supplier typically charges a markup over and above the prevailing wholesale price for fast‐shipped items. When fast shipping is not supported, items are backordered if customers are willing to wait until the start of the next replenishment period. We characterize the retailers and the supplier's optimal stocking and production policies and then utilize our analytical framework to study how the two players respond to changes in supply chain parameters. We identify a sufficient condition such that the centralized supply chain is better off with the fast‐ship option. We find a range of markups for fast‐ship orders such that the fast‐ship option is preferred by both the supplier and the retailer in a decentralized chain. However, a markup that is a win–win for both players may not exist even when offering fast‐ship option is better for the centralized chain. Our analysis also shows that depending on how the markup is determined, greater customer participation in fast‐ship orders does not necessarily imply more profits for the two players. For some predetermined markups, the retailer's profit with the fast‐ship option is higher when more customers are willing to wait. However, the retailer may not be able to benefit from the fast‐ship option because the supplier may choose not to support the fast‐ship option when fast‐ship participation increases due to the fact that the fast‐ship participation rate adversely affects the initial order size.     

Benefits of Surgical Smoothing and Spare Capacity: An Econometric Analysis of Patient Flow

In this study, we examine the hospital's ability to admit patients from its emergency department. From a medical perspective, the number of patients being admitted should depend solely on the patients’ clinical conditions. Using a large‐scale econometric study that includes detailed operational and clinical data on all cardiac patient encounters from a set of 128 hospitals over a period of four years, we show that this is not the case. In particular, we find that independent of their medical condition, many emergency patients are denied hospital admission because of a lack of inpatient beds. Our analysis suggests that having one more inpatient bed at the start of a day can increase the likelihood of an emergency room patient admission by around 3% on average. We examine two policies – active discharge and demand smoothing – that can help hospitals improve patient access. We find that some hospitals actively discharge inpatients when beds become scarce; hospitals that follow such an active discharge protocol are, on average, able to admit more patients. We also investigate to what extent the hospital's ability to smooth its surgical schedule impacts hospital admissions. Hospitals tend to schedule their elective patients early in the week (Mondays and Tuesdays), and discharge them by the weekend in order to minimize weekend staffing, effectively maximizing bed occupancy during the middle of the week. This “weekend effect” artificially induces variability, and reduces effective system capacity. We find that by scheduling patients more uniformly over the week, hospitals can dramatically increase patient access, obviating the need for active discharges or additional capacity investment. Our analysis quantifies these effects, and can help hospitals make effective capacity management decisions in order to improve patient flow.     

Are Patients Patient? The Role of Time to Appointment in Patient Flow

The current state of outpatient healthcare delivery is characterized by capacity shortages and long waits for appointments, yet a substantial fraction of valuable doctors’ capacity is wasted due to no‐shows. In this study, we examine the effect of wait to appointment on patient flow, specifically on a patient's decision to schedule an appointment and to subsequently arrive to it. These two decisions may be dependent, as appointments are more likely to be scheduled by patients who are more patient and are thereby more likely to show up. To estimate the effect of wait on these two decisions, we introduce the willingness to wait (WTW), an unobservable variable that affects both bookings and arrivals for appointments. Using data from a large healthcare system, we estimate WTW with a state‐of‐the‐art non‐parametric method. The WTW, in turn, allows us to estimate the effect of wait on no‐shows. We observe that the effect of increased wait on the likelihood of no‐shows is disproportionately greater among patients with low WTW. Thus, although reducing the wait to an appointment will enable a provider to capture more patient bookings, the effects of wait time on capacity utilization can be non‐monotone. Our counterfactual analysis suggests that increasing wait times can sometimes be beneficial for reducing no‐shows.     

Two Backorder Compensation Mechanisms in Inventory Systems with Impatient Customers

We study a compensation mechanism design problem with customer‐choice behavior in a continuous review setting where the production and demand processes are stochastic. When a stockout occurs, the firm controls backorders on the basis of certain compensation policies. Customers make decisions to maximize their utility, which is decreasing in the price, the waiting time, and the customer's impatience factor. We assume that the impatience factor is private information held by the customer only. Two compensation mechanisms are designed to control backorders, namely uniform compensation and priority auction with an admission price. Under uniform compensation, the firm offers the same discount to all customers, whereas under auction compensation, priority is granted according to the customers' bid prices. We obtain the optimal stockout price and base stock level under each mechanism, and analyze the properties of the respective optimal policies. Assuming linear waiting costs with uniformly distributed impatience factor, we find that the auction mechanism (1) maintains a lower base stock level and results in greater profit and (2) benefits customers with relatively lower or higher impatience factors, but customers with a medium impatience factor may be rendered worse off. We further show that both compensation mechanisms are suitable for products with a high unit profit, a high lost sales penalty cost, and a high holding cost.     

医疗信息共享对患者转移数量和服务质量水平影响研究

当前我国医院间存在严重的"信息孤岛"，医院参与医疗信息共享意愿不高，患者的诊疗信息被静态碎片化储存而无法充分有效地利用。考虑到医院进行医疗信息共享将降低患者转移成本，本文构建一个多阶段双寡头动态博弈模型研究医疗信息共享对医院竞争过程中患者转移数量和服务质量水平决策的影响。首先，根据是否存在转移成本，将患者分为新患者和经验性患者，借助Hotelling模型刻画患者的效用函数，分析患者就诊决策。然后，在政府价格规制和不考虑医院利他性情景下，构建了医院累积期望收益目标函数，使用动态规划方法，求解实现医院累积期望收益最大化的服务质量水平，获得了实现患者相互转移且医院在市场中共存的马尔可夫完美均衡。最后，根据医院参与医疗信息共享后患者转移成本降为零，分析与比较信息共享前后患者转移数量和服务质量水平变化。研究发现：在不同医院间本身存在患者转移背景下，医院参与信息共享后，患者转移数量增加但存在一个上限，增加的转移量与患者在医院间的转移成本呈正相关，与初始感知效用的差值范围呈负相关；医院参与信息共享后，均衡状态下的医院服务质量水平高于信息共享前的服务质量水平。因此，在不改变当前医保支付方式下，要加快推进医疗信息共享，政府部门可以根据医院的患者数量和服务质量水平变化对其进行定期补贴，以激励医院积极参与医疗信息共享，本文给出了这个补贴的量化表达。