QFD Approach for the Optimization of Hospital Cleaning Processes and the Implementation of Digital Transformation

Authors

DOI:

https://doi.org/10.5281/zenodo.17992123

Keywords:

Digital Transformation, QFD, Healthcare Institutions, Optimization

Abstract

Today, the use of digital transformation applications in healthcare services has provided significant improvements in hospital service management. These developments not only focus on patient care and treatment processes but also play a crucial role in improving hygiene and cleanliness processes in healthcare settings. This study aimed to follow a systematic way to improve the quality of service in a university hospital and Gemba and Focus Group studies were carried out. The current situation of the hospital, including the relationships between expectations and requirements, as well as competitors, was taken into consideration. The Quality Function Deployment (QFD) method was used as an analysis and improvement tool. The importance levels of customer expectations were calculated with the QFD. Technical requirements have been determined in order to meet customer expectations. The findings show that hygiene and control-oriented factors have the highest priority; It has revealed that cleaning processes should be supported not only by routine applications, but also by measurable, traceable and digital solutions. The integration of traceability applications with RFID, QR code systems, and blockchain-based models demonstrates that processes can be made more reliable and optimized. As a result, the study highlights the strategic importance of managing cleaning and hygiene processes in terms of preventing infection and operational efficiency of work processes in healthcare institutions. The study proposes a QFD-based improvement roadmap integrated with digital transformation.

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Published

2025-12-19

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Section

Articles

How to Cite

QFD Approach for the Optimization of Hospital Cleaning Processes and the Implementation of Digital Transformation. (2025). The Journal of Artificial Intelligence and Human Sciences, 2(2), 42-56. https://doi.org/10.5281/zenodo.17992123

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