Ambient Noise in the Operating Theater: A Bibliometric Analysis of Studies Examining Its Effects on Physiological, Psychological, and Performance of Health Professionals

Abstract

Purpose: To present a bibliometric analysis of scientific studies examining the effects of noise, specifically, to review publications, authors, journals, and research institutions and to outline thematic developments in this area. Design: This retrospective and descriptive study was conducted using a bibliometric analysis approach. Methods: The study population comprised 238 publications from the Web of Science Core Collection database, focusing on noise in operating room environments with keywords in titles, abstracts, or main texts. Based on inclusion and exclusion criteria, a sample of 64 publications was selected. These records were downloaded in BibTeX format as of August 21, 2024, excluding studies added to the database after this date. Data analysis was performed using R Studio software. Findings: The earliest study on the effects of operating room noise on health care professionals was published in 1993. Of the total publications, 62.5% were published within the past 4 years. The most frequent journals were Anaesthesia, Surgical Endoscopy and Other Interventional Techniques, and World Journal of Surgery. The country with the highest output was the United States. Keywords such as noise, operating room, music, communication, and stress were most commonly used. Additionally, interest in noise and music topics peaked in 2021. Conclusions: There has been a notable increase in studies on the impact of operating room noise on health care professionals, with a concentration of research in 2021. Most studies originated from developed countries like the United States, the United Kingdom, and Germany. This trend reflects a growing global awareness of the importance of occupational safety and health for health care workers. (c) 2025 American Society of PeriAnesthesia Nurses. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.