Lean thinking to improve emergency department throughput at AORN Cardarelli hospital

Improta, Giovanni; Romano, Maria; Cicco, Maria Vincenza Di; Ferraro, Antonino; Borrelli, Anna; Verdoliva, Ciro; Triassi, Maria; Cesarelli, Mario

doi:10.1186/s12913-018-3654-0

Cited by 79 publications

(83 citation statements)

References 29 publications

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“…The proper focus on the value added to patients and the well-being improvements among hospital workers, created by a well-designed sustainable continuous improvement system, would certainly represent an enormous contribution to society. (Clark, Moller, & O'Brien, 2014) CS A10 N/A I3;I4 N/A (Colldén, Gremyr, Hellström, & Sporraeus, 2017) CS A17 N/A N/A N/A (Costa, Filho, Rentes, Bertani, & Mardegan, 2017) (Hwang, Hwang, & Hong, 2014) CS A2;A6;A17 T42; T29; T28; T20 N/A N/A (Improta et al, 2018) AR A1 T41;T30; T27; T1; T8 I1;I3 N/A (Lot et al, 2018) AR A4 T41; T29; T13; T4; T31; T30 I2;I3;I5 N/A (Mannon, 2014) CS A2;A3; T29; T27; T13 I1 N/A (Matt, Rauch, & Franzellin, 2015) AR A2 N/A I2;I3;I5 D3 (Mehdi & Al Bahrani, 2017) CS A11 T39; T8; T17 I1;I2;I3;I4 N/A (Miller & Chalapati, 2015) CS A4 T41; T30 I3 D1;D3 (Nanda, Rybkowski, Pati, & Nejati, 2017) CS A3 T31; T22 N/A N/A (Narayanamurthy, Gurumurthy, & Lankayil, 2018) CS A4;A14 T33; T2; T32; T49; T10 N/A N/A (Nayar, Ojha, Fetrick, & Nguyen, 2016) AR A2 T12; T44; T36 I3;I4 D5 (P. Simons et al, 2017) CS A11 T41;T30; T22; T17 N/A N/A (Pineda Dávila & Tinoco González, 2015) AR A7 T30; T4; T42 I1;I2;I4;I5 N/A (Poksinska, Fialkowska-Filipek, & Engström, 2017) (Fournier & Jobin, 2018) CS…”

Section: Discussion Recommendations and Conclusionmentioning

confidence: 99%

Implementation of lean in health care environments: an update of systematic reviews

Lima

Dinis‐Carvalho

Souza

et al. 2020

IJLSS

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Purpose Even though the implementation of lean in health care environments is relatively recent, it has been receiving a lot of attention in recent years. Partly because of the fact that it is a recent field of practise and research and partly because the number of works developed in this field has grown rapidly, it is important to frequently update the perspectives on this field of investigation. Thus, this study aims to review the implementation of lean tools and techniques applied to hospital organizational areas in a five-year period, between 2014 and 2018, complementing some of the most relevant reviews already published. The most important criteria such as tools, methods and principles, hospital areas intervened, improvements and difficulties were assessed and quantified. Design/methodology/approach As starting point for this systematic literature review (SLR), a set of selected pre-existing review publications was used to support the current study and as the ground base for the expansion of the studies about lean health care. The current study contemplated 114 articles from a five-year period between 2014 and 2018. A subset of 58 of these articles was critically assessed to understand the application of lean tools and methods in different hospital areas. Findings The thorough analysis of selected articles show a lack of works in continuous improvement approaches when compared to the application of production organization methods, visual management and diagnosing and problem-solving tools. The reported improvement results demonstrate alignment with the principles and foundations of lean philosophy, but such results are presented in isolated initiatives and without robust evidence of long-term maintenance. Moreover, this study shows an evolution in the number of articles referring to lean implementation in hospital areas, but in its great majority, such articles report isolated implementations in different areas, not spreading those for the global organization. Thus, some of the main recommendations are the need to implement studies on complete flows of patients, drugs and materials, instead of isolated initiatives and strive to promote the cultural change of hospitals through structural changes, following new visions and strategic objectives, supported by real models of continuous structural and sustained improvement. Originality/value The current study develops a new perspective of the articles published under the thematic of lean health care, published in a recent period of five years, which are not completely covered by other works. Additionally, it explicitly applied, in an innovative way, an approach that used a set of previous reviews as the starting point for this SLR. In this way, it integrates approaches and categories from different SLRs, creating a framework of analysis that can be used by future researchers. Finally, it shows the most recent implementations of lean health care, exposing the current trends, improvements and also the main gaps.

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Section: Discussion Recommendations and Conclusionmentioning

confidence: 99%

Implementation of lean in health care environments: an update of systematic reviews

Lima

Dinis‐Carvalho

Souza

et al. 2020

IJLSS

View full text Add to dashboard Cite

show abstract

“…2 The rise of chronic diseases, alongside the need to improve the effectiveness, equity, and sustainability of healthcare systems, has required innovation among care services. [3][4][5] Many approaches have been proposed for the treatment and management of chronic diseases, 6,7 and innovative methodologies have been introduced to reduce waste in healthcare processes [8][9][10] and support decision-makers in the evaluation of technologies and in the choice of appropriate therapies. 11,12 Healthcare is rapidly generating big data (so-called both for its volume and its complexity and range) that can be exploited to derive new knowledge regarding patient care, compliance, and various regulatory requirements.…”

Section: Introductionmentioning

confidence: 99%

Linear discriminant analysis and principal component analysis to predict coronary artery disease

Ricciardi

Valente

Edmund

et al. 2020

Health Informatics J

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Coronary artery disease is one of the most prevalent chronic pathologies in the modern world, leading to the deaths of thousands of people, both in the United States and in Europe. This article reports the use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia. Overall, 22 features are extracted, and linear discriminant analysis is implemented twice through both the Knime analytics platform and R statistical programming language to classify patients as either normal or pathological. The former of these analyses includes only classification, while the latter method includes principal component analysis before classification to create new features. The classification accuracies obtained for these methods were 84.5 and 86.0 per cent, respectively, with a specificity over 97 per cent and a sensitivity between 62 and 66 per cent. This article presents a practical implementation of traditional data mining techniques that can be used to help clinicians in decision-making; moreover, principal component analysis is used as an algorithm for feature reduction.

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“…To support hospital managers to cope with the third and the fourth groups of factors affecting the ED waiting times, some scholars have proposed operation or lean management approaches [42,43]. Whilst to cope with the first two groups of factors, many scholars suggested to rearrange primary care or healthcare pathways.…”

Section: Introductionmentioning

confidence: 99%

Waiting times in emergency departments: a resource allocation or an efficiency issue?

Vainieri

Panero

Coletta

2020

BMC Health Serv Res

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Background: In recent years, the flow of patients to the Emergency Departments (ED) of Western countries has steadily increased, thus generating overcrowding and extended waiting times. Scholars have identified four main causes for this phenomenon, related to: continuity of primary care services; availability of specific clinical pathways for chronic patients; ED's personnel endowment; organization of the ED. This study aims at providing a logical diagnostic framework to support managers in investigating specific solutions to be applied to their EDs to cope with high ED waiting times. The framework is based on the ED waiting times and ED admission rate matrix. It was applied to the Tuscan EDs as illustrative example. Methods: To provide the factors to be analyzed once the EDs are positioned into the matrix, a list of issues has been identified. The matrix was applied to Tuscan EDs. Data were collected from the Tuscan performance evaluation system, integrated with specific data on Tuscan EDs' personnel. The Tuscan EDs matrix, the descriptive statistics for each quadrant and the Spearman's rank correlation analysis among waiting times, admission rates and a set of performance indicators were conducted to help managers to read the phenomena that they need to investigate. Results: The combined reading of the correlations and waiting times-admission rates matrix shows that there are no optimal rules for all the EDs in managing admission rates and waiting times, but solutions have to be found considering mixed and personalized strategies. Conclusions: The waiting times-admission rates matrix provides a tool able to support managers in detecting the problems related to the management of ED services. In particular, using this matrix, healthcare managers could be facilitated in the identification of possible solutions for their specific situation.

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Lean thinking to improve emergency department throughput at AORN Cardarelli hospital

Cited by 79 publications

References 29 publications

Implementation of lean in health care environments: an update of systematic reviews

Implementation of lean in health care environments: an update of systematic reviews

Linear discriminant analysis and principal component analysis to predict coronary artery disease

Waiting times in emergency departments: a resource allocation or an efficiency issue?

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