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Detecting and visualizing outliers in provider profiling via funnel plots and mixed effect models

Francesca Ieva1, Anna Maria Paganoni

  • 1MOX - Modellistica e Calcolo Scientifico, Dipartimento di Matematica, Politecnico di Milano, via Bonardi 9, 20133, Milan, Italy, francesca.ieva@polimi.it.

Health Care Management Science
|January 10, 2014
PubMed

Insights

This study introduces the funnel plot, a graphical tool to identify outlier hospitals treating Acute Myocardial Infarction (AMI) patients. It compares this method

Area of Science:

  • Healthcare analytics
  • Medical informatics
  • Public health surveillance

Background:

  • Acute Myocardial Infarction (AMI) patient outcomes can vary significantly between hospitals.
  • Identifying performance outliers is crucial for quality improvement in cardiovascular care.
  • Administrative databases offer a rich source for analyzing in-hospital mortality rates.

Purpose of the Study:

  • To evaluate the effectiveness of the funnel plot as a graphical diagnostic tool for detecting outlier hospitals in Acute Myocardial Infarction (AMI) treatment.
  • To compare the performance of the funnel plot with traditional parametric mixed-effects models for outlier detection.
  • To apply these methods to real-world AMI hospitalization data from a regional administrative database.

Main Methods:

  • Utilizing a funnel plot, a graphical method for assessing heterogeneity and identifying outliers in performance metrics.
  • Applying parametric mixed-effects models to analyze in-hospital mortality data.
  • Comparing the concordance and discrepancies between outlier identification using graphical tools versus statistical models.

Main Results:

  • The funnel plot effectively identified potential outlier hospitals based on in-hospital mortality rates for Acute Myocardial Infarction (AMI) patients.
  • Results from the funnel plot showed good agreement with, but also offered complementary insights to, those from parametric mixed-effects models.
  • The graphical approach provided an intuitive visualization of hospital performance variation.

Conclusions:

  • The funnel plot is a valuable and accessible tool for identifying outlier hospitals in the context of Acute Myocardial Infarction (AMI) care.
  • Graphical diagnostic tools can serve as a practical complement or alternative to complex statistical models for quality monitoring.
  • This approach aids in focusing quality improvement efforts on hospitals with potentially suboptimal outcomes.

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