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Learnche.org. (n.d.). Shewhart charts. Retrieved October 15, 2024, from https://learnche.org/pid/process-monitoring/shewhart-charts
The article from Learnche.org provides a comprehensive overview of Shewhart charts, also known as control charts, and their application in process monitoring.

Clinical Excellence Commission. (n.d.). Control Charts. CEC Academy. Retrieved October 15, 2024, from https://www.cec.health.nsw.gov.au/CEC-Academy/quality-improvement-tools/control-charts
The article from the Clinical Excellence Commission's CEC Academy provides a comprehensive overview of control charts, also known as Shewhart Charts or Statistical Process Control Charts (SPCC), and their application in healthcare quality improvement.

Burstin, H., Leatherman, S., & Goldmann, D. (2016, March 3). Why process measures are often more important than outcome measures in healthcare. Health Affairs Blog.
The article by Burstin, Leatherman, and Goldmann (2016) discusses the importance of process measures in healthcare quality improvement, arguing that they are often more crucial than outcome measures.

McQuillan, R. F., Silver, S. A., Harel, Z., Weizman, A., Thomas, A., Bell, C., Chertow, G. M., Chan, C. T., & Nesrallah, G. (2016). How to Measure and Interpret Quality Improvement Data. Clinical Journal of the American Society of Nephrology, 11(5), 908-914.
McQuillan et al. provide a comprehensive guide on measuring and interpreting quality improvement (QI) data in healthcare settings, particularly in nephrology. The article emphasizes the importance of selecting appropriate measures, including outcome, process, and balancing measures, to effectively track improvement efforts. It introduces the Plan-Do-Study-Act (PDSA) cycle as a framework for implementing and evaluating changes. The authors highlight the use of run charts as a powerful tool for visualizing data over time and detecting improvements or challenges in real-time. They explain how to construct and interpret run charts, including the application of probability-based rules to identify significant changes. The paper also discusses the importance of operational definitions for measures and offers practical advice on data collection and analysis. By providing a clear and practical framework, this article aims to equip healthcare professionals with the necessary skills to conduct and evaluate quality improvement projects in clinical settings.

Institute for Healthcare Improvement. (2017). QI Essentials Toolkit: Run Chart & Control Chart.
The Institute for Healthcare Improvement's QI Essentials Toolkit provides a comprehensive guide on using run charts and control charts for quality improvement in healthcare settings. Run charts are presented as simple yet effective tools for tracking data over time, helping teams visualize trends and determine if changes are leading to improvements. The guide explains that control charts, which include upper and lower control limits, offer more detailed insights into process variation when there are more than 15 data points available. Both types of charts are crucial for distinguishing between common-cause variation (inherent in the process) and special-cause variation (due to specific circumstances).
The toolkit offers step-by-step instructions for creating these charts, including how to plot data, calculate medians or means, and add control limit. It also emphasizes the importance of annotating charts to indicate when tests of change were initiated, allowing teams to easily observe the effects of interventions on the measured process.

Daudelin, D. H., Selker, H. P., & Leslie, L. K. (2015). Applying process improvement methods to clinical and translational research: Conceptual framework and case examples. Clinical and Translational Science, 8(6), 779–786.
This article provides a comprehensive overview of how process improvement methods can be applied to enhance the quality and efficiency of clinical and translational research, offering both a conceptual framework and practical examples for implementation.

Mulissa, Z. (2019, January 4). Tools to understand variation in your improvement journey. NICHQ.
Tools to understand variation in improvement journeys are essential for organizations seeking to enhance their processes and outcomes. These tools help identify patterns, distinguish between common and special cause variation, and inform decision-making for effective improvements.

Slyngstad, L. (2021). The Contribution of Variable Control Charts to Quality Improvement in Healthcare: A Literature Review. Journal of Multidisciplinary Healthcare, 14, 2473-2481.
This literature review by Slyngstad (2021) examines the application and impact of variable control charts in healthcare quality improvement.

Winckler, B., McKenzie, S., & Lo, H. (2024). A Practical Guide to QI Data Analysis: Run and Statistical Process Control Charts. Hospital Pediatrics, 14(1), e83-e89.
The article by Winckler, McKenzie, and Lo provides a practical guide for healthcare professionals on using run charts and statistical process control (SPC) charts in quality improvement (QI) initiatives. These tools are essential for analyzing and interpreting data in healthcare settings, allowing practitioners to monitor processes over time and identify significant changes.