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This scientometric analysis reviews 200 highly cited articles from Scopus to map the intellectual structure and key themes related to Total Quality Management (TQM) in surgical and healthcare contexts. The study identifies key research clusters including patient safety, surgical procedure optimization, QI methodologies, and ethical considerations, noting a shift towards patient-centric and data-driven approaches. The analysis highlights influential institutions and publications contributing to safety protocols, QI methodologies, and patient engagement.
The study reveals a mature and evolving research domain focused on data-driven and patient-centric approaches to surgical quality improvement, suggesting a move beyond theoretical frameworks.
We present a scientometric analysis of the research landscape about the application of Total Quality Management (TQM) rules within surgical and broader healthcare contexts. The study utilizes a dataset of 200 highly cited articles extracted from Scopus and maps the intellectual structure, key themes, and evolving priorities in this critical field. The study discloses a mature yet dynamically evolving survey domain characterized by a distinct shift from theoretical process frameworks to patient-centric and data-driven methodologies. Key study clusters determined include Patient Safety Culture and Adverse Event Reduction, Specific Surgical Procedure Optimization, Methodological Frameworks for Quality Improvement (QI), and Ethical & Inclusive Care Considerations. Highly cited articles and authors as well as influential institutions are determined, representing a global collaboration network with strong representation from the United States and Northern Europe. The most effective publications, as stated by citation frequency, are studied in detail, briefing their contributions to building safety protocols, validating QI methodologies like DMAIC, and expanding the discourse on patient engagement and health equity. The present review summarizes that the field is advancing towards more predictive, equitable, and technologically integrated models of care, with future research poised to leverage artificial intelligence and federated learning to personalize and enhance surgical quality improvement on a global scale.
