Transcript Biostatistics in Quality Improvement

Henry Domenico
Vanderbilt University Medical Center
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Guidelines for a Data Driven Project
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What can Biostatistics do for a Project?
Defining a Question
Data Collection
Analysis
Conclusion
Readmissions at Vanderbilt
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Background
Identifying Important Factors
Predicting the Probability of Readmission
Future Work
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Statistics is a powerful tool.
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Statistics should be part of improving the
care we give, not just for research.
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My focus is on using statistics to…
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Answer questions about quality issues
Understand where we are and where we’re going
Identify the driving force behind the issues we face
Develop new strategies for improving care
Understand the effectiveness of an intervention
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Understand your population
Decide if previously held notions are correct
Discover what factors are important to your
problem
Make Predictions
Test an Intervention
Present your findings in a convincing way
Wealth of data collected at Vanderbilt
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Biostatistics can be built into each stage of a
project.
◦ Question
◦ Data Collection
◦ Analysis
◦ Conclusions
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You can make your life easier by starting with
a carefully defined question.
◦ What factors lead to increased patient satisfaction?
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Clearly identify the population.
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What are the possible factors?
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How will the response be measured?
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What is a clinically significant result?
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Data should be collected from the population of
interest.
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Ideally would like to have any variable that effects
the response.
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Observational or Experimental?
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At what level should the data be gathered?
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A lot of effort can go into gathering data that
won’t answer your question.
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If the question is carefully defined and data is
gathered correctly, analysis becomes the easy
part.
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Identify important factors.
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Determine statistical/clinical significance.
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Account for confounding factors.
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We want to present our results in a way that
is convincing to others.
Use the results of the analysis to present a
clear picture of what is occurring.
Directly answer the original question.
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Hospital Readmission rate is being viewed as
a quality metric.
In the near future, Vanderbilt will see financial
penalties for patients discharged and
readmitted within 30 days.
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Can we identify which patients are at risk of
being readmitted?
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Can we model a patient’s probability of being
readmitted within 30 days?
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Can we present this information to providers
at the point of decision?
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Is there an intervention that will reduce the
probability that a patient is readmitted?
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Working with a data set of all 2009
Inpatients.
Created a readmission flag.
Demographic and diagnostic variables are
included.
Missing lab and vital sign information.
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Can examine each DRG’s readmission rate.
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Several have only one observation.
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May be more meaningful to examine which
had a higher number of readmissions than
average.
DRG
Description
Residual
280
AMI Disc. Alive
6
847
Chemo
5.5
293
Heart Failure
5.1
291
Heart Failure
4.5
216
Cardiac Valve
3.6
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Used same method to determine:
Patients from Davidson County had a higher
readmission rate.
ICD-9’s 780( Malaise and Fatigue) and
789.09(Symptoms involving abdomen and
Pelvis) had higher readmission rates.
Patients on Blue Cross/Blue Shield were at
higher risk.
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Logistic regression models the probability of
readmission based on a patient’s explanatory
variables.
Used logistic regression to model the odds
ratio for different factors.
The odds ratio tells us the increased
probability of readmission associated with
these factors.
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Again using logistic regression, we can
develop a model that will provide each
patient’s readmission probability.
Specify which variables we want to use to
make predictions.
Use a statistical software package to build a
model.
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Model a patient’s probability of readmission
based on:
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Age
Length of Stay
Number of Medications
DRG
Model should only be applied to patients
from same population used to build the
model.
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Alternative non-parametric approach to
logistic regression.
Breaks population into subsets and identifies
factors important to each subset.
Provides predicted probabilities like logistic
regression.
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Our goal is to be able to inform providers of a
patient’s probability of readmission.
Let them know what factors need to be
addressed before discharge.
Goal is to reduce preventable readmissions.
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Obtain a more comprehensive data set.
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All admitted patients
Gender
Lab Values
Vital Signs
Readmission Status
Develop specific models for individual DRGs.
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Eventual goal is to work with subject matter
experts to develop an intervention that
reduces readmissions.
Show effectiveness using a Randomized
Controlled Trial.
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This is just one example of how statistics can
improve a project.
Hopefully demonstrates the value of being
data driven and using statistics.
Biostatistics Free Clinic
Thank You
Questions?