Management of the Diabetic Patient in the Diagnostic Care Pathway

Improta, Giovanni; Luciano, Maria Antonietta; Vecchione, Donatella; Cesarelli, Giuseppe; Rossano, Lucia; Santalucia, Ida; Triassi, Maria

doi:10.1007/978-3-030-64610-3_88

Cited by 27 publications

(9 citation statements)

References 25 publications

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“…, 2020), specific care path (Improta et al. , 2021; Improta et al. , 2020), data analysis (Ponsiglione et al.…”

Section: The Importance Of Reducing Amimentioning

confidence: 99%

“…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%

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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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“…, 2020), specific care path (Improta et al. , 2021; Improta et al. , 2020), data analysis (Ponsiglione et al.…”

Section: The Importance Of Reducing Amimentioning

confidence: 99%

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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“…Ferraro et al [ 19 ], on the other hand, utilize the Lean Six Sigma approach to analyze the impact of healthcare-associated infections using LOS as a control variable. Strategies need to be implemented to objectively study healthcare processes [ 20 , 21 , 22 ] or support resource management to contain costs in an increasingly business-like healthcare system [ 23 , 24 , 25 , 26 ]. In procedures such as childbirth, expenditure items are mainly associated with the hospitalization of the mother and child, the use of neonatal intensive care, and the type of delivery [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Predictive Analysis of Hospital Stay after Caesarean Section: A Single-Center Study

et al. 2023

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Caesarean section (CS) rate has seen a significant increase in recent years, especially in industrialized countries. There are, in fact, several causes that justify a CS; however, evidence is emerging that non-obstetric factors may contribute to the decision. In reality, CS is not a risk-free procedure. The intra-operative, post-pregnancy risks and risks for children are just a few examples. From a cost point of view, it must be considered that CS requires longer recovery times, and women often stay hospitalized for several days. This study analyzed data from 12,360 women who underwent CS at the “San Giovanni di Dio e Ruggi D’Aragona” University Hospital between 2010 and 2020 by multiple regression algorithms, including multiple linear regression (MLR), Random Forest, Gradient Boosted Tree, XGBoost, and linear regression, classification algorithms and neural network in order to study the variation of the dependent variable (total LOS) as a function of a group of independent variables. We identify the MLR model as the most suitable because it achieves an R-value of 0.845, but the neural network had the best performance (R = 0.944 for the training set). Among the independent variables, Pre-operative LOS, Cardiovascular disease, Respiratory disorders, Hypertension, Diabetes, Haemorrhage, Multiple births, Obesity, Pre-eclampsia, Complicating previous delivery, Urinary and gynaecological disorders, and Complication during surgery were the variables that significantly influence the LOS. Among the classification algorithms, the best is Random Forest, with an accuracy as high as 77%. The simple regression model allowed us to highlight the comorbidities that most influence the total LOS and to show the parameters on which the hospital management must focus for better resource management and cost reduction.

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“…In fact, many studies have focused on how to reduce patients’ LOS by optimizing care processes. For example, Scala et al and Improta et al demonstrated how the introduction of a diagnostic therapeutic pathway for femur fracture and diabetic patient management, respectively, reduced LOS with consequent benefits for both patients and hospitals [ 13 , 14 ]. Biomedical data analytics is key to improving processes, reducing costs, and giving clinicians new tools to manage all different patients.…”

Section: Introductionmentioning

confidence: 99%

Regression Models to Study the Total LOS Related to Valvuloplasty

Scala

Trunfio

Coppi

et al. 2022

IJERPH

Self Cite

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Background: Valvular heart diseases are diseases that affect the valves by altering the normal circulation of blood within the heart. In recent years, the use of valvuloplasty has become recurrent due to the increase in calcific valve disease, which usually occurs in the elderly, and mitral valve regurgitation. For this reason, it is critical to be able to best manage the patient undergoing this surgery. To accomplish this, the length of stay (LOS) is used as a quality indicator. Methods: A multiple linear regression model and four other regression algorithms were used to study the total LOS function of a set of independent variables related to the clinical and demographic characteristics of patients. The study was conducted at the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” of Salerno (Italy) in the years 2010–2020. Results: Overall, the MLR model proved to be the best, with an R2 value of 0.720. Among the independent variables, age, pre-operative LOS, congestive heart failure, and peripheral vascular disease were those that mainly influenced the output value. Conclusions: LOS proves, once again, to be a strategic indicator for hospital resource management, and simple linear regression models have shown excellent results to analyze it.

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Management of the Diabetic Patient in the Diagnostic Care Pathway

Cited by 27 publications

References 25 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 Analysis of Hospital Stay after Caesarean Section: A Single-Center Study

Regression Models to Study the Total LOS Related to Valvuloplasty

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