Sequential and Simultaneous Decision Making for Optimizing Health Care Resource Flexibilities

Health care administrators commonly employ two types of resource flexibilities (demand upgrades and staffing flexibility) to efficiently coordinate two critical internal resources, nursing staff and beds, and an external resource (contract nurses) to satisfy stochastic patient demand. Under demand upgrades, when beds are unavailable for patients in a less acute unit, patients are upgraded to a more acute unit if space is available in that unit. Under staffing flexibility, nurses cross‐trained to work in more than one unit are used in addition to dedicated and contract nurses. Resource decisions (beds and staffing) can be made at a single point in time (simultaneous decision making) or at different points in time (sequential decision making). In this article, we address the following questions: for each flexibility configuration, under sequential and simultaneous decision making, what is the optimal resource level required to meet stochastic demand at minimum cost? Is one type of flexibility (e.g., demand upgrades) better than the other type of flexibility (e.g., staffing flexibility)? We use two‐stage stochastic programming to find optimal resource levels for two nonhomogeneous hospital units that face stochastic demand following a continuous, general distribution. We conduct a full‐factorial numerical experiment and find that the benefit of using staffing flexibility on average is greater than the benefit of using demand upgrades. However, the two types of flexibilities have a positive interaction effect and they complement each other. The type of flexibility and decision timing has an independent effect on system performance (capacity and staffing costs). The benefits of cross‐training can be largely realized even if beds and staffing levels have been determined prior to the establishment of a cross‐training initiative.     

Examining Links Between Sign-Out Reporting During Shift Changeovers and Patient Management Risks

This article reports on a qualitative study that investigated how various risk factors associated with the process of sign-out reporting across shifts in critical care hospital environments could lead to flawed communication and thus to increased risk of poor patient outcomes. The study was performed in two critical care hospital units: the pediatric intensive care unit (PICU) and the postanesthesia care unit (PACU). We collected data from observations of eight nurses and four resident physicians in the PICU and four nurses and four resident physicians in the PACU giving sign-out reports during their shift changes. In addition, we conducted semi-structured interviews with a separate sample of medical providers consisting of nurse managers, attending physicians, nurses, and residents from each of these two units. The issues that were addressed in these interviews included how various methods of conducting sign-outs and factors such as personality and experience could impact the effectiveness of communication during sign-out reporting. We also collected data from these medical providers on how failures in communication during sign-out reporting could lead to potentially adverse patient outcomes. The article concludes with the presentation of a modeling framework that demonstrates how the combined influences of risk factors can generate a particularly important type of failure mode in communication and how interventions can be targeted to serve as barriers to such events. A number of recommendations intended for reducing risks associated with the communication of sign-out reports are also presented.     

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.     

Blocking in Healthcare Operations: A New Heuristic and an Application

We consider the problem of optimal capacity allocation in a hospital setting, where patients pass through a set of units, for example intensive care and acute care (AC), or AC and post‐acute care. If the second stage is full, a patient whose service at the first stage is complete is blocked and cannot leave the first stage. We develop a new heuristic for tandem systems to efficiently evaluate the effects of such blocking on system performance and we demonstrate that this heuristic performs well when compared with exact solutions and other approaches presented in the literature. In addition, we show how our tandem heuristic can be used as a building block to model more complex multi‐stage hospital systems with arbitrary patient routing, and we derive insights and actionable capacity strategies for a real hospital system where such blocking occurs between units.     

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.     

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.     

DRAM: a model of health care resource allocation in czechoslovakia

This paper presents an application to data from Czechoslovakia of a health care resource allocation model called DRAM (Disaggregated Resource Allocation Model). DRAM was developed by the health care systems modelling group at the International Institute for Applied Systems Analysis (IIASA). It attempts to predict the consequences of resource-level changes, in terms of the numbers of patients treated in each clinical category and the quality of care they receive in each mode of treatment. In this application, seven acute clinical categories and two types of resources (hospital doctors and hospital beds) are selected for examination in one mode of treatment—in-patient care. Some parallels are drawn with a comparable application in the UK.     

Reducing Hospital Readmissions by Integrating Empirical Prediction with Resource Optimization

Hospital readmissions present an increasingly important challenge for health‐care organizations. Readmissions are expensive and often unnecessary, putting patients at risk and costing $15 billion annually in the United States alone. Currently, 17% of Medicare patients are readmitted to a hospital within 30 days of initial discharge with readmissions typically being more expensive than the original visit to the hospital. Recent legislation penalizes organizations with a high readmission rate. The medical literature conjectures that many readmissions can be avoided or mitigated by post‐discharge monitoring. To develop a good monitoring plan it is critical to anticipate the timing of a potential readmission and to effectively monitor the patient for readmission causing conditions based on that knowledge. This research develops new methods to empirically generate an individualized estimate of the time to readmission density function and then uses this density to optimize a post‐discharge monitoring schedule and staffing plan to support monitoring needs. Our approach integrates classical prediction models with machine learning and transfer learning to develop an empirical density that is personalized to each patient. We then transform an intractable monitoring plan optimization with stochastic discharges and health state evolution based on delay‐time models into a weakly coupled network flow model with tractable subproblems after applying a new pruning method that leverages the problem structure. Using this multi‐methodologic approach on two large inpatient datasets, we show that optimal readmission prediction and monitoring plans can identify and mitigate 40–70% of readmissions before they generate an emergency readmission.     

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.     

Veteran's health care: time for a change?

The Department of Veterans Affairs' mission is "to care for him who are shall have borne the battle for his widow and orphan." The Veterans Health Administration comprises 172 hospitals that are the hub of the health care delivery system. It is the largest provider of graduate medical education, and one of the major research organizations in the United States. The medical care budget exceeds $17 billion annually. Most of the persons cared for are not legally entitled to this health care based on service connected disability. The utilization of acute care hospital beds appears excessive when compared to that obtainable with managed care for Medicare or commercial insurance beneficiaries--the cost per member per month is three times higher. There may also be exploitation of the Veterans Administration hospitals by university medical schools. The Veterans Health Administration is a very expensive way to deliver care to entitled service connected veterans. Therefore, it is suggested that privatization be considered as an alternative vehicle for delivering health care.     

Accounting for the transition from inpatient to outpatient surgery

This article reports on some of the factors that have advanced and impeded hospital progress in moving from inpatient to outpatient surgery. Early on, patients, physicians, and hospital administrators all agreed that outpatient surgery had an intuitive appeal. Patients liked it because they didn't have to go in the hospital. Physicians liked it because they could get in and out of the outpatient surgery center more easily than the main hospital operating room. Administrators recognized the inherent appeal of outpatient procedures but were unable or unwilling to switch services from inpatient to outpatient for a variety of reasons. First, empty hospital beds and diminished scope of inpatient operations are a threat to the power of administrators. Moving surgery from inpatient to outpatient settings reduces inhouse operations. Second, reimbursement incentives were definitely in favor of continued inpatient care long after technology was in place for outpatient care. The third and most critical reason was that cost data on outpatient operations were just not available for making decisions on when to move into the outpatient setting. This review of the literature was intended to document the lack of relevant cost-based accounting. Instead, many other factors that more directly slowed progress were encountered. More than anything, this illustrates the erratic course of progress in health care reform.     

Generalized Reduced‐Form Auctions: A Network‐Flow Approach

We develop a network‐flow approach for characterizing interim‐allocation rules that can be implemented by ex post allocations. Our method can be used to characterize feasible interim allocations in general multi‐unit auctions where agents face capacity constraints, both ceilings and floors. Applications include a variety of settings of practical interest, ranging from individual and group‐specific capacity constraints, set‐aside sale, partnership dissolution, and government license reallocation.     

A Hybrid Methodology for Modeling Risk of Adverse Events in Complex Health‐Care Settings

In spite of increased attention to quality and efforts to provide safe medical care, adverse events (AEs) are still frequent in clinical practice. Reports from various sources indicate that a substantial number of hospitalized patients suffer treatment‐caused injuries while in the hospital. While risk cannot be entirely eliminated from health‐care activities, an important goal is to develop effective and durable mitigation strategies to render the system “safer.” In order to do this, though, we must develop models that comprehensively and realistically characterize the risk. In the health‐care domain, this can be extremely challenging due to the wide variability in the way that health‐care processes and interventions are executed and also due to the dynamic nature of risk in this particular domain. In this study, we have developed a generic methodology for evaluating dynamic changes in AE risk in acute care hospitals as a function of organizational and nonorganizational factors, using a combination of modeling formalisms. First, a system dynamics (SD) framework is used to demonstrate how organizational‐level and policy‐level contributions to risk evolve over time, and how policies and decisions may affect the general system‐level contribution to AE risk. It also captures the feedback of organizational factors and decisions over time and the nonlinearities in these feedback effects. SD is a popular approach to understanding the behavior of complex social and economic systems. It is a simulation‐based, differential equation modeling tool that is widely used in situations where the formal model is complex and an analytical solution is very difficult to obtain. Second, a Bayesian belief network (BBN) framework is used to represent patient‐level factors and also physician‐level decisions and factors in the management of an individual patient, which contribute to the risk of hospital‐acquired AE. BBNs are networks of probabilities that can capture probabilistic relations between variables and contain historical information about their relationship, and are powerful tools for modeling causes and effects in many domains. The model is intended to support hospital decisions with regard to staffing, length of stay, and investments in safety, which evolve dynamically over time. The methodology has been applied in modeling the two types of common AEs: pressure ulcers and vascular‐catheter‐associated infection, and the models have been validated with eight years of clinical data and use of expert opinion.     

Global theory and the nature of risk, Part 2. Towards a choice-based model of managed care

Orthodox managed care depends on top-down, command and control techniques to squeeze efficiency out of the system. But for every unit of economic good this approach produces, two or three bad units come as result. The key to moving to an environment where value and efficiency become self-sustaining is to structurally recognize the medicoeconomic reality of medicine: the episode of care. The episode forms a natural unit of analysis that not only renders costs and outcomes information translucent and accessible, but it also forms the natural conduit through which premium dollars can find their optimal value. By bifurcating probability risk from technical risk and allocating them in the ex ante and ex post markets, respectively, health care insurers and providers return to their rightful economic roles, and to their appropriate fiduciary duties. And patients regain some semblance of reasonable sovereignty in managing their own medical affairs.     

Public hospitals: an assessment and plan for the future.

We are currently living in very difficult times for most health care providers. Even though we have always known it, the fact that resources for health care are limited is now abundantly apparent to consumers, health care providers, fiscal intermediaries, government (local, state, and federal), health care planners, and policy makers. Hospitals, especially, are being severely pressured to reduce resource consumption and costs. Conditions that are difficult for nonpublic hospitals are critical for public hospitals in general and nearly fatal for rural public hospitals. Fortunately, nonpublic hospitals are beginning to realize for the first time that their future depends, to a significant degree, on a strong and financially healthy public hospital system. If the public hospital, the hospital of last resort, closes, medically indigent patients will have to be treated in nonpublic hospitals, with the resultant medical, financial, economic, political, and social consequences. Therefore, the importance of public hospitals has to be even better recognized and appreciated and these institutions actively supported in order for the private and total health care systems to be successful.     

Design requirements for health care production control systems

This paper addresses the issue of determining design requirements for production control in health care organizations, with a restriction to the internal production control of hospitals. Hospital management has limited possibilities to control hospital production, as hospital production processes are driven by medical specialists who, however, do not manage that process. We consider therefore the hospital as a virtual organization, consisting of a number of relatively independent businesses in a common framework. Each business unit functions as a focused factory for a range of more or less homogeneous products. Production control principles can be applied to each of these businesses, but not to the system as a whole. A number of elements from classical production control theory can be also applied to health care, i.e. the use of decoupling points, the bottleneck-oriented approach, and the operational control between production and market. However, important factors that need to be considered in health production control are that often specifications on quality are not available at the start of the process, and that there is strong interaction between the patient and the process. Our conclusion is that a dedicated framework for approaching hospital production control is necessary. The specific characteristics of hospital care and its state of production control development are the main arguments for this dedicated framework.     

CPOE is much more than computers

A prominent hospital in Pennsylvania turned to CPOE to help reduce medical errors and improve patient care. Learn what steps hospital officials took to establish a successful CPOE system.     

The ‘optimum’ number of beds in a coronary care unit

A study of the Coronary Care Services provided in the community of Salford has been underway now for more than 3 years. The effects of the introduction of a mobile cardiac ambulance on the community and the hospital are being investigated. This paper reports on one important issue stemming from the present study namely how many beds a CCU ought to develop according to medical and operational criteria. On the one hand an excess of beds results in under-utilisation whilst on the other a lack of beds results in the treatment of some patients in the CCU being truncated.     

CAN WE INFER HOSPITAL QUALITY FROM MEDICAL GRADUATES’ RESIDENCY CHOICES?

This paper uses a novel approach to infer hospital technical quality from revealed preferences over residency programs. Specifically, we use Spanish medical graduates’ residency choices made from 1995 to 2000. We start by estimating a model of medical graduates preferences that controls for hospital, proximity, specialty, and gender effects. We interpret the coefficients on the hospital dummy variables as measures of medical graduates’ preferences over hospitals. Our results show that graduates do indeed discriminate between hospitals and that their preferences correlate with hospital‐specific covariates arguably related to hospital training quality. We then show that preferences from medical graduates are positively and statistically significantly correlated with risk‐adjusted hospital rankings based on five alternative outcome measures. Finally, we construct reputation scores for each hospital using news story counts in three media outlets and find that medical graduates’ preferences are especially valuable for inference of hospital technical quality of care as they do not simply reflect well known reputation.