Operating theatre scheduling with patient recovery in both operating rooms and recovery beds

International audienceThis paper investigates the impact of allowing patient recovery in the operating room when no recovery bed is available. Three types of identical resources are considered: transporters, operating rooms and recovery beds. A fixed number of patients must be planned over a term horizon, usually one or two weeks. The surgery process is modelled as follows: each patient is transported from the ward to the operating theatre. Then the patient visits an operating room for surgery operation and is transferred to the recovery room. If no recovery bed is available, the patient wakes up in the operating room until a bed becomes available. The operating room needs to be cleaned after the patient's departure, before starting another operation. Finally, the patient is transported back to the ward after his recovery. We consider several criteria based on patients' completion times. We propose a Lagrangian relaxation-based method to solve this operating theatre scheduling problem. The efficiency of this method is then validated by numerical experiments. A comprehensive numerical experiment is then performed to quantify the benefit of allowing patient recovery in operating rooms. We show that the benefit is high when the workload of the recovery beds is high

A Business Process Reengineering of the Surgical Path through Lean Technique: The Real Case Study of a Midsize Italian Hospital

This period of pandemic has had important consequences on the flow and the entire organization of any hospital. In particular, the number of accesses to the emergency room has increased, with the consequent urgent need to reorgani ze it quickly. The model proposed in this paper allows to respond to these needs by freeing not only shifts of nursing staff but also surgical staff. This workforce can then be relocated in the emergency room or of the intensive care unit who are in fact at the forefront of emergency management. The aim of this study conducted by the authors is to analyze, inside the context of a midsize Italian hospital, the actual organization model, and then to approach it by Business Process Reengineering (BPR) methodology with the goal to propose a KPI management system that evaluates the efficiency of the whole surgical path. The second objective of the study is to verify if the Operating Rooms (ORs) are properly sized to cover the surgical workload or if it would be necessary to build new ORs (answer to this question is the project mandate by Surgical Wards Chiefs). The last objective is to implement a flexible to cope with emergency situations such as a pandemic. The main result is the approximate maintenance of surgical annual activity (8169 vs 7889). The fewer resources required can be reallocated to deal with emergencies such as the current COVID-19 pandemic. In fact, the surgical shifts decreased during the test case from 464 versus 365 (-15,32%). The rooms’ utilization coefficient rose from 41% to over 52%, whereas the surgeons’ utilization coefficient rose to 61% (with values over 68% for parallel shifts). The results achieved demonstrate that improving efficiency of surgical processes is feasible and a systematic approach allows to respond to new global health challenges

Near real-time bed modelling feasibility study

Hospital bed management is crucial to ensure that patients do not have to wait for the right bed for their care. A simulation model has been developed that mimics the bed management rules applied to the Trauma & Orthopaedic wards of a busy Welsh hospital. The model includes forecasting methodologies to predict the number of emergency admissions, split by gender. The model uses near real-time admission data to see whether patients will be admitted to a given ward on a given day in a 7-day planning horizon. The one-week feasibility pilot study examined the accuracy and usability of the tool. The study has shown that it is possible to correctly predict the short-term processes of a Trauma & Orthopaedic bed management system by accurately forecasting arrivals, using known data and statistical distributions to predict patient length of stay, and applying generic bed management rules to dictate their placement

Scheduling the hospital-wide flow of elective patients

In this paper, we address the problem of planning the patient flow in hospitals subject to scarce medical resources with the objective of maximizing the contribution margin. We assume that we can classify a large enough percentage of elective patients according to their diagnosis-related group (DRG) and clinical pathway. The clinical pathway defines the procedures (such as different types of diagnostic activities and surgery) as well as the sequence in which they have to be applied to the patient. The decision is then on which day each procedure of each patient’s clinical pathway should be done, taking into account the sequence of procedures as well as scarce clinical resources, such that the contribution margin of all patients is maximized. We develop two mixed-integer programs (MIP) for this problem which are embedded in a static and a rolling horizon planning approach. Computational results on real-world data show that employing the MIPs leads to a significant improvement of the contribution margin compared to the contribution margin obtained by employing the planning approach currently practiced. Furthermore, we show that the time between admission and surgery is significantly reduced by applying our models

Planning and scheduling of operating theater under resources constraints: State of the art and future trends and impact on energy consumption

The management of operating theaters is currently the subject of considerable discussion, particularly with regard to the use of human and material resources, which are available in limited quantities. The first part of this paper deals with the management of hospital systems and operating theaters. In the second part, we review the main studies on planning surgical procedures under resources constraints, as well as the different methods for solving planning and scheduling problems in operating theaters. A comparative analysis is carried out in order to identify the fundamental ideas leading to the adoption of a new model capable of meeting the needs and satisfying the different constraints of this management. This article shows that planning and scheduling play a major role in the management of an operating theater, which remains difficult given the multiplicity of determinants involved. In this work, we describe the problem of planning and scheduling operating theaters according to several authors, aiming to evaluate and improve existing operating programs to make them feasible and of good quality. The depletion of the world’s available energy resources requires the construction of hospital buildings that respect the environment and take into account energy efficiency while meeting different needs

ADAPTIVE MULTI-OBJECTIVE OPERATING ROOM PLANNING WITH STOCHASTIC DEMAND AND CASE TIMES

The operating room (OR) is accountable for most hospital admissions and is one of the most cost and work intensive areas in the hospital. From recent trends, we discover an unexpected parallel increase in expenditure and waiting time. Therefore, improving OR planning has become obligatory, particularly regarding utilization, and service level. Significant challenges in OR planning are the high variations in demand, processing times of surgical specialties, the trade-off between the objectives, and control of OR performance in long-term. Our model provides OR configurations at a strategical level of OR planning to minimize the tradeoff between the utilization and service level accounting for variation in both demand and processing times of surgical specialties. An adaptive control scheme is proposed to aid OR managers to maintain the OR performance within the prescribed controllable limits. Our model is validated using a simulation of demand and processing time data of surgical services at University of Kentucky Health Care

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

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles 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

A permutation flowshop model with time-lags and waiting time preferences of the patients

The permutation flowshop is a widely applied scheduling model. In many real-world applications of this model, a minimum and maximum time-lag must be considered between consecutive operations. We can apply this model to healthcare systems in which the minimum time-lag could be the transfer times, while the maximum time-lag could refer to the number of hours patients must wait. We have modeled a MILP and a constraint programming model and solved them using CPLEX to find exact solutions. Solution times for both methods are presented. We proposed two metaheuristic algorithms based on genetic algorithm and solved and compared them with each other. A sensitivity of analysis of how a change in minimum and maximum time-lags can impact waiting time and Cmax of the patients is performed. Results suggest that constraint programming is a more efficient method to find exact solutions and changes in the values of minimum and maximum time-lags can impact waiting times of the patients and Cmax significantly