Control charts, used in healthcare operations to monitor process stability and quality, are essential for ensuring patient safety and improving clinical outcomes. This comprehensive research study aims to provide a thorough understanding of the role of control charts in healthcare quality monitoring and future perspectives by utilizing a dual methodology approach involving a systematic review and a pioneering bibliometric analysis. A systematic review of 73 out of 223 articles was conducted, synthesizing existing literature (1995-2023) and revealing insights into key trends, methodological approaches, and emerging themes of control charts in healthcare. In parallel, a bibliometric analysis (1990-2023) on 184 articles gathered from Web of Science and Scopus was performed, quantitatively assessing the scholarly landscape encompassing control charts in healthcare. Among 25 countries, the USA is the foremost user of control charts, accounting for 33% of all applications, whereas among 14 health departments, epidemiology leads with 28% of applications. The practice of control charts in health monitoring has increased by more than one-third during the last 3 years. Globally, exponentially weighted moving average charts are the most popular, but interestingly the USA remained the top user of Shewhart charts. The study also uncovers a dynamic landscape in healthcare quality monitoring, with key contributors, research networks, research hotspot tendencies, and leading countries. Influential authors, such as J.C. Benneyan, W.H. Woodall, and M.A. Mohammed played a leading role in this field. In-countries networking, USA-UK leads the largest cluster, while other clusters include Denmark-Norway-Sweden, China-Singapore, and Canada-South Africa. From 1990 to 2023, healthcare monitoring evolved from studying efficiency to focusing on conditional monitoring and flowcharting, with human health, patient safety, and health surveys dominating 2011-2020, and recent years emphasizing epidemic control, COronaVIrus Disease of 2019 (COVID-19) statistical process control, hospitals, and human health monitoring using control charts. It identifies a transition from conventional to artificial intelligence approaches, with increasing contributions from machine learning and deep learning in the context of Industry 4.0. New researchers and journals are emerging, reshaping the academic context of control charts in healthcare. Our research reveals the evolving landscape of healthcare quality monitoring, surpassing traditional reviews. We uncover emerging trends, research gaps, and a transition in leadership from established contributors to newcomers amidst technological advancements. This study deepens the importance of control charts, offering insights for healthcare professionals, researchers, and policymakers to enhance healthcare quality. Future challenges and research directions are also provided.
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