Erwin W. Hans

Taxonomic classification of planning decisions in health care: a structured review of the state of the art in OR/MS

We provide a comprehensive overview of the typical decisions to be made in resource capacity planning and control in health care, and a structured review of relevant articles from the field of Operations Research and Management Sciences (OR/MS) for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making.

An Exact Approach for Relating Recovering Surgical Patient Workload to the Master Surgical Schedule

No other department influences the workload of a hospital more than the Department of Surgery and in particular, the activities in the operating room. These activities are governed by the master surgical schedule (MSS), which states which patient types receive surgery on which day. In this paper, we describe an analytical approach to project the workload for downstream departments based on this MSS. Specifically, the ward occupancy distributions, patient admission/discharge distributions and the distributions for ongoing interventions/treatments are computed. Recovering after surgery requires the support of multiple departments, such as nursing, physiotherapy, rehabilitation and long-term care. With our model, managers from these departments can determine their workload by aggregating tasks associated with recovering surgical patients. The model, which supported the development of a new MSS at the Netherlands Cancer Institute–Antoni van Leeuwenhoek Hospital, provides the foundation for a decision support tool to relate downstream hospital departments to the operating room.

A Framework for Healthcare Planning and Control

Rising expenditures spur healthcare organizations to organize their processes more efficiently and effectively. Unfortunately, healthcare planning and control lags behind manufacturing planning and control. We analyze existing planning and control concepts or frameworks for healthcare operations management and find that they do not address various important planning and control problems. We conclude that they only focus on hospitals and are too narrow, focusing on a single managerial area, such as resource capacity planning, or ignoring hierarchical levels. We propose a modern framework for healthcare planning and control that integrates all managerial areas in healthcare delivery operations and all hierarchical levels of control, to ensure completeness and coherence of responsibilities for every managerial area. The framework can be used to structure the various planning and control functions and their interaction. It is applicable to an individual department, an entire healthcare organization, and to a complete supply chain of cure and care providers. The framework can be used to identify and position various types of managerial problems, to demarcate the scope of organization interventions and to facilitate a dialogue between clinical staff and managers.

A Simulation Model for Determining the Optimal Size of Emergency Teams on Call in the Operating Room at Night

BACKGROUND: Hospitals that perform emergency surgery during the night (e.g., from 11:00 pm to 7:30 am) face decisions on optimal operating room (OR) staffing. Emergency patients need to be operated on within a predefined safety window to decrease morbidity and improve their chances of full recovery. We developed a process to determine the optimal OR team composition during the night, such that staffing costs are minimized, while providing adequate resources to start surgery within the safety interval. METHODS: A discrete event simulation in combination with modeling of safety intervals was applied. Emergency surgery was allowed to be postponed safely. The model was tested using data from the main OR of Erasmus University Medical Center (Erasmus MC). Two outcome measures were calculated: violation of safety intervals and frequency with which OR and anesthesia nurses were called in from home. We used the following input data from Erasmus MC to estimate distributions of all relevant parameters in our model: arrival times of emergency patients, durations of surgical cases, length of stay in the postanesthesia care unit, and transportation times. In addition, surgeons and OR staff of Erasmus MC specified safety intervals. RESULTS: Reducing in-house team members from 9 to 5 increased the fraction of patients treated too late by 2.5% as compared to the baseline scenario. Substantially more OR and anesthesia nurses were called in from home when needed. CONCLUSION: The use of safety intervals benefits OR management during nights. Modeling of safety intervals substantially influences the number of emergency patients treated on time. Our case study showed that by modeling safety intervals and applying computer simulation, an OR can reduce its staff on call without jeopardizing patient safety.

A scenario based approach for flexible resource loading under uncertainty

Order acceptance decisions in manufacture-to-order environments are often made based on incomplete or uncertain information. To quote reliable due dates in order processing, manage resource capacity adequately and take into account uncertainty, the paper presents and analyses models and tools for more robust resource loading. We refer to the problem as flexible resource loading under uncertainty. We propose a scenario-based solution approach that can deal with a wide range of uncertainty types. The approach is based on an MILP to find a plan with minimum expected costs over all relevant scenarios. To solve this MILP, we propose an exact branch-and-price algorithm. Further, we propose a much faster improvement heuristic based on an LP (linear programming) approximation. A disadvantage of the scenario-based MILP, is that a large number of scenarios may make the model intractable. We therefore propose an approximate approach that uses the aforementioned solution techniques and only a sample of all scenarios. Computational experiments show that, especially for instances with sufficient slack, solutions obtained with deterministic techniques that only use the expected scenario can be significantly improved with respect to their expected costs (i.e. robustness). We also show that for large instances, our heuristics outperform the exact approach given a maximum computation time as a stopping criterion. Moreover, it turns out that using a small sample of selected scenarios generally yields better results than using all scenarios.

Tactical resource allocation and elective patient admission planning in care processes

Tactical planning of resources in hospitals concerns elective patient admission planning and the intermediate term allocation of resource capacities. Its main objectives are to achieve equitable access for patients, to meet production targets/to serve the strategically agreed number of patients, and to use resources efficiently. This paper proposes a method to develop a tactical resource allocation and elective patient admission plan. These tactical plans allocate available resources to various care processes and determine the selection of patients to be served that are at a particular stage of their care process. Our method is developed in a Mixed Integer Linear Programming (MILP) framework and copes with multiple resources, multiple time periods and multiple patient groups with various uncertain treatment paths through the hospital, thereby integrating decision making for a chain of hospital resources. Computational results indicate that our method leads to a more equitable distribution of resources and provides control of patient access times, the number of patients served and the fraction of allocated resource capacity. Our approach is generic, as the base MILP and the solution approach allow for including various extensions to both the objective criteria and the constraints. Consequently, the proposed method is applicable in various settings of tactical hospital management.

Accounting for Inpatient Wards when developing Master Surgical Schedules

BACKGROUND: As the demand for health care services increases, the need to improve patient flow between departments has likewise increased. Understanding how the master surgical schedule (MSS) affects the inpatient wards and exploiting this relationship can lead to a decrease in surgery cancellations, a more balanced workload, and an improvement in resource utilization. We modeled this relationship and used the model to evaluate and select a new MSS for a hospital. METHODS: An operational research model was used in combination with staff input to develop a new MSS. A series of MSSs were proposed by staff, evaluated by the model, and then scrutinized by staff. Through iterative modifications of the MSS proposals (i.e., the assigned operating time of specialties), insight is obtained into the number, type, and timing of ward admissions, and how these affect ward occupancy. RESULTS: After evaluating and discussing a number of proposals, a new MSS was chosen that was acceptable to operating room staff and that balanced the ward occupancy. After implementing the new MSS, a review of the bed-use statistics showed it was achieving a balanced ward occupancy. The model described in this article gave the hospital the ability to quantify the concerns of multiple departments, thereby providing a platform from which a new MSS could be negotiated. CONCLUSION: The model, used in combination with staff input, supported an otherwise subjective discussion with quantitative analysis. The work in this article, and in particular the model, is readily repeatable in other hospitals and relies only on readily available data.

Master surgery scheduling with consideration of multiple downstream units

We consider a master surgery scheduling (MSS) problem in which block operating room (OR) time is assigned to different surgical specialties. While many MSS approaches in the literature consider only the impact of the MSS on operating theater and operating staff, we enlarge the scope to downstream resources, such as the intensive care unit (ICU) and the general wards required by the patients once they leave the OR. We first propose a stochastic analytical approach, which calculates for a given MSS the exact demand distribution for the downstream resources. We then discuss measures to define downstream costs resulting from the MSS and propose exact and heuristic algorithms to minimize these costs.

A Norm Utilisation for Scarce Hospital Resources: Evidence from Operating Rooms in a Dutch University Hospital

BackgroundUtilisation of operating rooms is high on the agenda of hospital managers and researchers. Many efforts in the area of maximising the utilisation have been focussed on finding the holy grail of 100% utilisation. The utilisation that can be realised, however, depends on the patient mix and the willingness to accept the risk of working in overtime.Materials and methodsThis is a mathematical modelling study that investigates the association between the utilisation and the patient mix that is served and the risk of working in overtime. Prospectively, consecutively, and routinely collected data of an operating room department in a Dutch university hospital are used. Basic statistical principles are used to establish the relation between realistic utilisation rates, patient mixes, and accepted risk of overtime.ResultsAccepting a low risk of overtime combined with a complex patient mix results a low utilisation rate. If the accepted risk of overtime is higher and the patient mix is less complex, the utilisation rate that can be reached is closer to 100%.ConclusionBecause of the inherent variability of health-care processes, the holy grail of 100% utilisation is unlikely to be found. The method proposed in this paper calculates a realistic benchmark utilisation that incorporates the patient mix characteristics and the willingness to accept risk of overtime.

Simulation framework to analyze operating room release mechanisms

The block time (BT) schedule, which allocates Operating Rooms (ORs) to surgical specialties, causes inflexibility for scheduling outside the BT, which negatively affects new surgeons, new specialties, and specialties that have fluctuation in the number of surgeries. For this inflexibility, we introduce the concept of releasing ORs, and present a generic simulation and evaluation framework that can be used by hospitals to evaluate various release mechanisms. The simulation and evaluation framework is illustrated by a case study at Vanderbilt Medical Center and University (VUMC) in Nashville. The results show that introducing a release policy has benefits in decreasing the number of unscheduled patients and decreasing access time, without affecting the specialties originally assigned to the released rooms.