Service Operations Management, Healthcare Services, Supply Chain Management
Academic Degrees
PhD, Indiana University, 1990
MBA, Indiana University, 1986
BS, Purdue University, 1984
Professional Experience
Indiana University, 1999-Present
Senior Editor at Production and Operations Management, 2015-Present
Indiana University, Chairperson of Doctoral Programs, Kelley School of Business, 2006-2012
Texas A & M University, 1990-1999
Awards, Honors & Certificates
The John & Esther Reese Professor, 2015-Present
W. Michael & William D. Wells Life Sciences Faculty Fellow, 2013-2015
Kimball Faculty Fellow, 2003-2013
Selected Publications
Jola-Sanchez, A., Pedraza-Martinez, A. J., Britto, R., and Bretthauer, K.M. (2016). Effect of Armed Conflicts on Humanitarian Operations: Total Factor Productivity and Efficiency of Rural Hospitals. Journal of Operations Management,45(July), 73-85.
Abstract
We study an important but widely neglected topic in humanitarian operations: armed conflicts. Specifically, this paper empirically analyzes the effect of armed conflicts on the operational performance of first-layer response organizations. Using as a case study the Colombian conflict we investigate the effect of conflict on public rural hospitals' (i) total factor productivity, (ii) efficiency and (iii) efficiency variability. The panel data set (2007e2011) used in this study includes information at the hospital level for 163 hospitals and qualitative data collected from interviews with medical staff from the Colombian Ministry of Health and hospitals in different conflict zones. Our results indicate that armed conflict has a positive effect on total factor productivity, while it has a negative impact on hospital efficiency, and interestingly that efficiency and total factor productivity both increase in post conflict. Finally, the results show that efficiency variability is higher in peace and post-conflict hospitals and lower in medium and severe-conflict hospitals. These results have operations management implications and opportunities for future research related to sourcing decisions, supply chain and workforce flexibility, behavioral impacts on the workforce, and humanitarian response to conflicts.
Helm, J. E., Alaeddini, A., Stauffer, J. M., Bretthauer, K.M., and Skolarus, T. (2016). Reducing Hospital Readmissions by Integrating Empirical Prediction with Resource Optimization. Production and Operations Management,25(2), 233-257.
Abstract
Expensive and frequent hospital readmissions present an increasingly important challenge for healthcare organizations. Knowing when a patient is likely to be readmitted and when to monitor the discharged patient to identify a condition before it triggers readmission are key to treating this issue. In their research, Kurt, Jonathan, and their colleagues develop new methods to generate an estimate of the time to readmit, which can then be used to prepare a post‐discharge monitoring schedule and staffing plan to support monitoring needs. Using a multi-methodologic approach that includes classical prediction modeling, delay time models and network flow models, optimal readmission prediction and monitoring plans can identify and reduce the severity of 40–70% of readmissions before they generate an emergency readmission.
Mahar, S., Wright, P.D., Bretthauer, K.M., and Hill, R. (2014). Optimizing Marketer Costs and Consumer Benefits Across Clicks and Bricks. Journal of the Academy of Marketing Science, 42(5), 619-641.
Bretthauer, K.M., Heese, H. S., Pun, H., and Coe, E. (2011). Blocking in Healthcare Operations: A New Heuristic and an Application. Production and Operations Management, 20(3), 375-391.
Abstract
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.
Salzarulo, P., Bretthauer, K.M., Côté, M., and Schultz, K. (2011). The Impact of Variability and Patient Information on Healthcare System Performance. Production and Operations Management, 20(6), 848-859.
Abstract
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%.
Wright, P. D., and Bretthauer, K.M. (2010). Strategies for Addressing the Nursing Shortage: Coordinated Decision Making and Workforce Flexibility. Decision Sciences, 41(2), 373-40.
Abstract
In this article, we present strategies to help combat the U.S. nursing shortage. Key considerations include providing an attractive work schedule and work environment—critical issues for retaining existing nurses and attracting new nurses to the profession—while at the same time using the set of available nurses as effectively as possible. Based on these ideas, we develop a model that takes advantage of coordinated decision making when managing a flexible workforce. The model coordinates scheduling, schedule adjustment, and agency nurse decisions across various nurse labor pools, each of differing flexibility levels, capabilities, and costs, allowing a much more desirable schedule to be constructed. Our primary findings regarding coordinated decision making and how it can be used to help address the nursing shortage include (i) labor costs can be reduced substantially because, without coordination, labor costs on average are 16.3% higher based on an actual hospital setting, leading to the availability of additional funds for retaining and attracting nurses, (ii) simultaneous to this reduction in costs, more attractive schedules can be provided to the nurses in terms of less overtime and fewer undesirable shifts, and (iii) the use of agency nurses can help avoid overtime for permanent staff with only a 0.7% increase in staffing costs. In addition, we estimate the cost of the shortage for a typical U.S. hospital from a labor cost perspective and show how that cost can be reduced when managers coordinate.
Mahar, S., Bretthauer, K.M., and Venkataramanan, M. A. (2009). The Value of Virtual Pooling in Dual Sales Channel Supply Chains. European Journal of Operational Research, 192(2), 561-575.
Abstract
Recently the most significant growth in online retailing has been attributed to traditional offline retailers extending their brands online. Unfortunately, there is little research addressing the value of better information in retail/e-tail organizations. To fill this gap, this paper examines how investing in the continuous monitoring of online demands and inventory positions can provide economic benefit for companies that handle both in-store and online sales. Specifically, we develop and evaluate two dynamic assignment policies that incorporate real time information to specify which of a firm's e-fulfillment locations will handle each of its Internet sales. Computational results indicate that investing in dynamic assignment capability can reduce system cost (holding, backorder, and transportation) by as much as 8.2% over the optimal static policy. The percentage of sales occurring online plays a critical role in determining the magnitude of the benefit.
Wright, P. D., Bretthauer, K.M., and Côté, M. (2006). Reexamining the Nurse Scheduling Problem: Staffing Ratios and Nursing Shortages. Decision Sciences, 37(1), 39-70.
Abstract
Legislators at the state and national levels are addressing renewed concerns over the adequacy of hospital nurse staffing to provide quality care and ensure patient safety. At the same time, the well-known nursing shortage remains an ongoing problem. To address these issues, we reexamine the nurse scheduling problem and consider how recent health care legislation impacts nursing workforce management decisions. Specifically, we develop a scheduling model and perform computational experiments to evaluate how mandatory nurse-to-patient ratios and other policies impact schedule cost and schedule desirability (from the nurses' perspective). Our primary findings include the following: (i) nurse wage costs can be highly nonlinear with respect to changes in mandatory nurse-to-patient ratios of the type being considered by legislators; (ii) the number of undesirable shifts can be substantially reduced without incurring additional wage cost; (iii) more desirable scheduling policies, such as assigning fewer weekends to each nurse, have only a small impact on wage cost; and (iv) complex policy statements involving both single-period and multiperiod service levels can sometimes be relaxed while still obtaining good schedules that satisfy the nurse-to-patient ratio requirements. The findings in this article suggest that new directions for future nurse scheduling models, as it is likely that nurse-to-patient ratios and nursing shortages will remain a challenge for health care organizations for some time.
Bendoly, E., Blocher, J.D., Bretthauer, K.M., Krishnan, S. and Venkataramanan, M. A. (2005). Online/In-Store Integration and Customer Retention. Journal of Service Research, 7(4), 313-327.
Abstract
Reducing the risks believed to be associated with product availability can be critical to increasing consumer retention rates. This study considers the role that perceptions of channel integration have on such beliefs and their impact on purchasing decisions. Surveys distributed to purchasers of specific goods both online and in-store provide data used in the analysis of these effects. The findings suggest that firms simultaneously managing both online and in-store channels should not only reassess the repercussions of availability failures but also consider efforts that encourage the transparency of channel integration.
Hur, D., Mabert, V., and Bretthauer, K.M. (2004). Real-Time Work Schedule Adjustment Decisions: An Investigation and Evaluation. Production and Operations Management, 13(4), 322-339.
Abstract
Service managers often find that available worker capacity does not match with actual demand during a given day. They then must attempt to modify the planned work schedule to improve service and increase profitability. This study, which defines such a setting as the real-time work schedule adjustment decision, proposes mathematical formulations of the real-time adjustment and develops efficient heuristic approaches for this decision. The study evaluates the relative effectiveness of these heuristics versus experienced service managers, investigates the effect of the degree of schedule adjustment on profitability, and assesses the effect of demand forecast update errors on the performance of the schedule adjustment efforts. First, the results indicate that the computer based heuristics achieve higher profit improvement than experienced managers. Second, there is a trade-off between schedule stability and profitability so that more extensive schedule revisions (efficiency first heuristics) generally result in higher profitability. However, the incremental return on schedule changes is diminishing. Third, we find that active adjustments of work schedules are beneficial as long as the direction of demand change is accurately identified.
Bretthauer, K.M. (2000). Optimal Service and Arrival Rates in Jackson Queueing Networks. Naval Research Logistics, 47(1), 1-17.
Abstract
In this paper we present an algorithm for solving a class of queueing network design problems. Specifically, we focus on determining both service and arrival rates in an open Jackson network of queueing stations. This class of problems has been widely studied and used in a variety of applications, but not well solved due to the difficulty of the resulting optimization problems. As an example, consider the classic application in computer network design which involves determining the minimum cost line capacities and flow assignments while satisfying a queueing performance measure such as an upper limit on transmission delay. Other application areas requiring the selection of both service and arrival rates in a network of queues include the design of communication, manufacturing, and health care systems. These applications yield optimization problems that are difficult to solve because typically they are nonconvex, which means they may have many locally optimal solutions that are not necessarily globally optimal. Therefore, to obtain a globally optimal solution, we develop an efficient branch and bound algorithm that takes advantage of the problem structure. Computational testing on randomly generated problems and actual problems from a health care organization indicate that the algorithm is able to solve realistic sized problems in reasonable computing time on a laptop computer.
Bretthauer, K.M., and Côté, M. (1998). A Model for Planning Resource Requirements in Health Care Organizations. Decision Sciences, 29(1), 243-270.
Abstract
n this paper we present a general model and solution methodology for planning resource requirements (i.e., capacity) in health care organizations. To illustrate the general model, we consider two specific applications: a blood bank and a health maintenance organization (HMO). The blood bank capacity planning problem involves determining the number of donor beds required and determining the size of the nursing and support staff necessary. Capacity must be sufficient to handle the expected number of blood donors without causing excessive donor waiting times. Similar staff, equipment, and service level decisions arise in the HMO capacity planning problem. To determine resource requirements, we develop an optimization/queueing network model that minimizes capacity costs while controlling customer service by enforcing a set of performance constraints, such as setting an upper limit on the expected time a patient spends in the system. The queueing network model allows us to capture the stochastic behavior of health care systems and to measure customer service levels within the optimization framework.
Bretthauer, K.M., and Shetty, B. (1997). Quadratic Resource Allocation with Generalized Upper Bounds. Operations Research Letters, 20(2), 51-57.
Abstract
In this paper we present an algorithm for solving a quadratic resource allocation problem that includes a set of generalized upper bound (GUB) constraints. The problem involves minimizing a quadratic function over one linear constraint, a set of GUB constraints, and bounded variables. GUB constraints, when added to a standard resource allocation problem, can be used to set upper limits on the amount of a resource consumed by one or more subsets of the activities. To solve the problem, we present an efficient algorithm that solves a series of quadratic knapsack subproblems and box constrained quadratic subproblems. Computational results are reported for large-scale problems with as many as 100 000 variables and 1000 constraints. The computational results indicate that our algorithm is up to 4000 times faster than the general purpose nonlinear programming software LSGRG.
Bretthauer, K.M., and Shetty, B. (1995). The Nonlinear Resource Allocation Problem. Operations Research, 43(4), 670-683.
Abstract
In this paper we study the nonlinear resource allocation problem, defined as the minimization of a convex function over one convex constraint and bounded integer variables. This problem is encountered in a variety of applications, including capacity planning in manufacturing and computer networks, production planning, capital budgeting, and stratified sampling. Despite its importance to these and other applications, the nonlinear resource allocation problem has received little attention in the literature. Therefore, we develop a branch-and-bound algorithm to solve this class of problems. First we present a general framework for solving the continuous-variable problem. Then we use this framework as the basis for our branch-and-bound method. We also develop reoptimization procedures and a heuristic that significantly improve the performance of the branch-and-bound algorithm. In addition, we show how the algorithm can be modified to solve nonconvex problems so that a concave objective function can be handled. The general algorithm is specialized for the applications mentioned above and computational results are reported for problems with up to 200 integer variables. A computational comparison with a 0, 1 linearization approach is also provided.
