An Application of Value Analysis for Lean Healthcare Management in an Emergency Department

Efe, Burak; Efe, Ömer Faruk

doi:10.1080/18756891.2016.1204117

Cited by 24 publications

(12 citation statements)

References 25 publications

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“…The data analysis improved efficiency in service due to the equipment availability. These results influenced the patients’ perception of value in care [ 43 ].…”

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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“…The data analysis improved efficiency in service due to the equipment availability. These results influenced the patients’ perception of value in care [ 43 ].…”

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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show abstract

“…Bu çalışmada ise bir sonraki adım olan, "hangi destek programı öncelikli olarak KOBİ performansı üzerinde etkindir" sorusu cevaplanmıştır. Günümüzde, ÇKKV alanında farklı yöntemler, çalışmalarda ( [25][26][27][28][29]) etkin şekilde kullanılmaktadır. PROMETHEE ve ORESTE yöntemleri bu çalışmada ele alınan problem tanımı gereği en uygun yöntemler olarak seçilmiştir.…”

Section: Sonuçlar (Conclusion)unclassified

KOBİ’lere sağlanan desteklerin performans etkinlik sıralarının Promethee ve Oreste yöntemleri ile belirlenmesi

Yerlikaya¹,

Arikan²

2016

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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“…The optimization of the system is possible with properly selected management strategies. For instance, the application of lean service tools in the health sector will increase the efficiency of the system and decrease expenditures (Efe and Efe, 2016). Therefore, decision support systems designed to manage health expenditures should predict health expenditure, determine the indicators of health expenditures and optimize the resources (Ministry of Development, 2014).…”

Section: Introductionmentioning

confidence: 99%

Predicting Health Spending in Turkey Using the GPR, SVR, and DT Models

Güleryüz¹

2021

acin

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Rising healthcare costs for countries and the long-term maintainability of this situation are at the center of the political agenda. The steady increase in health spending puts pressure on government budgets, healthcare, and personal patient financing. Policymakers would like to plan reforms to reduce these costs to adapt to problems that may arise. This has led planners to decision support systems and forecasting models. In this paper, three machine learnings algoritms, namely Support Vector Regression (SVR), Decision Tree Regression (DT), and Gaussian Process Regression (GPR) are employed to design a forecasting model for Health Spendings (HS) of Turkey considering various determinants. Gross domestic product per capita, urban population rate, unemployment rate, population ages 65 and above, the life expectancy, the physicians' rate, and the total number of hospital beds are used as inputs. The data set consists of 30 years between 1990-2019, which splits as training and test sets. Developed models were compared considering performance metrics, and the most accurate model was identified. The coefficient of determinations (R 2 ) for SVR, GPR, and DT models are 0.9929, 0.9989, and 0.9611 in the training phase, 0.9536, 0.8944, and 0.1166 in the testing stage, respectively. Therefore, the SVR model has accurate prediction results with the highest R 2 and the least root mean square error values in the testing phase. The study showed that the proposed SVR model reduced RMSE value by 32.02% and 39.66% compared to the GPR and DT models, respectively. Consequently, the Health Spendings of Turkey can be predicted by employing SVR with high accuracy.

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An Application of Value Analysis for Lean Healthcare Management in an Emergency Department

Cited by 24 publications

References 25 publications

A Systematic Review on Lean Applications’ in Emergency Departments

A Systematic Review on Lean Applications’ in Emergency Departments

KOBİ’lere sağlanan desteklerin performans etkinlik sıralarının Promethee ve Oreste yöntemleri ile belirlenmesi

Predicting Health Spending in Turkey Using the GPR, SVR, and DT Models

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