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Automating Payroll Paper Generation in the Healthcare Industry: A Technical Solution for Hublo’s Customer Plants.

Francesca Paoletti

Automating Payroll Paper Generation in the Healthcare Industry: A Technical Solution for Hublo’s Customer Plants.

Rel. Paolo Garza. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2023

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Abstract:

As the healthcare industry continues to evolve, automation has become increasingly important to improve the overall efficiency and productivity of businesses. With this in mind, Hublo, a medical technology solutions provider, sought to automate the process of generating payroll papers for its customer factories. This procedure proved to be challenging since many various requirements and standards had to be considered while also ensuring efficacy and generalization. The technical solution provided involves several steps, including data collecting and collation, data cleansing and construction, and combining plant features to automatically generate payroll papers. The project's ultimate goal was to provide a global solution that was not only efficient and adaptable but also intuitive, answering the particular demands of individual clients while also meeting regulatory standards. This project was a great achievement, with the technical solution proving to be extremely efficient and allowing Hublo to automate what was previously a laborious and time-consuming operation. Since the introduction of this solution, numerous additional clients have subscribed to the module, demonstrating its efficacy and adaptability. The automated pipeline is safe, efficient, and GDPR-compliant, increasing safety, productivity, and accuracy while decreasing the number of manual procedures necessary. This project exemplifies the benefits of automation in the healthcare business and demonstrates the possibilities for future innovation in this field.

Relatori: Paolo Garza
Anno accademico: 2022/23
Tipo di pubblicazione: Elettronica
Numero di pagine: 72
Soggetti:
Corso di laurea: Corso di laurea magistrale in Data Science And Engineering
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-32 - INGEGNERIA INFORMATICA
Ente in cotutela: INSTITUT NATIONAL POLYTECHNIQUE DE GRENOBLE (INPG) - ENSIMAG (FRANCIA)
Aziende collaboratrici: SAS HUBLO
URI: http://webthesis.biblio.polito.it/id/eprint/26881
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