Operating theatre modelling: integrating social measures

Hospital resource modelling literature is primarily focussed on productivity and efficiency measures. In this paper, our focus is on the alignment of the most valuable revenue factor, the operating room (OR) with the most valuable cost factor, the staff. When aligning these economic and social decisions, respectively, into one sustainable model, simulation results justify the integration of these factors. This research shows that integrating staff decisions and OR decisions results in better solutions for both entities. A discrete event simulation approach is used as a performance test to evaluate an integrated and an iterative model. Experimental analysis show how our integrated approach can benefit the alignment of the planning of the human resources as well as the planning of the capacity of the OR based on both economic related metrics (lead time, overtime, number of patients rejected) and social related metrics (personnel preferences, aversions, roster quality)

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

Integrated Planning in Hospitals: A Review

Efficient planning of scarce resources in hospitals is a challenging task for which a large variety of Operations Research and Management Science approaches have been developed since the 1950s. While efficient planning of single resources such as operating rooms, beds, or specific types of staff can already lead to enormous efficiency gains, integrated planning of several resources has been shown to hold even greater potential, and a large number of integrated planning approaches have been presented in the literature over the past decades. This paper provides the first literature review that focuses specifically on the Operations Research and Management Science literature related to integrated planning of different resources in hospitals. We collect the relevant literature and analyze it regarding different aspects such as uncertainty modeling and the use of real-life data. Several cross comparisons reveal interesting insights concerning, e.g., relations between the modeling and solution methods used and the practical implementation of the approaches developed. Moreover, we provide a high-level taxonomy for classifying different resource-focused integration approaches and point out gaps in the literature as well as promising directions for future research

Applicability of advanced planning and scheduling on surgical blocks

An assessment of the suitability of Advanced Planning and Scheduling software on operating rooms and materials centre is carried out. From data gathering and process mapping a scheduling system is implemented and compared to a private hospital’s approach and information system. A method for compromising schedule assertiveness and productivity is proposed and tested on a simulation setting. Final remarks contend that Advanced Planning and Scheduling is a viable tool for surgical block scheduling on the studied hospital and may improve synchronicity with materials centre, sophisticate rescheduling and enable scenario comparison through scheduling simulation

Developing A Personal Decision Support Tool for Hospital Capacity Assessment and Querying

This article showcases a personal decision support tool (PDST) called HOPLITE, for performing insightful and actionable quantitative assessments of hospital capacity, to support hospital planners and health care managers. The tool is user-friendly and intuitive, automates tasks, provides instant reporting, and is extensible. It has been developed as an Excel Visual Basic for Applications (VBA) due to its perceived ease of deployment, ease of use, Office's vast installed userbase, and extensive legacy in business. The methodology developed in this article bridges the gap between mathematical theory and practice, which our inference suggests, has restricted the uptake and or development of advanced hospital planning tools and software. To the best of our knowledge, no personal decision support tool (PDST) has yet been created and installed within any existing hospital IT systems, to perform the aforementioned tasks. This article demonstrates that the development of a PDST for hospitals is viable and that optimization methods can be embedded quite simply at no cost. The results of extensive development and testing indicate that HOPLITE can automate many nuanced tasks. Furthermore, there are few limitations and only minor scalability issues with the application of free to use optimization software. The functionality that HOPLITE provides may make it easier to calibrate hospitals strategically and/or tactically to demands. It may give hospitals more control over their case-mix and their resources, helping them to operate more proactively and more efficiently.Comment: 33 pages, 11 tables, 17 figure