Related Experiment Video
Updated: Jul 30, 2025

Hemodynamic Precision in the Neonatal Intensive Care Unit using Targeted Neonatal Echocardiography
Published on: January 27, 2023
Challenging Cases in Statistical Process Control for Quality Improvement in Neonatal Intensive Care
Munish Gupta1, Lloyd P Provost2, Heather C Kaplan3
1Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA.
Abstract:
Effective quality improvement (QI) depends on rigorous analysis of time-series data through methods such as statistical process control (SPC). As use of SPC has become more prevalent in health care, QI practitioners must also be aware of situations that warrant special attention and potential modifications to common SPC charts, which include skewed continuous data, autocorrelation, small persistent changes in performance, confounders, and workload or productivity measures. This article reviews these situations and provides examples of SPC approaches for each.
Related Concept Videos
Introduction to Statistical Process Control
Quality Control
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Regression Toward the Mean
The R Chart
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
Healthcare Associated Infections II: Preventive Measures
The best practices for preventing healthcare-associated infections include hand hygiene, patient risk...

