A configurable computer simulation model for reducing patient waiting time in oncology departments

Using stochastic simulation modelling to study occupancy levels of decentralised admission avoidance units in Norway

“…Agents may be individuals (e.g., patients or healthcare personnel), households, organisations, or even health care resources. Corsini et al utilised agent-based modelling to simulate the impact of reconfigured pathways on patient flows through an oncology department, system efficiency and the time patients spent waiting (Corsini et al, 2022 ). When determining when to use different types of simulation, agent-based simulation “… may be particularly useful in modelling systems where the decisions of, and interactions between, individual agents and their actions are likely to affect those aspects of overall system behaviour under study”.…”

Predicting Hospital Admissions to Reduce Crowding in the Emergency Departments

“…These activities and relationships are often complex and multidisciplinary, making them a major focus of study as not only the development and application of advanced analytical methods but also their optimization. Both of these can result in improvements in many different areas simultaneously, such as the reduction in administrative costs [2], the optimization of hospital resources [1], and the reduction in patient waiting times or a greater degree of service customization [3]. This results in a higher quality service that is appreciated by both institutions and users alike.…”

“…2022, 12, x FOR PEER REVIEW 3 of 17 precious resources such as time, space, and medical attention, which should instead be used in other ED tasks, and contribute to the overcrowding of the service [12]. Over the past few years, various solutions have been proposed to resolve this issue, the most significant of which is the simplification of the admission procedure [13], the addition of more doctors to the admission process of both hospitals and emergency department [3,14,15], an optimized process of early hospital discharges [16,17], or the creation of an ED dependent unit for short-length stays [18]. However, none of them appears to be easily implementable without significant economic investment, or sustainable in a situation in which the number of visits to emergency departments will continue to rise.…”

“…The first step of this paper was to identify data requirements followed by preprocessing data. Then, a gradient boosting machine was used to train and test the predictive models in R. After that, variable importance for each of the models was analyzed, In spite of some studies [3,13,19] showing that patient visit data can improve the predictive performance slightly when compared with a model predicting admissions at the time of arrival, this study aims at predicting admissions using information readily available at the moment of triage, immediately following the arrival of the patient at the ED by analyzing data from more than 60 different centers in the Integral Healthcare System for Public Use in Catalonia (SISCAT). Triage is the first stage of an ED visit and its objective is to assess in a regulated, validated, and reproducible way, and as accurately as possible, the level of urgency of a visit to organize patients in recognizable groups to prioritize the sickest ones [20].…”

Staff pooling in healthcare systems – results from a mixed-methods study

“…A staff pool is a general capacity that can be allocated to parts of the system where the existing workload and demand for capacity is unusually high (Gutjahr & Rauner, 2007 ; Hopp & Lovejoy, 2013 ; Kuntz et al, 2015 ; Vanberkel et al, 2012 ). Like other OR approaches in healthcare (Brailsford et al, 2021 ; Corsini et al, 2022 ; Lamé et al, 2022 ; Wing & Vanberkel, 2021 ; Zyl-Cillié et al, 2022 ), staff pooling is a planning method that can be used to improve utilisation of current resources and for managing bottlenecks in the system (e.g., doctors and specialist nurses). It is a well-established tool in healthcare systems (Cattani & Schmidt, 2005 ; Dziuba-Ellis, 2006 ; Fagefors et al, 2020 ; Kuntz et al, 2015 ; Mahar et al, 2011 ; Terwiesch et al, 2011 ).…”