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The Number One Skill for a
Healthcare Data Analyst

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External factors are re-shaping the healthcare
delivery landscape: mergers and acquisitions,
at-risk contracting between payers and
hospitals, a combination of reimbursement
models, and a complex payer mix.
This is the reality of healthcare today.
There’s a real need for healthcare analysts to
understand the pressures on the system so
that their analyses can help leadership
develop strategies to improve care delivery
and keep clinic doors open and profitable.
Healthcare Data Analysts

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For a top-performing healthcare data analyst,
it’s insufficient to simply model and forecast
increasing volumes and charges.
She must do that and explore care models
that deliberately drive profit away from
hospitals into ambulatory settings while
being mindful of the impacts (good or bad)
regarding their at-risk contracts.
She must also think about getting much
further upstream in a care continuum to
support population health initiatives.
Healthcare Data Analysts

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Health systems don’t hire analysts to
run reports or build dashboards.
These duties may be assigned to an
analyst, but that’s not where their value
lies. They hire healthcare data analysts
to solve problems.
This presentation demonstrates the
step-by-step problem-solving approach
used by the best healthcare data
analysts today using real-world
examples to show the tremendous
value to healthcare organizations.
Healthcare Data Analysts

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The best healthcare data analysts maximize
their value to the organization by using their
problem-solving skills to become a partner
for clinical and operational improvement.
They also use a common approach to
solving problems. Their approach involves
the following pattern of thinking:
Step 1: Healthcare operations
Step 2: Healthcare Data
Step 3: Technical Skills
Step 4: Tools
Lead with “What Problems Need to be Solved?”
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Step 1. Healthcare Operations
First, the top healthcare data analyst asks
lots of questions that seek understand,
• “What is the problem we’re trying to solve?”
• “Why does it matter?”
This deliberate questioning helps to
tease out the best opportunities.
Lead with “What Problems Need to be Solved?”

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Step 2. Healthcare Data
Next, the analyst asks:
• “What information would be needed to
help solve this problem?”
Top analysts turn data into information so
what they’re getting at is:
• “What data do I need to begin to address
the issue and where do I find it?”
Lead with “What Problems Need to be Solved?”

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Step 3. Technical Skills
After finding the data, the healthcare data
analyst then asks:
• “How does this data need to be organized,
analyzed, and presented to address the
problem?”
• “Who do I need to present this information
to so they can make a decision based on
the information I’ve shared?”
Lead with “What Problems Need to be Solved?”

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Step 4. Tools
The last step top analysts take is reach for
their tools. This process reframes the role
of technology.
When analysts see their role as problem
solvers, they effectively become partners
for clinical, financial and operational teams.
Lead with “What Problems Need to be Solved?”

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The pairing of technical aptitude with domain
expertise becomes a sustainable model for
analysts to become a tremendous asset to
be leveraged.
In the words of Jim Collins in Good To Great:
Technology cannot turn a good enterprise into
a great one, nor by itself prevent disaster.”
The same holds true for analysts. No
technology will make someone a great
healthcare data analyst.
Lead with “What Problems Need to be Solved?”

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Lead with “What Problems Need to be Solved?”
Figure 1: Healthcare Analysts must have technical aptitude coupled with knowledge of healthcare data and operations.

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While this model is simple to understand, it’s
rather difficult to implement.
One of the most challenging aspects of this
model is the interchange between technical
experts and domain experts.
That interplay is fascinating to observe.
A Real-World Application:
Building a Diabetes Registry

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The manner in which clinicians are trained
to think about data is strikingly dissimilar to
the way analysts think about data.
When the two are in the same room
together, there is a real risk that they will
talk past one another.
Great analysts have learned that for
them to add value, it’s not about what
they say, it’s about what their audience
hears that matters.
A Real-World Application:
Building a Diabetes Registry

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Looking at a real-world use case of building a diabetes
registry will help illustrate this.
The design of the registry begins with the why:
• “Why do we need to build the diabetes registry
A Real-World Application:
Building a Diabetes Registry

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Through questioning of the right stakeholders,
the analyst learned that the organization has
embarked on a population health initiative as
part of a marketing campaign to raise
community awareness and healthy living.
The timing of this contract coincided with
an increased physician network of primary
care clinics.
A Real-World Application:
Building a Diabetes Registry

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The focus for the initial registry build is in
support of population health and the target
audience is primary care physicians with
diabetes on their panels.
With further questioning, the analyst learned
that the primary care physicians are excited to
have a more robust set of clinical definitions
for who has diabetes.
But, they’re also concerned about being held
accountable for diabetics on their panels if the
patient-provider attribution model is not clear.
This told the analyst he needed to get
physician buy-in.
Understanding the Business Drivers

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He also learned that a small portion of physician
compensation is tied to the effective management
of the diabetic population, making the attribution
model accuracy even more important.
By listening carefully and asking lots of questions,
he developed a rule-set for the diabetes
population, driven by the PCPs (Figure 2).
Understanding the Business Drivers

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Understanding the Business Drivers
Figure 2: Rule set for diabetes population

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Looking at this list of qualifiers, do any
seem peculiar?
Knowing that the business driver for this
registry is a population health initiative with
a focus on PCPs treating diabetics in the
ambulatory setting, the inclusion of rules
one and two is especially brilliant.
Here’s why: physician leadership points out
that an inpatient or ED visit for diabetics
could be viewed as a potential failure within
the population health effort.
Understanding the Business Drivers

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The analyst took this to heart.
By creating the rules through careful
questioning, the analyst was able to
establish a baseline that was illuminating for
the population health clinical leadership.
It showed a significant number of diabetics
were slipping through the cracks, much more
than this health system had anticipated.
Understanding the Business Drivers

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Through a simple but elegant analysis of the rules,
the analyst learned the following:
Many patients were diagnosed for the first
time with diabetes in inpatient or ED settings.
Population health leadership called most of
these “failures of the care delivery system.”
Some patients had insulin orders and fills without
an accompanying diagnosis of diabetes.
There was less than 50 percent compliance
of PCPs putting diabetes on the active problem
list for their diabetic patients.
Many patients were rapidly approaching the
qualifying criteria of an A1C > 8 but hadn’t
crossed it yet.
Understanding the Business Drivers
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In this example, the healthcare data analyst
was able to highlight genuine opportunity for
improvement because he understood the
problems the clinicians and leadership were
trying to solve.
This analysis ultimately informed the
strategy for population health.
Understanding the Business Drivers

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In this diabetes registry example, in order to
accurately populate the clinically-defined
rule set, the analyst needed to understand
which data elements were both available for
use and approved by key stakeholders.
For these rules, the analyst used ICD
codes, Rx orders and fills, lab orders
and results, and the problem list
within the EMR.
Data-Driven Rules of Cohort Inclusion

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Of course, not all ICD codes were used nor
needed: only those that were specific to diabetes
and even more detailed in definition, only those
that were a primary diagnosis for an inpatient or
ED encounter.
Similarly, only A1C lab orders and results came
from the laboratory data system or EMR.
Healthcare analysts have been given an
incredible gift through the use of coding systems.
Data-Driven Rules of Cohort Inclusion

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The embedded meaning associated with
these codes can be exploited to produce
powerful analysis.
But these can only be leveraged if the
analyst understands the healthcare data
captured within coding systems.
An expert analyst of this caliber must have
a deep understanding of the inherent
meaning in coded data, where it’s captured,
and why it’s captured in clinical work flow.
Data-Driven Rules of Cohort Inclusion

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The best analysts are in top technical form.
In the diabetes registry example, let’s walk
through how these technical competencies
shaped the building of the cohort.
1. Data Query
2. Data Movement
3. Data Modeling
4. Data Analysis
5. Data Visualization
Five Necessary Technical Skills for a
Top Healthcare Data Analyst

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#1 – Data Query
SQL was used to join primary diagnosis
to encounters as well as patient types
for the first three rules.
Separate queries were developed for
each data source, claims, and the EMR.
Data query was used to tease out
assumptions about where lab orders
and results lived within the EMR.
Five Necessary Technical Skills for a
Top Healthcare Data Analyst

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#2 – Data Movement
ETL, as its commonly known, leveraged
the SQL queries to pull only the needed
data for rules.
The resultant data sets were staged
within a dedicated analytic environment.
By following ETL best practices,
questions from the clinical team around
data integrity and data lineage were
easily addressed.
Five Necessary Technical Skills for a
Top Healthcare Data Analyst

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#3 – Data Modeling
Data Modeling rode on the heels of data
query and data movement by creating a
landing zone for patient-rule qualifications,
including patients who qualified for more
than one rule or the same rule multiple times.
Every instance was captured and by so
doing, was then available for analysis.
Data modeling plays a pivotal role in
capturing what the business cares about
and documenting that in a database,
primed for analysis.
Five Necessary Technical Skills for a
Top Healthcare Data Analyst

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#4 – Data Analysis
Data Analysis came after data query,
data movement, and data modeling.
With a deep understanding of the
population health business drivers
and the problems to be solved, the
analyst could do what he does best:
analyze the data within the data
model in light of the business drivers.
Five Necessary Technical Skills for a
Top Healthcare Data Analyst

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#5 – Data Visualization
In this diabetes registry build, up to this
point, we were only interested in what
skills were needed to develop the cohort
for inclusion/exclusion.
The analyst used Excel bar charts to
highlight the counts of rule qualification.
This was sufficient to get buy-in for the
rules of inclusion.
Five Necessary Technical Skills for a
Top Healthcare Data Analyst

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Later a tool like Qlik or Tableau consumed the
subsequent data models and highlighted the
baked-in analysis, but this wasn’t necessary
for the inclusion criteria.
These are the five technical skills absolutely
necessary for analysts to seize the best
opportunities within healthcare systems.
To be an effective healthcare analyst will
require technical aptitude, the data skills
outlined above, coupled with knowledge of
healthcare data and operations.
Five Necessary Technical Skills for a
Top Healthcare Data Analyst

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The more analysts understand the problems
their health systems are trying to solve, the
more value they will be able to provide with
their insights.
The lessons for healthcare data analysts and
healthcare leaders are clear.
Healthcare leadership must strive to elevate
the role of the healthcare analyst to be a
problem solver, not just a report writer.
Summary

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The best analysts harness technology without
becoming overly reliant on it supplanting the
five key data skills.
They must understand the business needs to
provide maximum value to the organization.
And lastly, healthcare leaders need to invest in
education for analysts to grow deeper into
healthcare data and operations understanding.
Summary

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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.
The Number One Skill for a Healthcare Data Analyst
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Three Principles for Making Healthcare Data Analytics Actionable
Health Catalyst Insights
The Number One Secret of Highly Effective Healthcare Data Analysts
Sarah Provan, BS, Operations ASO, VP; Caitlin Kelly, Clinical Data Analyst

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
John joined Health Catalyst in September 2011 as a senior data architect. Prior to Health
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.
John Wadsworth