Lean thinking by integrating with discrete event simulation and design of experiments: an emergency department expansion

Gabriel, Gustavo Teodoro; Campos, Afonso Teberga; Magacho, Aline de Lima; Segismondi, Lucas Cavallieri; Vilela, Flávio Fraga; Queiroz, José Antônio de; Montevechi, José Arnaldo Barra

doi:10.7717/peerj-cs.284

Cited by 11 publications

(8 citation statements)

References 41 publications

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“…Other less reported tools included process capability, regressions, and design of experiments (DOE). In fact, DOE has been reported as one of the main tools within six sigma [172]; however, in this research, only one study reported the DOE utilization [37]. This absence might be due to simulation replacing trials in processes where real experimentation is difficult.…”

Section: Lss Tools Utilized In the Interventionsmentioning

confidence: 69%

“…Notably, the dual intervention of improvement approaches (lean, SS, and LSS) and simulation in healthcare has increased in recent years. For instance, lean and system dynamic (SD) simulation for an ICU re-design [35]; just-in-time approach and simulation for inventory management of surgical instruments in operating rooms (OR) [36]; LSS and simulation for reducing the patients' length of stay in the ED [37]; transforming an ED workflow combining lean, machine learning, and simulation [7]; or reducing waiting time through system dynamics and value stream map [27] have not only demonstrated the possibility but also the practicality of dual interventions. Despite the increasing popularity and adoption of LSS and simulation, not all organizations have reported successful outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions

et al. 2022

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In the Industry 4.0 era, healthcare services have experienced more dual interventions that integrate lean and six sigma with simulation modeling. This systematic review, which focuses on evidence-based practice and complies with the PRISMA guidelines, aims to evaluate the effects of these dual interventions on healthcare services and provide insights into which paradigms and tools produce the best results. Our review identified 4018 studies, of which 39 studies met the inclusion criteria and were selected. The predominantly positive results reported in 73 outcomes were mostly related to patient flow: length of stay, waiting time, and turnaround time. In contrast, there is little reported evidence of the impact on patient health and satisfaction, staff wellbeing, resource use, and savings. Discrete event simulation stands out in 74% of the interventions as the main simulation paradigm. Meanwhile, 66% of the interventions utilized lean, followed by lean-six sigma with 28%. Our findings confirm that dual interventions focus mainly on utilization and access to healthcare services, particularly on either patient flow problems or problems concerning the allocation of resources; however, most interventions lack evidence of implementation. Therefore, this study promotes further research and encourages practical applications including the use of Industry 4.0 technologies.

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Section: Lss Tools Utilized In the Interventionsmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions

et al. 2022

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“…The specialists and the modeling team analyzed the simulated model. They agreed that it was as close to the actual patient flow as possible, and then experiments were performed to reduce patients’ waiting time and standardize it [ 63 ].…”

Section: Research Problems Approaches and Applied Toolsmentioning

confidence: 99%

A Systematic Review on Lean Applications’ in Emergency Departments

et al. 2021

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This article presents the state of the art of Lean principles applied in Emergency Departments through a systematic literature review. Our article extends previous work found in the literature to respond to the following questions: (i) What research problems in emergency departments can Lean principles help overcome? (ii) What Lean approaches and tools are used most often in this environment? (iii) What are the results and benefits obtained by these practices? and (iv) What research opportunities appear as gaps in the current state of the art on the subject? A six-step systematic review was performed following the guidance of the PRISMA method. The review analysis identified six main research problems where Lean was applied in Emergency Departments: (i) High Waiting Time and High Length of Hospital Stay; (ii) Health Safety; (iii) Process redesign; (iv) Management and Lessons Learned; (v) High Patient Flow; (vi) Cost Analysis. The six research problems’ main approaches identified were Lean Thinking, Multidisciplinary, Statistics, and Six Sigma. The leading Lean tools and methodologies were VSM, Teamwork, DMAIC, and Kaizen. The main benefits of applying Lean Principles were (a) reductions in waiting time, costs, length of hospital stay, patient flow, and procedure times; and (b) improvements in patient satisfaction, efficiency, productivity, standardization, relationships, safety, quality, and cost savings. Multidisciplinary integration of managers and work teams often yields good results. Finally, this study identifies knowledge gaps and new opportunities to study Lean best practices in healthcare organizations.

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“…However, results show such systems do not fit for stream fluctuation and uneven workload distribution. Semeniuta & Falkman (2019) and Gabriel et al (2020) state that periodic sampling became a dominant strategy for real-time stream processing systems. Although the periodic approach works well, in the distributed environment, one faces such challenges as latency.…”

Section: Literature Surveymentioning

confidence: 99%

An efficient approach for low latency processing in stream data

Bhatt

Thakkar

2021

PeerJ Computer Science

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Stream data is the data that is generated continuously from the different data sources and ideally defined as the data that has no discrete beginning or end. Processing the stream data is a part of big data analytics that aims at querying the continuously arriving data and extracting meaningful information from the stream. Although earlier processing of such stream was using batch analytics, nowadays there are applications like the stock market, patient monitoring, and traffic analysis which can cause a drastic difference in processing, if the output is generated in levels of hours and minutes. The primary goal of any real-time stream processing system is to process the stream data as soon as it arrives. Correspondingly, analytics of the stream data also needs consideration of surrounding dependent data. For example, stock market analytics results are often useless if we do not consider their associated or dependent parameters which affect the result. In a real-world application, these dependent stream data usually arrive from the distributed environment. Hence, the stream processing system has to be designed, which can deal with the delay in the arrival of such data from distributed sources. We have designed the stream processing model which can deal with all the possible latency and provide an end-to-end low latency system. We have performed the stock market prediction by considering affecting parameters, such as USD, OIL Price, and Gold Price with an equal arrival rate. We have calculated the Normalized Root Mean Square Error (NRMSE) which simplifies the comparison among models with different scales. A comparative analysis of the experiment presented in the report shows a significant improvement in the result when considering the affecting parameters. In this work, we have used the statistical approach to forecast the probability of possible data latency arrives from distributed sources. Moreover, we have performed preprocessing of stream data to ensure at-least-once delivery semantics. In the direction towards providing low latency in processing, we have also implemented exactly-once processing semantics. Extensive experiments have been performed with varying sizes of the window and data arrival rate. We have concluded that system latency can be reduced when the window size is equal to the data arrival rate.

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Lean thinking by integrating with discrete event simulation and design of experiments: an emergency department expansion

Cited by 11 publications

References 41 publications

Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions

Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions

A Systematic Review on Lean Applications’ in Emergency Departments

An efficient approach for low latency processing in stream data

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