A new and more accurate model for predicting compliance to evidence-based bundles of practice

CHOT researchers used probability generating functions to rapidly predict compliance and illustrate the improved detection of non-compliance over other methods. This new prediction model for monitoring compliance is practical, fast, and accurate compared to more commonly used types of compliance control charts based on binomial or normal distribution of compliance data, which are shown to produce poor detection properties. CHOT researchers also show that by using this correct method, exact results can be obtained fairly easily, in turn accelerating improvement towards greater compliance to evidence-based practice bundles. Compliance to evidence-based practices, individually and in ‘bundles’, remains an important focus of healthcare quality improvement for many clinical conditions.

Chen, B., Matis, T., & Benneyan, J. (2016). Computing exact bundle compliance control charts via probability generating functions. Health Care Management Science, 103-110.


Research Contact:
James Benneyan, PhD