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[Comparative quality measurements part 3: funnel plots].

Jan Kottner1, Nils Lahmann2

  • 1Clinical Research Center for Hair and Skin Science, Klinik für Dermatologie, Venerologie und Allergologie, Charité-Universitätsmedizin Berlin.

Pflege
|February 28, 2014
PubMed
Summary

Comparative quality assessments require standardized, risk-adjusted measures. Funnel plots, using Statistical Process Control, help account for random error and prevent flawed institutional rankings.

Keywords:
Funnel PlotsPflegeQualitätsindikatorenQualitätskontrolleQualitätsverbesserungfunnel plotsnursingquality controlquality improvementquality indicators

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Area of Science:

  • Healthcare Quality Improvement
  • Statistical Process Control
  • Performance Measurement

Background:

  • Comparative quality measurements between organizations are prevalent.
  • Standardization, risk adjustment, and accounting for random error are crucial for accurate quality assessment.
  • Rankings that ignore precision can lead to misinterpretations and manipulation ('gaming').

Purpose of the Study:

  • To introduce funnel plots as a method for evaluating institutional performance.
  • To demonstrate how funnel plots account for random variation in quality measures.
  • To provide a data-based approach for quality management.

Main Methods:

  • Utilizing funnel plots, a modification of control charts based on Statistical Process Control (SPC) theory.
  • Plotting quality measures against their respective sample sizes.
  • Incorporating warning and control limits (2 or 3 standard deviations) to identify common and special cause variation.

Main Results:

  • Funnel plots visually represent the relationship between precision and sample size, with control limits narrowing as group size increases.
  • Data points falling within control limits indicate common cause variation, while those outside suggest special cause variation.
  • This method helps distinguish genuine performance differences from random fluctuations, mitigating spurious rankings.

Conclusions:

  • Funnel plots offer a statistically sound method for evaluating institutional performance in quality management.
  • They provide a visual tool to account for random error and improve the interpretation of comparative quality data.
  • Implementing funnel plots can lead to more accurate assessments and prevent the negative consequences of "gaming" quality metrics.