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  • Quality Improvement Article
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Measurement for quality improvement: using data to drive change

Journal of Perinatology volume 40, pages 962–971 (2020)Cite this article

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Abstract

Measurement is a core foundation of quality improvement (QI), and analysis of data for QI requires distinct approaches and tools as compared with other areas of healthcare. QI efforts can use structural, process, outcome, and balancing measures, and each measure should have a clear operational definition. Data for improvement should be analyzed dynamically over time, with a focus on understanding the variation present in the data. Distinguishing between common cause and special cause variation is necessary to evaluate and guide improvement efforts. Statistical process control tools such as run charts and control charts can be powerful tools to analyze data over time and help understand variation. This article continues a series of QI educational papers in the Journal of Perinatology, and offers a review of the use of data and measures to drive improvement.

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Fig. 1: Example of common cause and special cause variation.
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Fig. 6: Algorithm to choose type of control chart based on type of data.
Fig. 7: Run charts and control charts for QI initiative to increase first feedings as mother’s milk in NICU infants.

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Authors and Affiliations

  1. Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA

    Munish Gupta

  2. Perinatal Institute and James M. Anderson Center for Health Systems Excellence, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Heather C. Kaplan

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  1. Munish Gupta
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Correspondence to Munish Gupta.

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Gupta, M., Kaplan, H.C. Measurement for quality improvement: using data to drive change. J Perinatol 40, 962–971 (2020). https://doi.org/10.1038/s41372-019-0572-x

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