Reducing Patient Waiting Times for Radiation Therapy and Improving the Treatment Planning Process: a Discrete-event Simulation Model (Radiation Treatment Planning)

Babashov, Vusal; Aivas, I.; Begen, Mehmet A.; Cao, Jeffrey Q.; Rodrigues, George; D’Souza, David; Lock, Michael; Zaric, Gregory S.

doi:10.1016/j.clon.2017.01.039

Cited by 47 publications

(36 citation statements)

References 18 publications

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“…(Hussein et al, 2017) presented effective results reducing overcrowding in a hospital. Similar results were shown by (Babashov et al, 2017) and (Shim & Kumar, 2010) Although these studies present positive results, we found some limitations in them. Some papers did not consider variables and statistical distributions for different workgroups (Reynolds et al, 2011) and processes (Rau et al, 2013).…”

Section: Discrete Event Simulation and Lean In Healthcare Systemssupporting

confidence: 86%

Peer Review #2 of "Lean thinking by integrating with discrete event simulation and design of experiments: an emergency department expansion (v0.3)"

2020

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Background. Many management tools, such as Discrete Event Simulation (DES) and Lean Healthcare, are efficient to support and assist health care quality. In this sense, the study aims at using Lean Thinking (LT) principles combined with DES to plan a Canadian emergency department (ED) expansion and at meeting the demand that comes from small care centers closed. The project`s purpose is reducing the patients' Length of Stay (LOS) in the ED. Additionally, they must be assisted as soon as possible after the triage process. Furthermore, the study aims at determining the ideal number of beds in the Short Stay Unit (SSU). The patients must not wait more than 180 minutes to be transferred. Methods. For this purpose, the hospital decision-makers have suggested planning the expansion, and it was carried out by the simulation and modeling method. The emergency department was simulated by the software FlexSim Healthcare®, and, with the Design of Experiments (DoE), the optimal number of beds, seats, and resources for each shift was determined. Data collection and modeling were executed based on historical data (patients' arrival) and from some databases that are in use by the hospital, from April 1 st , 2017 to March 31 st , 2018. The experiments were carried out by running 30 replicates for each scenario. Results. The results show that the emergency department cannot meet expected demand in the current state. Only 17.2% of the patients were completed treated, and LOS was 2213.7 (average), with a confidence interval of (2131.8-2295.6) minutes. However, after changing decision variables and applying LT techniques, the treated patients' number increased to 95.7% (approximately 600%). Average LOS decreased to 461.2, with a confidence interval of (453.7-468.7) minutes, about 79.0%. The time to be attended after the triage decrease from 404.3 minutes to 20.8 (19.8-21.8) minutes, around 95.0%, while the time to be transferred from bed to the SSU decreased by 60.0%. Moreover, the ED reduced human resources downtime, according to Lean Thinking principles.

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Section: Discrete Event Simulation and Lean In Healthcare Systemssupporting

confidence: 86%

Peer Review #2 of "Lean thinking by integrating with discrete event simulation and design of experiments: an emergency department expansion (v0.3)"

2020

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“…(Hussein et al 2017) presented effective results reducing overcrowding in a hospital. Similar results were shown by (Babashov et al 2017) and (Shim and Kumar 2010) by decreasing patients' waiting time in an emergency department. (Rau et al 2013) and (Uriarte et al 2017) also used DES to reduce patient waiting time in treatment centers and the radiotherapy sector, respectively.…”

Section: Literature Backgroundsupporting

confidence: 80%

Lean healthcare integrated with discrete event simulation and design of experiments: an emergency department expansion

Gabriel

Campos

Magacho

et al. 2019

Preprint

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Background. Discrete Event Simulation (DES) and Lean Healthcare are management tools that are efficient and assist in the quality and efficiency of health services. In this sense, the purpose of the study is to use lean principles jointly with DES to plan the expansion of a Canadian emergency department and to the demand that comes from small closed care centers. Methods. For this, we used simulation and modeling method. We simulated the emergency department in FlexSim Healthcare® software and, with the Design of Experiments (DoE), we defined the optimal number of locations and resources for each shift. Results. The results show that the ED cannot meet expected demand in the current state. Only 17.2% of the patients were completed treated, and the Length of Stay (LOS), on average, was 2213.7, with a confidence interval of (2131.8 - 2295.6) minutes. However, after changing decision variables, the number of treated patients increased to 95.7% (approximately 600%). Average LOS decreased to 461.2, with a confidence interval of (453.7 - 468.7) minutes, about 79.0%. In addition, the study shows that emergency department staff are balanced, according to Lean principles.

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“…The next feature variable that can be used for the split point is the patient's age. The split point based on age can be determined by Equation 6. The split point based on the age of the patient will be obtained based on the results of Equation 6 which has the smallest value.…”

Section: Resultsmentioning

confidence: 99%

Patient Queue Systems in Hospital Using Patient Treatment Time Prediction Algorithm

Handayani

Mustafid

Surarso

2020

KINETIK

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Patient Treatment Time Prediction Algorithm was very important to build an outpatient queue system at the hospital. This study aims to build a system of outpatient queues to predict the waiting time of outpatients in the eye clinic at one of Cirebon hospitals. Patient Treatment Time Prediction algorithm was calculated based on historical data or medical records of patients in the hospital with 120 patient data. The Patient Treatment Time Prediction algorithm was trained by improved Random Forest algorithm for each service and a waiting time for each service. Prediction of waiting time for each patient service was obtained by calculating the consumption of patient care time based on patient characteristics. The waiting time for each service predicted by the trained Patient Treatment Time Prediction algorithm is the total waiting time of patients in the queue for each service. This research resulted in a system that can show the time taken by patients in every service available in the eye clinic. Patient time consumption in each service produced varies according to the patient's condition, in this case based on the patient's gender and age. This research provides benefits in terms of improving performance in each department involved, optimizing human resources, and increasing patient satisfaction. This research can be developed for each department in the hospital.

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Reducing Patient Waiting Times for Radiation Therapy and Improving the Treatment Planning Process: a Discrete-event Simulation Model (Radiation Treatment Planning)

Cited by 47 publications

References 18 publications

Peer Review #2 of "Lean thinking by integrating with discrete event simulation and design of experiments: an emergency department expansion (v0.3)"

Peer Review #2 of "Lean thinking by integrating with discrete event simulation and design of experiments: an emergency department expansion (v0.3)"

Lean healthcare integrated with discrete event simulation and design of experiments: an emergency department expansion

Patient Queue Systems in Hospital Using Patient Treatment Time Prediction Algorithm

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