A discrete event simulation approach for reserving capacity for emergency patients in the radiology department

Luo, Li; Zhang, Yumeng; Qing, Fang; Ding, Hongwei; Shi, Yujun; Guo, Huili

doi:10.1186/s12913-018-3282-8

Cited by 24 publications

(15 citation statements)

References 27 publications

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“…7,8 The large volumes of emergency patients often lead to lengthy wait times of outpatients and inpatients, intensive stress among technicians, which can directly reduce the operational efficiency and healthcare quality. 9 Hence, it is urgently needed to accurately estimate radiology emergency patient flows. Specifically, the radiology emergency patients are mainly from two sources: emergency department (ED) and ward.…”

Section: Introductionmentioning

confidence: 99%

Emergency patient flow forecasting in the radiology department

Zhang

Luo²,

Zhang³

et al. 2020

Health Informatics J

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The accurate forecast of radiology emergency patient flow is of great importance to optimize appointment scheduling decisions. This study used a multi-model approach to forecast daily radiology emergency patient flow with consideration of different patient sources. We constructed six linear and nonlinear models by considering the lag effects and corresponding time factors. The autoregressive integrated moving average and least absolute shrinkage and selection operator (Lasso) were selected from the category of linear models, whereas linear-and-radial support vector regression models, random forests and adaptive boosting were chosen from the category of nonlinear models. The models were applied to 4-year daily emergency visits data in the radiology department of West China Hospital in Chengdu, China. The mean absolute percentage error of six models ranged from 8.56 to 9.36 percent for emergency department patients, whereas it varied from 10.90 to 14.39 percent for ward patients. The best-performing model for total radiology visits was Lasso, which yielded a mean absolute percentage error of 7.06 percent. The arrival patterns of emergency department and total radiology emergency patient flows could be modeled by linear processes. By contrast, the nonlinear model performed best for ward patient flow. These findings will benefit hospital managers in managing efficient patient flow, thus improving service quality and increasing patient satisfaction.

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Section: Introductionmentioning

confidence: 99%

Emergency patient flow forecasting in the radiology department

Zhang

Luo²,

Zhang³

et al. 2020

Health Informatics J

Self Cite

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“…Instead of specifying underlying mathematical formula and likelihood, DES models a network of interdependent discrete events through computer simulation [ 35 , 36 ]. The model is robust and flexible, tolerates detailed constraint settings, and had been widely used in the process planning or optimization within the ED in recent decades [ 37 – 39 ]. Previously, this approach was regarded as time-consuming and expertise required, but had been gradually overcome by the improvement of computational capabilities and commercially available softwares [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

Quantifying Dynamic Flow of Emergency Department (ED) Patient Managements: A Multistate Model Approach

Chaou

Chiu

Pan

et al. 2020

Emergency Medicine International

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Background. Emergency department (ED) crowding and prolonged lengths of stay continue to be important medical issues. It is difficult to apply traditional methods to analyze multiple streams of the ED patient management process simultaneously. The aim of this study was to develop a statistical model to delineate the dynamic patient flow within the ED and to analyze the effects of relevant factors on different patient movement rates. Methods. This study used a retrospective cohort available with electronic medical data. Important time points and relevant covariates of all patients between January and December 2013 were collected. A new five-state Markov model was constructed by an expert panel, including three intermediate states: triage, physician management, and observation room and two final states: admission and discharge. A day was further divided into four six-hour periods to evaluate dynamics of patient movement over time. Results. A total of 149,468 patient records were analyzed with a median total length of stay being 2.12 (interquartile range = 6.51) hours. The patient movement rates between states were estimated, and the effects of the age group and triage level on these movements were also measured. Patients with lower acuity go home more quickly (relative rate (RR): 1.891, 95% CI: 1.881–1.900) but have to wait longer for physicians (RR: 0.962, 95% CI: 0.956–0.967) and admission beds (RR: 0.673, 95% CI: 0.666–0.679). While older patients were seen more quickly by physicians (RR: 1.134, 95% CI: 1.131–1.139), they spent more time waiting for the final state (for admission RR: 0.830, 95% CI: 0.821–0.839; for discharge RR: 0.773, 95% CI: 0.769–0.776). Comparing the differences in patient movement rates over a 24-hour day revealed that patients wait longer before seen by physicians during the evening and that they usually move from the ED to admission afternoon. Predictive dynamic illustrations show that six hours after the patients’ entry, the probability of still in the ED system ranges from 28% in the evening to 38% in the morning. Conclusions. The five-state model well described the dynamic ED patient flow and analyzed the effects of relevant influential factors at different states. The model can be used in similar medical settings or incorporate different important covariates to develop individually tailored approaches for the improvement of efficiency within the health professions.

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“…SimPy (https://simpy.readthedocs.io/en/latest/) is an open access Python-based framework, and it is a wellestablished tool used in industrial and scientific settings including medical resource allocation. [5][6][7] It was chosen as the base for our model because of its versatility, allowing better flexibility with user input. It formed the foundation of the model and enabled it to be applied to other regions and services by varying the geographic, population and other user-defined variables.…”

Section: Model Constructionmentioning

confidence: 99%

Application of a computational model in simulating an endovascular clot retrieval service system within regional Australia

et al. 2021

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Introduction:The global demand for endovascular clot retrieval (ECR) has grown rapidly in recent years creating challenges to healthcare system planning and resource allocation. This study aims to apply our established computational model to predict and optimise the performance and resource allocation of ECR services within regional Australia, and applying data from the state of South Australia as a modelling exercise. Method: Local geographic information obtained using the Google Maps application program interface and real-world data was input into the discrete event simulation model we previously developed. The results were obtained after the simulation was run over 5 years. We modelled and compared a single-centre and two-centre ECR service delivery system. Results: Based on the input data, this model was able to simulate the ECR delivery system in the state of South Australia from the moment when emergency services were notified of a potential stroke patient to potential delivery of ECR treatment. In the model, ECR delivery improved using a two-centre system compared to a one-centre system, as the percentage of stroke patients requiring ECR was increased. When 15% of patients required ECR, the proportion of 'failure to receive ECR' cases for a single-centre system was 17.35%, compared to 3.71% for a two-centre system. Conclusions: Geolocation and resource utilisation within the ECR delivery system are crucial in optimising service delivery and patient outcome. Under the model assumptions, as the number of stroke cases requiring ECR increased, a two-centre ECR system resulted in increased timely ECR delivery, compared to a single-centre system. This study demonstrated the flexibility and the potential application of our DES model in simulating the stroke service within any location worldwide.

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A discrete event simulation approach for reserving capacity for emergency patients in the radiology department

Cited by 24 publications

References 27 publications

Emergency patient flow forecasting in the radiology department

Emergency patient flow forecasting in the radiology department

Quantifying Dynamic Flow of Emergency Department (ED) Patient Managements: A Multistate Model Approach

Application of a computational model in simulating an endovascular clot retrieval service system within regional Australia

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