Implementation of lean practices to reduce healthcare associated infections

Ferraro, Antonino; Centobelli, Piera; Cerchione, Roberto; Cicco, Maria Vincenza Di; Montella, Emma; Raiola, Eliana; Triassi, Maria; Improta, Giovanni

doi:10.1504/ijhtm.2020.10039887

Cited by 8 publications

(2 citation statements)

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“…Several studies use advanced data processing in order to support doctors in the prevention, diagnosis, and treatment of diseases [15][16][17][18][19][20][21] or the management of hospital resources [22][23][24][25][26]. In the orthopedic field, many articles study the performance associated with the flow of patients who are admitted for fractures of the lower limbs.…”

Section: Related Workmentioning

confidence: 99%

Is It Possible to Predict the Length of Stay of Patients Undergoing Hip-Replacement Surgery?

Trunfio

Borrelli

Improta

2022

IJERPH

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The proximal fracture of the femur and hip is the most common reason for hospitalization in orthopedic departments. In Italy, 115,989 hip-replacement surgeries were performed in 2019, showing the economic relevance of studying this type of procedure. This study analyzed the data relating to patients who underwent hip-replacement surgery in the years 2010–2020 at the “San Giovanni di Dio e Ruggi d’Aragona” University Hospital of Salerno. The multiple linear regression (MLR) model and regression and classification algorithms were implemented in order to predict the total length of stay (LOS). Lastly, using a statistical analysis, the impact of COVID-19 was evaluated. The results obtained from the regression analysis showed that the best model was MLR, with an R2 value of 0.616, compared with XGBoost, Gradient-Boosted Tree, and Random Forest, with R2 values of 0.552, 0.543, and 0.448, respectively. The t-test showed that the variables that most influenced the LOS, with the exception of pre-operative LOS, were gender, age, anemia, fracture/dislocation, and urinary disorders. Among the classification algorithms, the best result was obtained with Random Forest, with a sensitivity of the longest LOS of over 89%. In terms of the overall accuracy, Random Forest and Gradient-Boosted Tree achieved a value of 71.76% and an error of 28.24%, followed by Decision Tree, with an accuracy of 71.13% and an error of 28.87%, and, finally, Support Vector Machine, with an accuracy of 65.06% and an error of 34.94%. A significant difference in cardiovascular disease, fracture/dislocation, and post-operative LOS variables was shown by the chi-squared test and Mann–Whitney test in the comparison between 2019 (before COVID-19) and 2020 (in full pandemic emergency conditions).

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Section: Related Workmentioning

confidence: 99%

Is It Possible to Predict the Length of Stay of Patients Undergoing Hip-Replacement Surgery?

Trunfio

Borrelli

Improta

2022

IJERPH

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“…For these reasons, in literature there are many works that have used LOS as an indicator of quality [ 13 – 15 ]. In all aspects of the healthcare sector, the extraction of clinical and organizational data for advanced analysis [ 16 – 19 ] and for process improvement [ 20 – 23 ] has proven to be a fundamental support in patient management.…”

Section: Introductionmentioning

confidence: 99%

Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy

Trunfio¹,

Scala

Giglio

et al. 2022

BMC Med Inform Decis Mak

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Background The rapid growth in the complexity of services and stringent quality requirements present a challenge to all healthcare facilities, especially from an economic perspective. The goal is to implement different strategies that allows to enhance and obtain health processes closer to standards. The Length Of Stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. In fact, a patient's LOS can be affected by a number of factors, including their particular condition, medical history, or medical needs. To reduce and better manage the LOS it is necessary to be able to predict this value. Methods In this study, a predictive model was built for the total LOS of patients undergoing laparoscopic appendectomy, one of the most common emergency procedures. Demographic and clinical data of the 357 patients admitted at “San Giovanni di Dio e Ruggi d’Aragona” University Hospital of Salerno (Italy) had used as independent variable of the multiple linear regression model. Results The obtained model had an R2 value of 0.570 and, among the independent variables, the significant variables that most influence the total LOS were Age, Pre-operative LOS, Presence of Complication and Complicated diagnosis. Conclusion This work designed an effective and automated strategy for improving the prediction of LOS, that can be useful for enhancing the preoperative pathways. In this way it is possible to characterize the demand and to be able to estimate a priori the occupation of the beds and other related hospital resources.

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Implementation of DMAIC Cycle to Study the Impact of COVID-19 on Emergency Department-LOS

Improta

Bottino²,

Stingone³

et al. 2023

Lecture Notes in Computer Science

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Implementation of lean practices to reduce healthcare associated infections

Cited by 8 publications

References 0 publications

Is It Possible to Predict the Length of Stay of Patients Undergoing Hip-Replacement Surgery?

Is It Possible to Predict the Length of Stay of Patients Undergoing Hip-Replacement Surgery?

Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy

Implementation of DMAIC Cycle to Study the Impact of COVID-19 on Emergency Department-LOS

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