NSF CHOT IUCRC PROGRESS REPORT - PROJECT # 6
Bundle Science Statistical Models and Analysis
Research team
James Benneyan, Eralp Dogu, Aven Samareh
Description
The objective of this project is to investigate
statistical methods for patient safety “bundles”
and risk-adjusted binary data. It would be
beneficial to monitor bundle compliance over
time, and analyze relative importance and
interaction of bundle elements. A particular
focus here is on investigating statistical quality
control charts under ‘real world’ conditions of
messy data with an assumption that process
parameters are not known to us.
Bundle Control Chart Example: Total Joint
Replacement SSI Bundle p Chart
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provided an analysis concerning the required
number of samples, sample sizes and number
of elements in a bundle. This includes:
 Developed a simulation model by which we
generated a phase I data sets for different
samples, sample sizes and number of
elements of bundle in Matlab.
 Developed a Markov chain code in Matlab as
an accurate approximation for average run
length (ARL), to compare performance under
ideal and above cases.
It could be seen that ARL performance is
sensitive to the choice of the samples and
sample size. This study could be very useful for
researchers for designing np charts in order to
detect minor process variations in evidencedbased events and improving quality of care.
Furthermore, ARL values are close to the
design value of 500 for lower number of bundle
elements and sample sizes as well as for low
compliance rates.
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How is this different than related research?
Despite becoming part of routine improvement
projects, the evidence based bundles is limited
at best. In this work we develop a general
bundle science framework and tools to
compare and monitor bundle compliance over
time.
Milestones achieved to date
An indispensable assumption for construction
of control charts is that the process parameters
are assumed to be known. In practice, the
process parameters are rarely known, and are
usually estimated from an in-control historical
data set (phase I). When the parameters are
estimated, the performance of the control
charts differs from the known parameters case
due to the variability of the estimators used
during the Phase I. Hence, we developed and
extended statistical methods for bundle
monitoring by deriving the run length
properties of the investigated np charts, and
Next steps
 Extend these results to other risk-adjusted
data and estimation error contexts
 Begin analysis of bundle compliance data,
relative effect sizes, aggregate impact, and
inclusion criteria
Potential member benefits
1. Validated statistical methods for comparing and monitoring bundle compliance over time
2. Understanding of the relative importance of different bundle elements
3. Development of a general bundle science framework