Computing exact bundle compliance control charts via probability generating functions

Binchao Chen, Timothy Matis, James Benneyan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Compliance to evidenced-base practices, individually and in ‘bundles’, remains an important focus of healthcare quality improvement for many clinical conditions. The exact probability distribution of composite bundle compliance measures used to develop corresponding control charts and other statistical tests is based on a fairly large convolution whose direct calculation can be computationally prohibitive. Various series expansions and other approximation approaches have been proposed, each with computational and accuracy tradeoffs, especially in the tails. This same probability distribution also arises in other important healthcare applications, such as for risk-adjusted outcomes and bed demand prediction, with the same computational difficulties. As an alternative, we use probability generating functions to rapidly obtain exact results and illustrate the improved accuracy and detection over other methods. Numerical testing across a wide range of applications demonstrates the computational efficiency and accuracy of this approach.

Original languageEnglish
Pages (from-to)103-110
Number of pages8
JournalHealth Care Management Science
Issue number2
StatePublished - Jun 1 2016


  • Control charts
  • Convolutions
  • Health care
  • Probability generating function


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