Towards Increased Use of Discrete-Event Simulation for Hospital Resource Planning

Health care systems in many countries are experiencing a growing demand while their resources remain limited. The discrepancy between demand and capacity creates many problems – long waiting times for treatment, overcrowding in hospital wards, high workload, etc. More efficient delivery of health care services can be achieved by better planning of its resources so that the mismatch between demand and capacity is minimized. Planning health care resources, including hospital resources, is difficult due to system complexity and variability in both resource availability and demand. Discrete-event simulation and other operational research methods can be used for solving planning problems in health care, and have been gaining increased attention from researchers during recent decades. Despite the growing number of academic publications, simulation appears to be less used in health care than in other application areas and only a small proportion of simulation studies is actually implemented. The aim of this thesis is to contribute to increased use of discrete-event simulation in hospital resource planning. The separate studies regarding intensive care unit capacity planning, operating room allocation strategies and the management of emergency patient flow in a radiology department highlight both the possibilities and the requirements for practical application of discrete-event simulation in hospital resource planning. The studies are described in five papers. In the first paper, the relationship between intensive care unit (ICU) occupancy and patient outcomes was investigated and the results showed that risk adjusted mortality was higher in the group of patients who were treated during high levels of occupancy. This indicates that appropriate planning of ICU resources is necessary to avoid adverse effects on patient outcomes. In the second paper, analysis of a relatively simple care chain consisting of two hospital departments – emergency and radiology – revealed a process that was not very well defined and measured. Investigation into data availability uncovered disparate information systems storing incompatible and fragmented data. It suggests that the current degree of process orientation and the current IT infrastructure does not enable efficient use of quantitative process analysis and management tools such as simulation. In the third paper, the value and possibilities of using simulation modelling in hospital resource planning were examined through the development and use of a simulation model for improved operating room time allocation and patient flow in a hospital operating department. The model was initially used for studying overcrowding in a post-anaesthesia care unit. Advanced planning logic implemented in the model enabled evaluation of several different scenarios aiming to improve the utilization of operating room resources. The results showed that it is possible to achieve slightly better and more even resource utilization, as well as provide greater flexibility in scheduling operations. In the fourth paper, a generic ICU model was developed and validated using data from four different hospital ICUs. The model was adapted and calibrated stepwise in order to identify important parameters and their values to obtain a match between model predictions and actual data. The study showed that in presence of high quality data and well defined process logic it is possible to develop a generic ICU simulation model that could provide accurate decision support for planning critical care resources. In the fifth paper, a number of factors that can contribute to successful implementation of simulation results in health care were identified. The timing of the simulation study must be right to support a critical decision, the benefit from implementation should clearly outweigh the cost of making the necessary changes and the model should be thoroughly validated to increase the credibility of the results. Staff involvement in simulation modelling activities, availability of good quality data, as well as proper incentives to improve the system contribute to implementation as well. These findings can help in establishing the conditions for successful implementation in future applications of simulation modelling in health care.