Making the most of a healthcare data analyst’s knowledge is a key component to getting the best ROI from a hospital improvement project. But all too often, analysts serve merely as data validators — they justify the data that leadership wants validated. Because analysts aren’t decision makers, they don’t have the authority to ask the questions that can save a health system millions. Empowering analysts, however, enables them to ask the right questions — and find the right answers — that will lead to significant savings.

1. What is the ROI
of investing in a
healthcare data
analyst?
— John Wadsworth
A real example
of saving millions

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Return on Investment - ROI
Are you interested in getting a
return on investment (ROI)
from your healthcare analytics
investment?
What follows is a real-world
example of how that is done.

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ROI – Real World Example
A few years ago, a health system
came to us and said…
We need to build an
observation wing.”
But when changes in patient demo-
graphics and demand no longer
justified the wing, it was ultimately
re-purposed to meet growing
emergency department (ED) needs.

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ROI – Real World Example
An interesting trend developed.
Observation patient volumes
steadily rose, while Medicare
reimbursements for the ED
population continued to decline.
As a result of the new trend, the
health system’s leaders were
looking for ways to optimize
clinical resource allocation for
their ED observation patients.

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EDW Driven Decision Making
The health system already had a
fully functional enterprise data
warehouse (EDW) in place.
By digging deep into its insights
with our help, their leadership
expected to make data-informed,
strategic decisions about the
details of the observation wing.

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EDW Driven Decision Making
We asked what answers they
expected to get from their EDW.
They needed these three
important questions answered:
1. How many observation beds
are needed for the wing?
2. What clinical staffing will be
needed for the new wing?
3. What will it cost to build the
new wing?

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Question One: Beds Needed
To answer this question, we would need to
leverage clinical data from the EMR source
mart to identify trends for patients classified
as observation patient types.

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Question Two: Staff Needed
Calculating the clinical staffing would
require a combination of clinical data as
well as HR data to build a staff model
based on historical patient volumes.

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Question Three: Cost to build
We would need to mine costing data in order to
forecast the cost to build the new wing.
(Data revealed the cost would be between $0.5 and $1
million per bed - $2.5 to $5.0 million for five beds)

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Evaluation
Our evaluation would provide
answers that could be used to
determine if building the new
wing was the right solution.
Or, was there an alternate
course of action that made
more economic sense?

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A Teaching Moment
Our evaluation was also a
teaching moment for the
health system client.
They welcomed further
analysis to better inform the
decision-making process.
We sought clues in the data
trying to understand the root
cause of increased observa-
tion patient volumes.

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Clue #1
Roughly two out of three
observation patients had a
complaint of some chest pain.
This is a very common com-
plaint for patients presenting to
the ED that end up as observa-
tion patients.
However, the fact that so many
of the observation patients had
the same concern was a clue.

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Clue #2
Nearly every patient that
presented in observation had
been a previous patient.
This was revealing because it
meant the increased observa-
tion volumes did not represent
a material increase in new
patient volumes.

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Clue #3
Of those patients complaining
of chest pain, four out of five
had been previously diagnosed
with heart failure from the
cardiology clinic.
This was a BIG clue.
It meant these patients were
aware that cardiology care was
available through the clinic, yet
they were presenting at the ED.
Why?

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Clue #4
For those chest pain patients,
three out of four arrived
between 5:00 and 10:00 p.m.
What was significant about this
time frame?
Why this five-hour window?
One quick phone call to the
cardiology clinic answered the
lingering question — the clinic
closed at 5 p.m.

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Data Analysis Led to Simple Answer
Data revealed that because
the cardiology clinic closed at
5:00 p.m., patients feeling
chest pain went to the next
available known care location
to get attention: the emergency
department.

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Data Driven Decision: Best ROI
The analyst leading the data
mining effort recommended:
1. Extend the cardiology clinic
hours until 10 p.m.
2. Don’t spend the $2.5
million to $5 million for an
observation wing.
The health system was thrilled.
So were we.
If the analyst on this project
were paid $75,000 a year as
a base salary, the value of
this decision to avoid
unnecessary significant
capital spend justified her or
her role for 30 to 60 years.

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More about this topic
4 Ways to Enable Healthcare Data Analysts to Provide Their Full Value
Russ Staheli, Technical Director
Health Data Stewardship and Its Importance in Healthcare Analytics
John Wadsworth, VP of Client Technical Operations
The Best Organizational Structure for Healthcare Analytics
John Wadsworth, VP of Client Technical Operations
3 Frequent Mistakes in Healthcare Data Analytics
John Wadsworth, VP of Client Technical Operations
Finding $5.7 Million with a Healthcare Data Warehouse
John Wadsworth, VP of Client Technical Operations
Link to original article for a more in-depth discussion.
What is the ROI of investing in a healthcare data analyst?

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For more information:
Download Healthcare: A Better Way.
The New Era of Opportunity
“This is a knowledge source for clinical and
operational leaders, as well as front-line
caregivers, who are involved in improving
processes, reducing harm, designing and
implementing new care delivery models, and
undertaking the difficult task of leading
meaningful change on behalf of the patients
they serve.”
– John Haughom, MD, Senior Advisor, Health Catalyst

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
John Wadsworth joined Health Catalyst in September 2011 as a senior data architect.
Prior to Catalyst, he worked for Intermountain Healthcare and for ARUP Laboratories
as a data architect. John has a Master of Science degree in biomedical informatics
from the University of Utah, School of Medicine.