Evidence map›Paper›PMID 31915369›Full record

ReviewJournal of perinatology : official journal of the California Perinatal Association2020

Measurement for quality improvement: using data to drive change.

Munish Gupta, Heather C Kaplan

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of perinatology : official journal of the California Perinatal Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. A FAR-Out approach for evaluating the impact of clinical practice changes on severe intracranial hemorrhage in preterm infants.Journal of perinatology : official journal of the California Perinatal Association · 2026
    Article
  3. Article
  4. Quicker team launch times for urgent priority neonatal retrievals: A Quality Improvement Initiative study.Journal of perinatology : official journal of the California Perinatal Association · 2026
    Article
  5. Review
  6. Status Dystonicus in Children: Is it more Common than we Realize?Movement disorders clinical practice · 2025
    Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Organization of care of infants with congenital diaphragmatic hernia-Building a high-functioning CDH program.Journal of perinatology : official journal of the California Perinatal Association · 2024
    Review
  13. Article
  14. Advancements in neonatology through quality improvement.Journal of perinatology : official journal of the California Perinatal Association · 2022
    Review
  15. Review
  16. Decreasing early hypoglycemia frequency in at-risk newborns after implementing a new hypoglycemia screening algorithm.Journal of perinatology : official journal of the California Perinatal Association · 2021
    Article
  17. Changing safety culture.Journal of perinatology : official journal of the California Perinatal Association · 2021
    Article
  18. A practical guide to publishing a quality improvement paper.Journal of perinatology : official journal of the California Perinatal Association · 2021
    Article
  19. Article
  20. The business case for quality improvement.Journal of perinatology : official journal of the California Perinatal Association · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Munish GuptaHarvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA. mgupta@bidmc.harvard.edu.
Heather C KaplanPerinatal 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Delivery of Health CareQuality ImprovementHumans

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.