Health systems have vast amounts of data, but frequently struggle to use that data to solve strategic problems in a timely fashion. A healthcare analytics team, made up of the right people with the right tools and skillsets, can help address these challenges. This article walks through the steps organizations need to take to put an effective analytics team in place. These include the following: Recognizing the need for change. Demonstrating the value of an analytics team. Conducting a current state assessment. Identifying solutions. Implementing a phased approach. Building a roadmap. Making the pitch. Putting the roadmap into action. The article also includes the foundation skills to look for when putting together the team and tips on how best to organize.

1. How to Build a Healthcare Analytics Team and
Solve Strategic Problems

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Building a Healthcare Analytics Team
This report is based on a 2018 Healthcare Analytics Summit presentation
given by David M. Wild, MD, Vice President of Lean Promotion, and Chris
Harper, MBAi, MPM, Director, Business Architecture & Analytics, at The
University of Kansas Health Center, entitled, “How We Developed an
Advanced Analytics Team to Solve Our Strategic Problems.”
David M. Wild, MD
Vice President of Lean Promotion
The University of Kansas Health Center
Chris Harper, MBAi, MPM,
Director, Business Architecture & Analytics
The University of Kansas Health Center

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Health systems have a wealth of data
available, but often struggle with leveraging
that data to solve strategic problems.
A healthcare analytics team—made up of the
right people with the right skills—can go a
long way toward addressing organizational
challenges and improving patient care.
These analysts use advanced analytics to
produce actionable insights, creating positive
impacts throughout the organization.
This presentation will walk through the
steps needed to put an effective applied
analytics team in place.
Building a Healthcare Analytics Team

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Healthcare organizations often struggle with
the time-value curve of data. The time it
takes between a clinical event and being
able to collect and use data to make and
measure an improvement is far too long.
Health systems can shorten the time-value
curve of analytics with an applied healthcare
analytics team.
These trained analysts, working with the
right tools, can get to actionable data faster,
providing value in improved outcomes,
operational efficiency, and the bottom line.
The Value of a Healthcare Analytics Team

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Organizations face a data quotient problem, with
vast amounts of data available today, yet data
assets, data usage information, and people with
data skills are often scattered throughout an
organization and aren’t strategically aligned.
While healthcare organizations can often collect
needed data without having a dedicated analytics
team, the process is slow, inefficient, and difficult
to manage.
A dedicated team, with the support of key
stakeholders, can focus on gathering accurate
data more quickly and turning that data into
insights that drive results.
The Value of a Healthcare Analytics Team

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Labor in any organization is a finite
resource, and because data analytics
can be time intensive, there is a limit to
the data employees and consultants can
process and report.
In many healthcare organizations, much
of data analysts’ time is spent managing
reports, leaving little time for analysis
and consulting.
Conduct a Current State Assessment

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As required reporting volume grows, time
for analysis shrinks, further compounding
the problem.
To identify solutions, organizations must
understand the needs and limitations of
processes in use.
A current state assessment can
reveal this information and is vital
for health systems challenged by
collecting and using data effectively.
Conduct a Current State Assessment

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An assessment highlights gaps in data
analysis and provides insights into
duplicative or inefficient processes.
It might include looking at who collects and
reports data, what tools they are using, and
where the data comes from.
Conduct a Current State Assessment

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The next step is to identify solutions to challenges
uncovered during the current state assessment.
For example, if the organizational goal is
to become more efficient and effective at
gathering and reporting data, leaders must
find a way to change the productivity curve.
How can they decrease the time it takes
to report data while increasing the time
available to analyze the data for
actionable insights?
To answer this question, leaders should
map out requirements for a viable solution.
Identify Solutions

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This might look like the following list of requirements:
Accommodates increasing report demand with an analytic platform approach.
Provides more value-add activities, such as data analysis and consulting with
stakeholders.
Introduces a scalable reporting platform that uses centralized and clearly defined
standardized data elements while supporting business unit reporting autonomy.
Speeds the delivery of more dynamic information to decision makers.
Allows key performance indicators (KPIs) and detailed information to flow all
the way to the desktop level of the organization.
Increases investment in data analysis and consulting with stakeholders to add value.
Moves to a scalable reporting platform that uses centralized, clearly defined
standardized data elements and supports autonomy for business units.
Delivers more dynamic information to decision makers faster.
Identify Solutions
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Identify Solutions
Identifying Solution Requirements
Changing the Productivity Curve Solution Requirements
• Accommodate increasing report demand with
platform approach.
• Provide more value-add activities such as data
analysis and consulting with stakeholders.
• Introduce a scalable reporting platform that uses
centralized and clearly defined standardized data
elements while supporting business unit reporting
autonomy.
• Speeds up the delivery of more dynamic
information to decision makers.
• Allow KPIs and detailed information to flow all the
way to the desktop level of the organization.
Figure 1: Identifying solution requirements

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Figure 1 shows that report development takes
up most of an analyst’s time, which leaves
little time for analysis and consulting.
Because labor is limited, as report volume
increases, the unmet demand for analysts’
time increases. Automating reports changes
the productivity curve.
If analysts could significantly reduce time
spent developing reports, they could add
more value to the organization doing what
they’re trained to do—analyze data and
provide insights from that analysis.
Identify Solutions

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Once the organization’s challenges and
potential solutions are mapped out, it’s
time to get buy-in about how to get from
the current state to a future, desired state.
To do this, organizations must look at both
people and tools and invest in team
members to prepare them for the task.
Identify Solutions

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A frequent debate when building a
healthcare analytics team is whether
or not to have a centralized approach.
Instead, it may better serve the organization
to decide the best approach from a cultural
standpoint and think more about the
technical architecture of the team.
This might include looking at what skills
will be needed on the team and then
deciding how to organize those people.
Identify Solutions

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A skills assessment provides data about whether
current staff have or can quickly be trained in the
skills necessary to produce actionable knowledge.
Not every team member must possess each skill,
but there are several skills the team, as a whole,
must have to be successful.
Identify Solutions

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Below are sample lists of foundational skills to include in a skills assessment:
Identify Solutions
Foundational skills (general)
Technical skills, such as visualization basic
application, SSIS, SSRS, visualization
application, EMR radiology, and EMR
reporting.
Business/clinical skills, such as EMR
database modeling, EMR front-end workflow,
and EMR build/workflow.
Analytical skills, such as regulatory and
compliance (HIPPA/ICD-10), clinical
requirement gathering, and healthcare
quality metrics.
Foundational skills (specialty)
Clinical terminology standardization
Data management development
Data modeling.
Data mining
Data visualization development
Advanced statistical modeling.
Statistical analysis

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An analytics team must have many of
these foundational skills and know how
to apply them in order to provide
efficient and effective data analysis.
Once the team is up and running, the
organization might later decide to
add additional specialty skills to
the team, either through training,
hiring, or contracting.
Identify Solutions

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Having effective tools is also imperative.
Not all data analysis software is created
equal, so it’s important to have the right
technology.
Technology needs vary from organization
to organization, but ideally organizations
invest in a comprehensive solution with a
variety of tools that will help speed the
variety of tasks analysts spend the bulk
of their time doing.
Identify Solutions

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An organization might spend a billion dollars
on technology and building out a pristine data
analytic platform, but this does nothing if the
organization doesn’t have the right people to
drive insights, and ultimately improvements.
Neither people nor tools alone will do the job.
Technology is vital to improving efficiency and
accuracy of data reporting, and so is having
the right people on board.
Identify Solutions

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Developing an effective healthcare analytics
team takes forethought and planning.
Many organizations use a phased approach
when building a team of analysts who are
ready and able to solve the most pressing
strategic problems.
This might include the following three phases:
Phase 1: Foundational capacity
Phase 2: Advanced capabilities
Phase 3: Predictive capability
Implement a Phased Approach to
Develop the Team
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Implement a Phased Approach to
Develop the Team
Phase 1: Foundational capacity – This is the operational
reporting layer. During this phase, organizations should invest
in developing core capabilities around retro-perspective
business intelligence (BI) and reporting and visualization tools.
Phase 2: Advanced capabilities – Phase two involves investing
in statistical and forecasting tools to answer strategic questions.
Phase 3: Predictive capability – This involves using predictive
analytic and statistical tools, along with an EMR for predicting,
monitoring, and reporting outcomes.
Figure 2: Phased layered analytics capabilities

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A roadmap is a guiding document that
clearly lays out where an organization is
and where it’s going, along with the
concrete steps to get there.
Part of building a roadmap involves
introspection and organizational analysis.
To do this, organizations need to take a
three-step approach:
Find the truth
Tell the truth
Face the truth
Build a Roadmap
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Find the truth
This problem identifying mission is the first
step in any process improvement initiative.
If an organization doesn’t understand what
the challenges are, finding and implementing
effective solutions will be difficult.
Finding the truth can be difficult for
organizations because “truth” is nebulous
and relative to individual points of view.
But organizations that can come to a
consensus on this point will be well
positioned for success.
Build a Roadmap

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Tell the truth
The second step is to present findings
to leadership in order to get buy-in for
potential solutions.
This takes diplomacy and sensitivity
and requires a clear sense of purpose.
Build a Roadmap

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Face the truth
The third step is to understand the
challenges.
It can take humility and being open
to the notion that change is
necessary to benefit patients.
It requires a community rather than
an individual focus.
Build a Roadmap

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Getting leadership buy-in to invest in an
applied healthcare analytics team requires a
clear, concise message. Data is an asset.
That’s a given in any healthcare organization.
Most health system leaders will agree that
analyzing data is critical—and that data
analysis is a supporting function of strategy.
So, the key to getting leadership on board
to invest in an analytics team may require
illustrating how to turn data analysis into a
function of competitive advantage rather
than just a supporting service.
The Pitch – How to Get Leadership Buy In

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This is challenging, however, if leadership
regularly makes decisions based on the
available data or even gut intuition.
To meet that challenge, the project leader
should make this pitch:
The benefit of a data analytics team is that
analysts will take data and, as quickly as
possible, develop information from that data.
They will then share what they’ve learned
with operational leads who can translate
that information into actionable insights.
The Pitch – How to Get Leadership Buy In

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It’s these insights that have an
immediate, measurable impact.
The pitch should also highlight the
competitive advantage of a dedicated
healthcare analytics team.
If the analytics team can do this work
better and faster than the competition,
what was once believed to be a
support service can become a
competitive advantage.
The Pitch – How to Get Leadership Buy In

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Once there is leadership buy-in, the next step
is to put the roadmap into action.
There are three general steps to building an
analytics team:
1. Ask the right questions to solve the right problems.
2. Use available resources to build the team.
3. Find space for the team to operate.
Put the Roadmap into Action

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1: Ask the right questions to solve the right problems.
Put the Roadmap into Action
Organizations that take time to ensure the
team is asking the right questions to solve
the right problems tend to have better results.
Zeroing in on what those questions are
takes a clear understanding of operations
and outcomes.
Knowing the biggest challenges and
identifying solutions moves the roadmap
from theory into action.

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2: Use available resources to build the team.
Put the Roadmap into Action
Limited resources can make building an analytics
team challenging, but it can be done.
Using available resources rather than having to
get additional budget approval can make the
process of building the team more efficient.
And, any team or process that adds value
without increasing expenditures is more
likely to get buy in.

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3: Find space for the team to operate.
Put the Roadmap into Action
Finding space for the analytics team
to operate can also be challenging.
If office space isn’t available, look for
alternative locations to set up
computer stations.
When these goals have been accomplished, the
healthcare analytics team can start doing what
they were created to do—analyzing data to
create actionable insights that can lead to
improved processes, care, and outcomes.

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From Actionable Insights to
Improved Patient Care
Data is power, and to unleash that power,
healthcare organizations need trained analysts,
the right tools for the job, and to carefully
consider the necessary steps to success.
The first step is to think it through. Real,
measurable change can happen when all
foundational work has been done.
The next step is to develop a plan that
uses an organization’s unique culture
and situation as a guide.

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From Actionable Insights to
Improved Patient Care
Every organization is different, so there is
no one “right” way to build and utilize an
analytics team.
Once stakeholders agree which strategic
problems to tackle first and understand
how an applied analytics team will drive
change, the team can get to work.
Finally, it’s important to remember the why.

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From Actionable Insights to
Improved Patient Care
Thoughtfully building a roadmap and
following it gives the applied analytics
team—and the data—the ability to shine.
A qualified, dedicated team of analysts
can provide actionable knowledge to
inform quality initiatives that will
positively affect patient care and
the patient experience.

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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.
How to Build a Healthcare Analytics Team and Solve Strategic Problems
Reducing Hospital Readmissions: A Case for Integrated Analytics
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John Wadsworth , Technical Operations, VP

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Other Clinical Quality Improvement Resources
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