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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

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  • Giovanni Improta

    (Department of Public Health, University of Naples “Federico II”, 80131 Naples, Italy
    Interdepartmental Center for Research in Healthcare Management and Innovation in Healthcare (CIRMIS), University of Naples “Federico II”, 80131 Naples, Italy)

  • Anna Borrelli

    (“San Giovanni di Dio e Ruggi d’Aragona” University Hospital, 84121 Salerno, Italy)

  • Maria Triassi

    (Department of Public Health, University of Naples “Federico II”, 80131 Naples, Italy
    Interdepartmental Center for Research in Healthcare Management and Innovation in Healthcare (CIRMIS), University of Naples “Federico II”, 80131 Naples, Italy)

Abstract

Background: In health, it is important to promote the effectiveness, efficiency and adequacy of the services provided; these concepts become even more important in the era of the COVID-19 pandemic, where efforts to manage the disease have absorbed all hospital resources. The COVID-19 emergency led to a profound restructuring—in a very short time—of the Italian hospital system. Some factors that impose higher costs on hospitals are inappropriate hospitalization and length of stay (LOS). 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. Methods: This study analyzed how COVID-19 changed the activity of the Complex Operative Unit (COU) of the Neurology and Stroke Unit of the San Giovanni di Dio e Ruggi d’Aragona University Hospital of Salerno (Italy). The methodology used in this study was Lean Six Sigma. Problem solving in Lean Six Sigma is the DMAIC roadmap, characterized by five operational phases. To add even more value to the processing, a single clinical case, represented by stroke patients, was investigated to verify the specific impact of the pandemic. Results: The results obtained show a reduction in LOS for stroke patients and an increase in the value of the diagnosis related group relative weight. Conclusions: This work has shown how, thanks to the implementation of protocols for the management of the COU of the Neurology and Stroke Unit, the work of doctors has improved, and this is evident from the values of the parameters taken into consideration.

Suggested Citation

  • Giovanni Improta & Anna Borrelli & Maria Triassi, 2022. "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," IJERPH, MDPI, vol. 19(9), pages 1-19, April.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5215-:d:802105
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    References listed on IDEAS

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    1. Arianna Scala & Alfonso Maria Ponsiglione & Ilaria Loperto & Antonio Della Vecchia & Anna Borrelli & Giuseppe Russo & Maria Triassi & Giovanni Improta, 2021. "Lean Six Sigma Approach for Reducing Length of Hospital Stay for Patients with Femur Fracture in a University Hospital," IJERPH, MDPI, vol. 18(6), pages 1-13, March.
    2. Giovanni Improta & Giuseppe Converso & Teresa Murino & Mosè Gallo & Antonietta Perrone & Maria Romano, 2019. "Analytic Hierarchy Process (AHP) in Dynamic Configuration as a Tool for Health Technology Assessment (HTA): The Case of Biosensing Optoelectronics in Oncology," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1533-1550, September.
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    Cited by:

    1. Agnieszka Zdęba-Mozoła & Remigiusz Kozłowski & Anna Rybarczyk-Szwajkowska & Tomasz Czapla & Michał Marczak, 2023. "Implementation of Lean Management Tools Using an Example of Analysis of Prolonged Stays of Patients in a Multi-Specialist Hospital in Poland," IJERPH, MDPI, vol. 20(2), pages 1-23, January.
    2. Zeynep Tosuner & Osman Karaer & Ümit İnce, 2023. "Lean Six Sigma Applications in a Multicenter Pathology Laboratory: an Industrial Pathology Model," SN Operations Research Forum, Springer, vol. 4(3), pages 1-18, September.
    3. Arianna Scala & Ilaria Loperto & Maria Triassi & Giovanni Improta, 2022. "Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study," IJERPH, MDPI, vol. 19(16), pages 1-10, August.

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