Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study

Improta, Giovanni; Borrelli, Anna; Triassi, Maria

doi:10.3390/ijerph19095215

Cited by 33 publications

(11 citation statements)

References 65 publications

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“…Several strategies have become widespread to support healthcare managers in increasing knowledge from data. Health technology assessment (Improta et al, 2009;Improta et al, 2012), smart technologies (Apicella et al, 2021;Arpaia et al, 2020;Rocco et al, 2020;Angrisani et al, 2020;Bernasconi et al, 2020), specific care path (Improta et al, 2021;Improta et al, 2020), data analysis (Ponsiglione et al, 2022;Improta et al, 2014;Ponsiglione et al, 2021a;Cesarelli et al, 2012;Cortesi et al, 2019) and machine learning (Improta et al, 2022;Ponsiglione et al, 2021b) are just a few examples.…”

Section: Lean Application In Healthcarementioning

confidence: 99%

Lean Six Sigma to reduce the acute myocardial infarction mortality rate: a single center study

Rosa

Trunfio

Marolla

et al. 2023

TQM

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PurposeCardiovascular diseases are the leading cause of death worldwide. In Italy, acute myocardial infarction (AMI) is a major cause of hospitalization and healthcare costs. AMI is a myocardial necrosis event caused by an unstable ischemic syndrome. The Italian government has defined an indicator called “AMI: 30-day mortality” to assess the quality of the overall care pathway of the heart attacked patient. In order to guarantee high standards, all hospitals had to implement techniques to increase the quality of care pathway. The aim of the paper is to identify the root cause and understand the mortality rate for AMI and redesign the patient management process in order to improve it.Design/methodology/approachA Lean Six Sigma (LSS) approach was used in this study to analyze the patient flow in order to reduce 30-days mortality rate from AMI registered by Complex Operative Unit (COU) of Cardiology of an Italian hospital. Value stream mapping (VSM) and Ishikawa diagrams were implemented as tools of analysis.FindingsProcess improvement using LSS methodology made it possible to reduce the overall times from 115 minutes to 75 minutes, with a reduction of 35%. In addition, the corrective actions such as the activation of a post-discharge outpatient clinic and telephone contacts allowed the 30-day mortality rate to be lowered from 16% before the project to 8% after the project. In this way, the limit value set by the Italian government was reached.Research limitations/implicationsThe limitation of the study is that it is single-centered and was applied to a facility with a limited number of cases.Practical implicationsThe LSS approach has brought significant benefits to the process of managing patients with AMI. Corrective actions such as the activation of an effective shared protocol or telephone interview with checklist can become the gold standard in reducing mortality. The limitation of the study is that it is single-centered and was applied to a facility with a limited number of cases.Originality/valueLSS, applied for the first time to the management of cardiovascular diseases in Italy, is a methodology which has proved to be strategic for the improvement of healthcare process. The simple solutions implemented could serve as a guide for other hospitals to pursue the national AMI mortality target.

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Section: Lean Application In Healthcarementioning

confidence: 99%

Lean Six Sigma to reduce the acute myocardial infarction mortality rate: a single center study

Rosa

Trunfio

Marolla

et al. 2023

TQM

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show abstract

“…When “ML, Six Sigma and COVID-19” keywords are searched together in scholarly articles, it is seen that only one study used Six Sigma and ML techniques during COVID-19 (Improta et al , 2022). However, in this single study, Six Sigma and ML tools are used separately, not in an integrative manner.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Six Sigma for Turkish manufacturers: utilization of machine learning tools in DMAIC

Uluskan

Karşı²

2022

IJLSS

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Purpose This study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC. Design/methodology/approach A data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects. Findings Among five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma. Originality/value Addressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

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“…In the literature, a view is presented according to which LM can be applied in any process in a healthcare facility to provide measurable benefits, such as reduction in inventory, shorter duration of activities and processes, improved quality of services or a higher level of satisfaction among both patients and employees [8]. Regardless of the manner and place of Lean implementation, five steps of its implementation are immutable and have to be followed in order: (1) define value from the patient's perspective, (2) map the value stream and identify issues and constraints, (3) remove the wastes and ensure the value flows without interruptions, (4) implement the solution and let the patients pull value, (5) maintain the improvements and seek perfection [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Lean Management Tools Using an Example of Analysis of Prolonged Stays of Patients in a Multi-Specialist Hospital in Poland

Zdęba-Mozoła

Kozłowski²,

Rybarczyk-Szwajkowska³

et al. 2023

IJERPH

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Healthcare institutions in Poland constantly encounter challenges related both to the quality of provided services and to the pressures associated with treatment effectiveness and economic efficiency. The implemented solutions have a goal of improving the service quality of lowering the continuously increasing operational costs. The aim of this paper is to present the application of Lean Management (LM) tools in a Polish hospital, which allowed for the identification of prolonged stays as one of the main issues affecting the service costs and the deteriorating financial results of the hospital. The study was conducted in the neurology department and involved an analysis of data for the whole of 2019 and the first half of 2022. In addition, surveys were conducted among the medical staff to help identify the main causes of prolonged stays. Methods of data analysis and feasible solutions were developed in order to improve the economic efficiency of the unit. The analysis shows that the application of LM tools may contribute to improvement in the functioning of hospitals and that further studies should focus on the development of the method to evaluate efficiency of the implemented solutions intended at shortening the hospital stays of the patients.

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Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study

Cited by 33 publications

References 65 publications

Lean Six Sigma to reduce the acute myocardial infarction mortality rate: a single center study

Lean Six Sigma to reduce the acute myocardial infarction mortality rate: a single center study

Predictive Six Sigma for Turkish manufacturers: utilization of machine learning tools in DMAIC

Implementation of Lean Management Tools Using an Example of Analysis of Prolonged Stays of Patients in a Multi-Specialist Hospital in Poland

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