The document discusses how a large health system used data science to improve patient outcomes and reduce costs related to orthopedic surgery readmissions. By using logistic regression models, the health system found that factors like BMI were not actually associated with readmissions. This allowed them to avoid unnecessary pre-op interventions and instead focus on other factors identified by the models, like behavior disorders and opioid use, that were tied to readmissions. Data science helped the health system optimize their improvement processes and resources to achieve the desired outcomes of reducing readmissions.

1. Why Health Systems Must Use Data
Science to Improve Outcomes

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Data Science Improves Outcomes
With more statistically rigorous analytic
methods to further automate insight
identification, data science and machine
learning can help health systems align
effective measures with specific
improvement goals more accurately and
faster than typical data analysis.
This presentation explains how improving healthcare with data science can
save organizations time and money by targeting actions that will help them
reach their goals, while avoiding spending resources on measures less likely
to lead to desired outcomes.

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
As part of an initiative to reduce readmissions in
its orthopedic surgery population, a large health
system proposed preoperative optimization
measures for each patient based on that
individual’s circumstances.
For example, one measure might recommend
delaying total joint replacement surgery for a
severely obese patient until their BMI was within
a range often associated with better
postoperative outcomes.
To help patients achieve this goal BMI range
before orthopedic surgery, severely obese
patients could undergo bariatric surgery, a
procedure to make the stomach smaller.

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
However, when this health system used data
science analysis (specifically, a logistic
regression model) to test its hypothesis about
BMI and rate of readmissions, it found that BMI
was not associated with lower readmissions.
The regression model insight showed the
health system improvement team that an
intervention (e.g., bariatric surgery) to reduce
BMI before orthopedic surgery may not be an
effective readmission improvement measure.
The system could better allocate its resources
to meet its goals.

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
For this specific analysis, data scientists elected to
use a statistical model instead of a machine learning
model due to the different purpose each serves.
For example, statistical models are designed to
understand relationships between variables,
whereas machine learning models are designed to
make the most accurate predictions possible.

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
There are four key reasons data science helps
health systems better align measures with their
improvement goals and maximize their ROI:
1. Measures Aligned with Desired Outcomes
Drive Improvement
2. Improvement Teams Focus on Processes
They Can Impact
3. Outcome-Specific Interventions Might
Impact Other Outcomes
4. Identifies Opportunities with Optimal ROI

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
#1–Measures Aligned with Desired Outcomes Drive Improvement
To ensure that the health system’s
preoperative optimization measures were
statistically associated with its target
outcomes, the improvement team worked
with data scientists to develop a logistic
regression model.
The goal of the regression model was to
understand how the risk factors from the
proposed optimization measures were associated
with readmissions, as well as to control for other
patient-specific attributes—rigorous improvement
over more simple methods—to better understand
how each factor affected the target outcome.

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
#1–Measures Aligned with Desired Outcomes Drive Improvement
Data scientists used several control variables
for the regression model:
Comorbid conditions
Gender and age
Living arrangement
Tobacco or alcohol use
Behavior disorders
Facility where treated
Date of the procedure (admit date or other)
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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
#1–Measures Aligned with Desired Outcomes Drive Improvement
After accounting for the risk factors described
above, the output from the model showed
that patients with a behavior disorder, low
hemoglobin, renal disease, and those taking
opioids before orthopedic surgery or who
were male tended to fare worse in regards to
readmissions within the health system’s
orthopedic population.
Conversely, patients undergoing knee
procedures (compared with hip procedures)
and those who reported using alcohol tended
to fare better.

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
#1–Measures Aligned with Desired Outcomes Drive Improvement
The analysis, however, showed that some of
the factors the improvement team expected
to be associated with readmissions did not
have a statistically significant association.
For example, a higher BMI was not
statistically significantly associated with
readmissions, and the magnitude of the
association was approximately zero.

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
#1–Measures Aligned with Desired Outcomes Drive Improvement
This implied that bariatric surgery wasn’t an
effective way to prevent readmissions among
patients undergoing orthopedic surgery, as
the surgery did not impact the likelihood that
an obese patient would return to the hospital.
If these patients were to undergo bariatric
surgery solely to reduce their risk of
readmission, they might be going through
an unnecessary procedure, including its
associated costs and risks.

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
#2–Improvement Teams Focus on Processes They Can Impact
To successfully impact orthopedic surgery
readmissions, the data scientists and
improvement team needed to identify
factors strongly statistically associated with
readmissions and determine whether they
could affect change around those factors.
The improvement team worked through the
factors with strong associations with
improvement to assess whether they could
improve an existing process or determine if
there was a new process they had the
resources to implement:

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
#2–Improvement Teams Focus on Processes They Can Impact
The team discussed opportunities to
recommend that patients with behavior
disorders follow up with their primary
care provider or a specialist—a light
touch intervention tied to a factor
(behavior disorder) strongly associated
with postoperative readmissions.

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
#2–Improvement Teams Focus on Processes They Can Impact
The team also discussed how patients
with an active opioid prescription before
surgery may be experiencing more pain
and be more susceptible to postoperative
complications.
These patients might benefit from
proactive conversations around pain
management, including understanding
what level of pain to expect, what level
requires immediate medical attention,
and the role and risks of pain medication.

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
#3–Outcome-Specific Interventions Might Impact Other Outcomes
Additional regression models provided other
balance measures (outcome measures that
may be important but aren’t the focus of the
readmissions project) the same level of
attention in the statistical analysis as
readmission measures.
The analysis showed how optimizing a
process for readmissions might also help or
hinder outcomes including length of stay
(LOS), mortality, or cost.

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
#3–Outcome-Specific Interventions Might Impact Other Outcomes
Reducing preoperative BMI may be an effective
measure for goals other than reducing readmissions.
For example, statistical modeling showed that a high
BMI was associated with a longer LOS following
orthopedic surgery.
Though bariatric surgery appeared to be an
ineffective measure for reducing readmissions, it
may be an effective action if reducing LOS is an
important improvement goal.

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
#4–Identifies Opportunities with Optimal ROI
To sustain improvement work, organizations
must consider if the effort invested in improving
specific processes will yield a worthwhile ROI.
Otherwise, their energy and resources are
better spent on other outcomes.
When data didn’t link patient BMI to
readmission rates, the improvement team
assessed other preoperative factors that might
identify patients more likely to be readmitted.
Looking retrospectively at reasons for
readmitting helped the team understand
why patients were returning.

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Four Reasons Data Science Drives Effective
Improvement and Optimal ROI
#4–Identifies Opportunities with Optimal ROI
Backed up by data and rigorous analysis, the
team determined that, given that most orthopedic
readmissions were unrelated to surgery, a
systemwide approach to readmission reduction
would be more effective than implementing
process improvements by department.
The team further supported this conclusion
after assessing the potential impact and volume
behind specific preoperative interventions,
ultimately changing its improvement focus to
systemwide data science-driven initiatives.

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Improving Healthcare with Data Science by Testing the
Hypothesis and Identifying the Right Opportunity
When the organization followed the data and
used more advanced data science methods to
evaluate its optimization criteria, it found that
delaying orthopedic surgery based on a patient’s
specific preoperative attributes may not be an
effective measure for avoiding readmissions.
The improvement team learned that many of the
factors associated with increased readmissions
do not tie to processes they can control directly.
The team also learned that readmission
processes it can impact may only require only
a light touch but yield meaningful improvement
and favorable ROI.

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Improving Healthcare with Data Science by Testing the
Hypothesis and Identifying the Right Opportunity
Health systems can decrease the risk, cost, and
time associated with outcomes improvement and
accelerate the process by using data science to
help determine which measures will help them
meet their goals for their specific populations.
As the industry continues to work toward out-
comes improvement, leading organizations will
rely on data science and machine learning to test
hypotheses, identify opportunities faster and more
accurately, and ensure that their improvement
measures support their overall goals.

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For more information:
“This book is a fantastic piece of work”
– Robert Lindeman MD, FAAP, Chief Physician Quality Officer

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More about this topic
Link to original article for a more in-depth discussion.
Why Health Systems Must Use Data Science to Improve Outcomes
Prescriptive Analytics Beats Simple Prediction for Improving Healthcare
David Crockett, Ph.D., Research & Predictive Analytics, Sr. Director
7 Features of Highly Effective Outcomes Improvement Projects
Brant Avondet, VP of Client Operations
Machine Learning, Predictive Analytics, and Process Redesign Reduces Readmission Rates
by 50 Percent – Health Catalyst Success Story
Accuracy of Readmission Risk Assessment Improved by Machine Learning
Health Catalyst Success Story
Hospital Readmissions Reduction Program: Keys to Success
Bobbi Brown, Sr. VP

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Taylor joined Health Catalyst in December 2014 as a Data Architect. Prior to coming to
Health Catalyst, he worked for the Colorado Department of Health Care Policy and
Financing as a Budget and Data Analyst. Taylor has a Master’s degree in Economics
from the University of Colorado.
Taylor Larsen