Challenging Cases in Statistical Process Control for Quality Improvement in Neonatal Intensive Care

Munish Gupta; Lloyd P Provost; Heather C Kaplan

doi:10.1016/j.clp.2023.02.004

Challenging Cases in Statistical Process Control for Quality Improvement in Neonatal Intensive Care

Clin Perinatol. 2023 Jun;50(2):321-341. doi: 10.1016/j.clp.2023.02.004. Epub 2023 Mar 27.

Authors

Munish Gupta¹, Lloyd P Provost², Heather C Kaplan³

Affiliations

¹ Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA. Electronic address: mgupta@bidmc.harvard.edu.
² Associates in Process Improvement, 2000 Red Hawk Road, Wimberly, TX 78676, USA.
³ Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229, USA.

PMID: 37201984
DOI: 10.1016/j.clp.2023.02.004

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.

Keywords: Control chart; Quality improvement; Quality improvement methods; Statistical process control.

Publication types

Review

MeSH terms

Delivery of Health Care
Humans
Infant, Newborn
Intensive Care, Neonatal*
Quality Improvement*