Leveraging Healthcare Analytics to Reduce Heart Failure Readmission Rates

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www.healthcatalyst.comProprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Leveraging Healthcare Analytics to Reduce
Heart Failure Readmission Rates
By Kathleen Merkley

Heart Failure Readmissions: One of the
Most Common Sources of Readmissions
Heart failure, including what was
formerly referred to as congestive
heart failure,* is an extremely
serious problem in the United
States for many reasons.
First, there are a high number of
patients who are suffering and
dying from this disease.
Second, the financial burden to
treat heart disease patients is
becoming an alarming public
health issue.
• Heart failure accounts for $38
billion of annual healthcare
spending.
• Heart failure accounts for 43% of
Medicare spending on 14% of
Medicare beneficiaries.

Many of these patients will be
readmitted to the hospital soon after
being seen for their heart condition
adding cost to the patient’s
treatment.
More than 25 percent of patients hospitalized for
heart failure will be readmitted to the hospital
within 30 days of discharge.
The top reason for readmission with the Medicare
fee-for-service patient population is for patients
suffering from heart failure. (2012 research)

Excessive readmissions tend to
indicate suboptimal care, so
government and commercial
payers are focusing on 30-day
readmission rates as a new
quality measure for hospitals.
Some proposed methods of reducing
heart related readmissions include:
• Patient education at discharge
• Appropriate medications prescribed
• Medication reconciliation
• Timely access to care after discharge
• Hand-off communications between
primary care providers and acute
care facilities
• Rapid distribution of hospital
documentation to primary care
providers
• Home health interventions
• Follow-up phone calls

Heart Failure Readmissions: One of the Most
Common Sources of Readmissions
Heart failure readmission dashboard tracks patient care

4 Ways to Leverage Healthcare Analytics to
Reduce Heart Failure Readmission Rates
Now that hospitals have to
comply with this new quality
measure or face financial
penalties, how should they
address heart failure
readmission rates? There is a
solution: advanced analytics
Sophisticated analytics are
able to comb through
terabytes of clinical data to
reveal opportunities to
improve quality and efficiency.
Here are four ways hospitals can
use their data to improve heart
failure readmission goals:

You can’t improve what you
don’t measure. It is very
important to establish
readmission baselines, track
performance metrics, and
distribute information to
everyone who is trying to
reduce readmissions. This is
the first step towards quality
improvement.
FOUR WAYS TO
REDUCE
READMISSIONS
1 Understand your current readmission
rates for your heart patients

Realize, however, that if
you’re looking old data, it’s
difficult to engage clinicians in
clinical improvement
initiatives. Adopting an
enterprise data warehouse
(EDW) could help ensure the
data is current.
FOUR WAYS TO
REDUCE
READMISSIONS
2
Establish 30- and 90-day
readmission baseline measures
for heart failure patients.

For heart patient readmissions,
there are three types of
balance measures:
1. Patient satisfaction rates
2. Emergency department
(ED) visits
3. Observation stays
FOUR WAYS TO
REDUCE
READMISSIONS
3
Be aware of balance measures
(changes designed to improve one part of the system
without causing new problems in another part of the system)

Integrate clinical, financial, and
patient satisfaction data using
an EDW to identify all patients
with a primary diagnosis of
heart failure and then stratifies
the populations as either high-
or low-risk for readmission and
plan intervention accordingly.
FOUR WAYS TO
REDUCE
READMISSIONS
4 Use an enterprise healthcare data warehouse

Heart Failure Readmissions: One of the Most
Common Sources of Readmissions
As shown in the Health Catalyst Heart Failure Readmission Dashboard,
you can lower readmission rates by using bundles intervention tracking.

Positive Results using the EDW and the
Heart Failure Advanced Application
21 percent reduction (seasonally adjusted)
in 30-day heart-failure readmissions
2X increase in the number of phone calls made
to patients within 48 hours of discharge
63 percent increase in physician medication
reconciliation post discharge
One Health Catalyst client saw these results
using the late-bindingTM Data Warehouse.
14 percent reduction (seasonally adjusted)
in 90-day heart-failure readmissions

Gratitude for Readmissions Improvements
From a professional perspective, it’s
important to understand the challenges
health systems face as they work to
reduce heart failure readmissions. But
from the personal perspective watching
a loved one suffer from a potentially
preventable readmission, I’m grateful
for the progress we’re making in
healthcare. In specific, I am thankful for
government scrutiny, progressive
clinicians, and healthcare IT solutions
that are beginning to contribute to the
reduction in heart failure readmissions.
– Kathleen Merkley
Health Catalyst
‘‘

More about this topic
How to Use Analytics to Reduce 30-day Heart Failure Readmissions
An academic medical center case study
Product Demo – Heart Failure Readmissions
Watch a 7-minute demo of our heart failure readmission application
References
http://cms.hhs.gov/Research-Statistics-Data-and-
Systems/Research/HealthCareFinancingReview/Downloads/08Springpg1.pdf
http://www.ncbi.nlm.nih.gov/pubmed/22653415
*The American College of Cardiology and the American Heart Association prefer
the use of “heart failure” over “congestive heart failure.”
http://content.onlinejacc.org/article.aspx?articleID=1695825

Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Kathleen Merkley joined Health Catalyst in March 2013, as an Engagement
Executive. Prior to coming to HC, she worked for Intermountain Healthcare as
the corporate clinical IT implementation manager. Kathleen is a registered
nurse/nurse practitioner and just received her doctorate in nursing practice
from the University of Utah in May 2013.
More by this author:
Overcoming Clinical Data Problems in Quality Improvement Projects
Defining Patient Populations Using Analytical Tools: Cohort Builder and Risk
Stratification
A Key to Measurable Healthcare Quality Improvement: Use AIM Statements

Leveraging Healthcare Analytics to Reduce Heart Failure Readmission Rates

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