Suitability and managerial implications of a Master Surgical Scheduling approach

Abstract: Operating room (OR) planning and scheduling is a popular and challenging subject within the operational research applied to health services research (ORAHS). However, the impact in practice is very limited. The organization and culture of a hospital and the inherent characteristics of its processes impose specific implementation issues that affect the success of planning approaches. Current tactical OR planning approaches often fail to account for these issues.Master surgical scheduling (MSS) is a promising approach for hospitals to optimize resource utilization and patient flows. We discuss the pros and cons of MSS and compare MSS with centralized and decentralized planning approaches. Finally, we address various implementation issues of MSS and discuss its suitability for hospitals with different organizational foci and culture

Levelled bed occupancy and controlled waiting lists using Master surgical schedules

Scheduling surgical patients is one of the complex organizational tasks hospitals face daily. Master surgical scheduling is one way to optimize utilization of scarce resources and to create a more predictable outflow from the operating room towards subsequent hospital departments. The paper addresses two aims. First, we investigate the effect of the length of the planning horizon and other planning parameters in a master surgical scheduling approach on patients ́ waiting time, schedule stability and hospital efficiency. Second, the master surgical scheduling approach is compared with a standard operating room planning approach on levelled bed occupancy. The assignment of patients to a master surgical schedule is carefully described. Using real case data from a regional hospital i

Balancing control and autonomy in master surgery scheduling: benefits of ICU quotas for recovery units

When scheduling surgeries in the operating theater, not only the resources within the operating theater have to be considered but also those in downstream units, e.g., the intensive care unit and regular bed wards of each medical specialty. We present an extension to the master surgery schedule, where the capacity for surgeries on ICU patients is controlled by introducing downstream-dependent block types – one for both ICU and ward patients and one where surgeries on ICU patients must not be performed. The goal is to provide better control over post-surgery patient flows through the hospital while preserving each medical specialty’s autonomy over its operational surgery scheduling. We propose a mixed-integer program to determine the allocation of the new block types within either a given or a new master surgery schedule to minimize the maximum workload in downstream units. Using a simulation model supported by seven years of data from the University Hospital Augsburg, we show that the maximum workload in the intensive care unit can be reduced by up to 11.22% with our approach while maintaining the existing master surgery schedule. We also show that our approach can achieve up to 79.85% of the maximum workload reduction in the intensive care unit that would result from a fully centralized approach. We analyze various hospital setting instances to show the generalizability of our results. Furthermore, we provide insights and data analysis from the implementation of a quota system at the University Hospital Augsburg. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10729-021-09588-8

Applying Mathematical Models to Surgical Patient Planning

On a daily basis surgeons, nurses, and managers face cancellation of surgery, peak demands on wards, and overtime in operating rooms. Moreover, the lack of an integral planning approach for operating rooms, wards, and intensive care units causes low resource utilization and makes patient flows unpredictable. An ageing population and advances in medicine are putting the available healthcare budget under great pressure. Under these circumstances, hospitals are seeking innovative ways of providing optimal quality at the lowest costs. This thesis provides hospitals with instruments for optimizing surgical patient planning. We describe a cyclic and integrated operating room planning approach, called master surgical scheduling, and models for efficient planning of emergency operations. Application of these instruments enables the simultaneous optimization of the utilization of operating rooms, ward and intensive care units. Moreover, iteratively executing a master schedule of surgical case types provides steady and thus more predictable patient flows in hospitals. The approach is generic and so can be implemented taking account of specific characteristics of individual hospitals. Prerequisites for successful implementation of logistical models in hospitals comprise sufficient room for last-minute changes as well as keeping the ultimate responsibility for individual patient scheduling with medical specialists. Both are satisfied in the master surgical scheduling approach which has already been successfully implemented in hospitals

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 is 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

Control architectures for Industrial Additive Manufacturing Systems

Industrial Additive Manufacturing technologies are increasingly being employed in manufacturing environments, yet there has been little consideration of these in terms of manufacturing systems. This paper explores the important concept of control for Industrial Additive Manufacturing Systems, drawing upon data achieved in twelve case studies to identify four feasible control architectures. Using an abductive approach, this paper contributes to a recognized knowledge gap in operations and manufacturing management research

Models, algorithms and performance analysis for adaptive operating room scheduling

The complex optimisation problems arising in the scheduling of operating rooms have received considerable attention in recent scientific literature because of their impact on costs, revenues and patient health. For an important part, the complexity stems from the stochastic nature of the problem. In practice, this stochastic nature often leads to schedule adaptations on the day of schedule execution. While operating room performance is thus importantly affected by such adaptations, decision-making on adaptations is hardly addressed in scientific literature. Building on previous literature on adaptive scheduling, we develop adaptive operating room scheduling models and problems, and analyse the performance of corresponding adaptive scheduling policies. As previously proposed (fully) adaptive scheduling models and policies are infeasible in operating room scheduling practice, we extend adaptive scheduling theory by introducing the novel concept of committing. Moreover, the core of the proposed adaptive policies with committing is formed by a new, exact, pseudo-polynomial algorithm to solve a general class of stochastic knapsack problems. Using these theoretica