Elective Patient Admission and Scheduling under Multiple Resource Constraints

We consider a patient admission problem to a hospital with multiple resource constraints (e.g., OR and beds) and a stochastic evolution of patient care requirements across multiple resources. There is a small but significant proportion of emergency patients who arrive randomly and have to be accepted at the hospital. However, the hospital needs to decide whether to accept, postpone, or even reject the admission from a random stream of non‐emergency elective patients. We formulate the control process as a Markov decision process to maximize expected contribution net of overbooking costs, develop bounds using approximate dynamic programming, and use them to construct heuristics. We test our methods on data from the Ronald Reagan UCLA Medical Center and find that our intuitive newsvendor‐based heuristic performs well across all scenarios.     

Toward an Effective Design of Behavioral Health Care Delivery: An Empirical Analysis of Care for Depression

A criticism of behavioral health care delivery is that it has largely missed the social determinants of behavioral health disorders and their diagnosis. Toward addressing this criticism, this study evaluates the delivery of behavioral health care as a part of primary care operations. Focusing on the treatment of depression, the study results show that: (i) primary care clinics operating in communities with superior social environment characteristics are associated with improved depression outcomes in the short term, and (ii) psychosocial resources (social and emotional support) and the built environment (man‐made resources and infrastructure to support human activity) of primary care clinics are associated with sustaining the improvement in depression outcome in the long term. Centering our attention on IT‐enabled, evidence‐based, and affordable primary care as mechanisms that can enable the integration of behavioral and medical care delivery, the results suggest that IT‐enabled and evidence‐based primary care are associated with improvements in depression outcomes. We also find that the effect of improving the affordability of behavioral health care delivery depends on the community's socioeconomic status. Primary care clinics in socioeconomically disadvantaged communities practicing cost‐containment are associated with improvements in depression outcomes, and, therefore, can contribute toward reducing disparities in behavioral health care delivery. Counter to our original expectations, we find that the effect of evidence‐based care on improvements on depression outcomes increases as the availability of medically trained behavioral health care specialists practicing in a community increases lending support to concerns that primary care clinics in resource‐rich communities obtain greater benefit from quality improvement interventions.     

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.     

Competition and The Operational Performance of Hospitals: The Role of Hospital Objectives

We examine the effect of a hospital's objective (i.e., non‐profit vs. for‐profit) in hospital markets for elective care. Using game‐theoretic analysis and queueing models to capture the operational performance of hospitals, we compare the equilibrium behavior of three market settings in terms of such criteria as waiting times and patient costs from waiting and hospital payments. In the first setting, a monopoly, patients are served exclusively by a single non‐profit hospital; in the second, a homogeneous duopoly, patients are served by two competing non‐profit hospitals. In our third setting, a heterogeneous duopoly, the market is served by one non‐profit hospital and one for‐profit hospital. A non‐profit hospital provides free care to patients, although they may have to wait; for‐profit hospitals charge a fee to provide care with minimal waiting. A comparison between the monopolistic and each of the duopolistic settings reveals that the introduction of competition can hamper a hospital's ability to attain economies of scale and can also increase waiting times. Moreover, the presence of a for‐profit sector may be desirable only when the hospital market is sufficiently competitive. A comparison across the duopolistic settings indicates that the choice between homogeneous and heterogeneous competition depends on the patients' willingness to wait before receiving care and the reimbursement level of the non‐profit sector. When the public funder is not financially constrained, the presence of a for‐profit sector may allow the funder to lower both the financial costs of providing coverage and the total costs to patients. Finally, our analysis suggests that the public funder should exercise caution when using policy tools that support the for‐profit sector—for example, patient subsidies—because such tools may increase patient costs in the long run; it might be preferable to raise the non‐profit sector's level of reimbursement.     

基于马尔科夫决策过程的货物流的配给策略

随着全球化趋势和不断增长的运输量,许多大型货站已经开始使用综合自动化装运处理系统。在这些货站中,不同起点(如仓库入口点)和讫点(如仓储货架)之间的路径选择是一个至关重要的决策,本文中,我们研究此类路径最优问题,并提出了有效的货物流配给策略。该策略中不同的货物有不同的路径集合与之对应,且只有在权重较高的路径的饱和度较低时,才允许权重较小的货物在权重较高的路径上运输,不允许权重高的货物在权重低的路径上运输。我们引入马尔可夫决策过程模型进行决策,并通过数值试验证明我们提出的路径策略的有效性。     

An Analytical Framework for Designing Community‐Based Care for Chronic Diseases

In this study, we propose a methodological framework to provide a road map to clinicians and system planners in developing chronic disease management strategies, and designing community‐based care. We extend the analytical epidemiologic model by utilizing a patient flow approach, in order to model the multiple care‐provider visit patterns of patients with a specific chronic illness. The patterns of care received by a group of patients are represented in compact form by means of a Markov model that is based on a disease‐specific state space. Our framework also reflects the case‐mix biases as well as the care‐provider level clustering of the patients. By using this approach, we identify the patterns of care, determine the care provider and patient characteristics associated with optimal management of care, and estimate the potential influence of various interventions. The framework is applied to the data of 4000+ stroke patients discharged from the acute care hospitals of Quebec to their homes. Our findings provide a basis for designing community‐based care initiatives for stroke survivors in the province.     

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%.     

Analysis of Effects of Operational Execution on Repeat Purchasing for Heterogeneous Customer Segments

Many retailers are increasingly turning to home delivery as a new arena of operational competition. This study controlled for industry by investigating the online home delivery grocery business, and an analysis of 1,919 customers of home delivery grocers identified four groups of online customers based on reasons for selecting this service. These four groups were next linked to operational execution in terms of service, product, and Internet quality, and found to vary in predicable ways. Subsequent to the initial data collection, five month's of post hoc longitudinal purchasing history was collected on the four groups of online customers to determine the relative profitability. Finally, as a follow‐on analysis, the study used regression to predict future consumer purchases based upon operational execution. Time savings and service quality emerged as the two most important independent variables in terms of future buying from such online home delivery services.     

Assessment of Patient Classification in Appointment System Design

This paper investigates two approaches to patient classification: using patient classification only for sequencing patient appointments at the time of booking and using patient classification for both sequencing and appointment interval adjustment. In the latter approach, appointment intervals are adjusted to match the consultation time characteristics of different patient classes. Our simulation results indicate that new appointment systems that utilize interval adjustment for patient class are successful in improving doctors' idle time, doctors' overtime and patients' waiting times without any trade‐offs. Best performing appointment systems are identified for different clinic environments characterized by walk‐ins, no‐shows, the percentage of new patients, and the ratio of the mean consultation time of new patients to the mean consultation time of return patients. As a result, practical guidelines are developed for managers who are responsible for designing appointment systems.     

爱尔朗型按订单装配系统最优生产——库存控制策略研究

本文研究具有复杂装配结构的爱尔朗型按订单装配(ATO)系统的组件生产与库存优化控制问题。系统涉及多种组件,一个最终产品和多类客户需求。在此系统中,各种组件的生产时间服从爱尔朗分布,各类客户的需求为泊松到达过程。针对不同客户需求类型:产品需求与独立组件需求且同为销售损失型,建立基于马尔可夫决策过程(MDP)的平均总成本模型,应用动态规划方法求解最优策略。仿真模拟方法实现最优策略,并通过数值实验分析多生产阶段和系统参数对最优策略的影响。研究结果表明,爱尔朗型生产时间ATO系统的最优策略为状态依赖型策略,即组件的生产与库存分配由动态基础库存水平值和动态库存配给水平值控制。对于任一组件,其基础库存水平值和库存配给水平值均随着生产阶段的增加而降低,且生产阶段对基础库存水平值和平均总成本的影响较显著。     

GIS支持下的防汛指挥决策支持系统的系统分析与设计

防汛指挥决策支持系统是一项重要的综合性非工程防洪减灾措施,是实现防汛指挥决策科学化和现代化的重要手段。本文对防汛指挥决策支持系统进行了系统分析,论述了GIS在防汛指挥支持系统中的作用,在此基础上重点探讨了GIS支持下的防汛指挥决策支持系统的系统设计,包括数据库设计、模型库设计、系统集成等方面的内容。     

The Effectiveness of Management‐By‐Walking‐Around: A Randomized Field Study

Management‐by‐walking‐around (MBWA) is a widely adopted technique in hospitals that involves senior managers directly observing frontline work. However, few studies have rigorously examined its impact on organizational outcomes. This study examines an improvement program based on MBWA in which senior managers observe frontline employees, solicit ideas about improvement opportunities, and work with staff to resolve the issues. We randomly selected hospitals to implement the 18‐month‐long, MBWA‐based improvement program; 56 work areas participated. We find that the program, on average, had a negative impact on performance. To explain this surprising finding, we use mixed methods to examine the impact of the work area's problem‐solving approach. Results suggest that prioritizing easy‐to‐solve problems was associated with improved performance. We believe this was because it resulted in greater action‐taking. A different approach was characterized by prioritizing high‐value problems, which was not successful in our study. We also find that assigning to senior managers responsibility for ensuring that identified problems get resolved resulted in better performance. Overall, our study suggests that senior managers' physical presence in their organizations' front lines was not helpful unless it enabled active problem solving.     

Exploring Framing Effects in Inventory Control Decisions: Violations of Procedure Invariance

Recent research on decision framing has shown that (i) there are multiple types of framing effects and (ii) the context of the decision can influence framing effects. This research examines decision framing effects in inventory control contexts by questioning the assumption of procedure invariance, that preference should not be impacted by how options are presented to a supply chain manager making an inventory control decision. Study 1 uses three single‐shot decision experiments to establish that all three types of framing effects identified by Levin et al. ( 1998 ) apply in basic inventory control contexts. Results were consistent with theory in all three cases. Given this evidence that framing effects have potential to impact inventory control decisions, two laboratory experiments in Study 2 utilize multi‐period decision tasks to demonstrate that framing effects can impact performance in a dynamic inventory decision setting similar to practice. One of the experiments in Study 2 was conducted with student subjects, while the other with inventory managers from a large retail firm. Results from both experiments provide evidence that even when initial framing effects on order quantities fade, there can be longer term effects on inventory levels and performance. Furthermore, these effects are robust to education and professional experience. The findings suggest that although a manager might select appropriate inventory control metrics, prudence must be exercised in the presentation of these metrics, and that mere presentation can be used to alleviate known human biases in inventory control decisions.     

Panel Size and Overbooking Decisions for Appointment‐Based Services under Patient No‐Shows

Many service systems that work with appointments, particularly those in healthcare, suffer from high no‐show rates. While there are many reasons why patients become no‐shows, empirical studies found that the probability of a patient being a no‐show typically increases with the patient's appointment delay, i.e., the time between the call for the appointment and the appointment date. This paper investigates how demand and capacity control decisions should be made while taking this relationship into account. We use stylized single server queueing models to model the appointments scheduled for a provider, and consider two different problems. In the first problem, the service capacity is fixed and the decision variable is the panel size; in the second problem, both the panel size and the service capacity (i.e., overbooking level) are decision variables. The objective in both cases is to maximize some net reward function, which reduces to system throughput for the first problem. We give partial or complete characterizations for the optimal decisions, and use these characterizations to provide insights into how optimal decisions depend on patient's no‐show behavior in regards to their appointment delay. These insights especially provide guidance to service providers who are already engaged in or considering interventions such as sending reminders in order to decrease no‐show probabilities. We find that in addition to the magnitudes of patient show‐up probabilities, patients' sensitivity to incremental delays is an important determinant of how demand and capacity decisions should be adjusted in response to anticipated changes in patients' no‐show behavior.     

Statistical Design for Sampling and Analysis of Animal Populations for Chemical Contamination

A strategy for sampling of animal tissues and a statistical approach for analyzing data on body burdens of a parent chemical and its metabolites is presented such that the data may be evaluated in relation to the detection limit(s)of the analytical techniques used and the criterion levels established for acceptable tissue concentrations.     

The Impact of Estimation: A New Method for Clustering and Trajectory Estimation in Patient Flow Modeling

The ability to accurately forecast and control inpatient census, and thereby workloads, is a critical and long‐standing problem in hospital management. The majority of current literature focuses on optimal scheduling of inpatients, but largely ignores the process of accurate estimation of the trajectory of patients throughout the treatment and recovery process. The result is that current scheduling models are optimizing based on inaccurate input data. We developed a Clustering and Scheduling Integrated (CSI) approach to capture patient flows through a network of hospital services. CSI functions by clustering patients into groups based on similarity of trajectory using a novel semi‐Markov model (SMM)‐based clustering scheme, as opposed to clustering by patient attributes as in previous literature. Our methodology is validated by simulation and then applied to real patient data from a partner hospital where we demonstrate that it outperforms a suite of well‐established clustering methods. Furthermore, we demonstrate that extant optimization methods achieve significantly better results on key hospital performance measures under CSI, compared with traditional estimation approaches, increasing elective admissions by 97% and utilization by 22% compared to 30% and 8% using traditional estimation techniques. From a theoretical standpoint, the SMM‐clustering is a novel approach applicable to any temporal‐spatial stochastic data that is prevalent in many industries and application areas.     

Multimedia Benchmarking Analysis for Three Risk Assessment Models: RESRAD, MMSOILS, and MEPAS

This paper is one in a series that describes results of a benchmarking analysis initiated by the Department of Energy (DOE) and the United States Environmental Protection Agency (EPA). An overview of the study is provided in a companion paper by Laniak et al. presented in this journal issue. The three models used in the study—RESRAD (DOE), MMSOILS (EPA), and MEPAS (DOE)—represent analytically-based tools that are used by the respective agencies for performing human exposure and health risk assessments. Both single media and multimedia benchmarking scenarios were developed and executed. In this paper, the multimedia scenario is examined. That scenario consists of a hypothetical landfill that initially contained uranium-238 and methylene chloride. The multimedia models predict the fate of these contaminants, plus the progeny of uranium-238, through the unsaturated zone, saturated zone, surface water, and atmosphere. Carcinogenic risks are calculated from exposure to the contaminants via multiple pathways. Results of the tests show that differences in model endpoint estimates arise from both differences in the models' mathematical formulations and assumptions related to the implementation of the scenarios.     

Visual management system to improve care planning and controlling: the case of intensive care unit

Health Lean Management (HLM) is usually implemented to enhance the operational performance in health care; nevertheless, scant attention is devoted to visual control and daily accountability processes. This paper aims at investigating how to adopt a visual management system to: plan and control patient journey in health care operations and perform a continuous improvement process. Conducting in-depth interviews and observations, participating in meetings and collecting documentation, the patient journey board and daily briefings of an Intensive Care Unit were studied and improved. In the patient journey board, the Plan, Do and Check phases were reported, distinguishing between actual and planned actions for the patient care. The daily meetings were organised and a continuous improvement board was also developed to solve specific issues (Act phase), by creating ad-hoc teams, involving the staff and reporting transparently on the board. Operational and sociotechnical performance, before and after the adoption of HLM, were assessed demonstrating the effectiveness and efficiency of visual management tools.     

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.