How to Run Your Healthcare Analytics Operation Like a Business

How to Run Your
Healthcare Analytics
Operation Like a Business

© 2018 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
According to the Small Business Association (SBA),
30 percent of new businesses fail within two years
of operating. Fifty percent fail within the first five
years, and 66 percent fail within the first 10.
Hundreds of books and articles have been written
about the lessons learned from these high failure
rates and the principles that, when followed,
increase the likelihood of success.
These lessons and principles are
equally relevant when establishing
and growing an analytics operation
within a healthcare institution.
Effective Analytics Business Operations

A robust data analytics operation is no longer
optional for healthcare organizations; it’s
necessary for their survival.
While most health systems have invested
heavily in their analytics operation in both
time and money, many aren’t getting the
kind of ROI they need.
However, underinvestment in an analytics
operation is more common and problematic
than overinvestment.

In many health systems’ analytics operations,
overtaxed resources that can never satisfy
demands placed on them by the organization.
Analysts have long queues of requests for data
and reports. Then, data consumers frequently
have complaints about the data – it’s
often incorrect, irrelevant, or takes a lot
of back and forth to get what they want.
Customer satisfaction among healthcare
executives and clinicians is often low
because the data they get isn’t timely
and not contextualized in a way that
allows them to make important decisions.

For an analytics enterprise to be
successful, it needs to be run like
a business.
And, just like any business, it will
succeed or fail based on factors like
customer satisfaction, profitability,
return on investment, and potential
for growth.
This presentation will walk through
how to run a healthcare analytics
operation like a business using the
five-question framework (figure 1).
Five Question Framework for Analytics

Figure 1: Five questions to answer in order to run a successful healthcare analytics operation.

This framework was developed by Eric Denna,
Professional Services Senior Vice President at
Health Catalyst, and is used by healthcare
data analytics teams across the country.
Analytics directors who do this successfully
have the biggest impact on healthcare
outcomes and ensure both their team’s
and organization’s survival.

The answers to these five questions come
from implementing these five steps.
1. Define the Customer
2. Define the Offering
3. Measure Success
4. Define the Process
5. Define the organization

Step 1: Define the Customer
Who does the analytics team serve and what
are those customers trying to do?
In order to provide products or services in the
most effective way, any good business has a
keen understanding of who their customers
are and what they want.
Similarly, the first step in running an analytics
enterprise like a business is to define the
customer.

In most analytics operations, this will include
several different types of customers, such as
executives and frontline leadership, clinicians,
and improvement teams.
For this reason, it may be helpful to segment
the customers into different categories
according to their needs.

In a typical healthcare analytics operation, that segmentation might look
something like this:
Self-service
These are customers who
are sophisticated users or
consumers of data who
just need access, the
right tools, and some
training and orientation so
they can self-serve.
Guided tours
A cross-functional team
working on outcomes
improvement might
include a physician, a
nurse, and a financial
expert. Additionally, the
team might need
someone with analytic
expertise that can bring
the tools and expertise to
respond to requests as
they arise.
Information Monitor
An executive or other
frontline leader might
need a dashboard that
shows how many patients
are in the hospital, the
readmission rate, and
length of stay at any
given time. This customer
doesn’t want or need to
drill down or design
analytics, they only want
to understand the story
presented.
Regulatory Measure
Submitters
These customers pull
together data from a
variety of sources, often
lacking in completeness
and timeliness.
Thousands of hours are
spent understanding the
meticulous requirements
and assembling reports to
the satisfaction of the
regulatory bodies that
require them.

This is not a comprehensive list, but it’s a
good example of how to define and segment
customers of the analytics operation.
The key is to ask not only who are they, but
what are their goals?
Answering these questions will help with
the second step in running a healthcare
analytics operation like a business:
determining the best way to provide
the service.

Step 2: Define the Offering
What services does the analytics team provide
to help customers accomplish their goals?
Any good business has a plan, complete with
a clearly defined offering.
An important part of that plan is a roadmap
that shows how to systematically improve
or grow the business.
An effective analytics strategy also
relies on creating this roadmap in
order to clearly define the offering
and the way forward.

There are several important components of the road map as it relates to the
analytics strategy that the team will need to think through and determine:
Data Access
How can the analytics team ensure
broad access to data in a way
that’s secure?
Toolset Alignment
How can the team provide access
to the right tools to create insights
and build knowledge?
Education and Stewardship
What is the best way to guide
customers through the data
journey?
Marketing/Communication
How can the team communicate
the tremendous value they provide
to the organization?
Data Source Plan
How should the team prioritize the
list of data sources needed in the
data warehouse to meet demand?
Data Modeling
How to eliminate repetitive tasks
that every user would have to do
when they access the raw data?

While these are just some examples of issues the
analytics team needs to think through to define their
offering, Mike Noke, a Senior Vice President in
Professional Services at Health Catalyst, explores
each of these success factors in his article on
driving strategic advantage through widespread
analytics adoption.

Figure 2: Analytics Platform Adoption Success Factors (credit: Mike Noke, Health Catalyst Professional Services SVP)

Step 3: Measure Success
How does the analytics team know they’re
doing a great job (and communicate that to
healthcare leadership)?
Once a business is up and running, the next
important step is measuring success.
How does a business know if it’s growing or
if it’s plateaued? How is success quantified?
Just like a business owner, an analytics
director will need to determine what defines
success in a healthcare analytics operation
and how to measure it.

Be warned, if success isn’t defined clearly
and confidently by the analytics director,
someone else will.
Common success measurements are:
Customer satisfaction
Team member satisfaction
Number of customers
Profitability
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Success Measure 1: Customer Satisfaction
Customer satisfaction is the cornerstone of
success for most businesses. Unfortunately,
many analytics directors are not focused on
this metric because they are not thinking like
a business owner.
Too often, analytics teams are buried in work,
and are more concerned with keeping their
heads above water with requests than delivering
timely, actionable data to the individual teams
and customers they are serving.

Success Measure 1: Customer Satisfaction
An August 2018 Gartner article focuses on this very
topic: why and how to make customer success a
focal point of healthcare analytics.
Despite the value customer satisfaction can
provide, many analytics teams fail to approach
or measure it in a consistent manner.
Doing so includes knowing how satisfied
customers are in a measurable way, whether
they are asked to fill out a satisfaction survey
each time a request is filled, or another
system that produces quantifiable results.

Success Measure 2: Team Member Satisfaction
In a technology-enabled services enterprise, the
people are the most important asset, hands-down.
Do you know how engaged, how loyal, how
satisfied your team members are?
Do you proactively measure and monitor
this and are you open to make difficult
adjustments when this data reveals
challenges?

Success Measure 3: Number of Customers
Another possible measurement for how well
a business is doing is measuring the number
of customers.
Is this number going up or down over time?
Keeping track of these simple statistics can
help analytics directors know how they’re
doing or if they need to make changes.

Success Measure 4: Profitability
Unlike most businesses, an analytics enterprise is
not receiving a payment for the services they’re
delivering, so defining profitability may take
some creativity.
The expenses of an analytics enterprise are visible
to the CFO, but not value provided?
When it’s time to cut costs and the only thing
visible is the expense, analytics teams are likely
to see cuts if they can’t effectively communicate
the value they’re providing to the organization.

Results With Improvement (RWIs) can help measure things such as patient
outcomes, increased efficiency, and cost savings in order to provide a look at
the true financial impact of an analytics enterprise.
How many
patient’s lives
were impacted? Did the team
help reduce
readmissions?
Did the team
help cut costs
from supply
chain?
Did the team
get more work
done in less
time?

Analytics directors need to have a
vested interest in defining upfront what
success looks like and then capturing
and recording the results.
Not only is it necessary to measure
success, it’s also necessary to articulate
and communicate the value generated.

For example, the team might share their
successes in a measurable, succinct way:
“This year we saved the organization 15 million
dollars, we saved 10,000 lives and we impacted
100,000 patients.”
Being able to show the true significance of
an analytics enterprise helps ensure the
operations’ viability and growth.
Ask yourself, is your operation a cost to the
business or an investment?

Step 4: Define the Process
What is the most efficient way to provide
analytics services?
Successful businesses must determine the best
way to provide their services to customers.
This includes defining both the intake and
fulfillment process in order to operate
efficiently and effectively.
A healthcare analytics operation also
needs a clearly defined process that is
transparent and easy for customers to use.

Intake
Defining the intake process is an important first
step in ensuring an effective business operation.
This might include answering questions such as:
• How do customers interact with the business?
• Do they shop online for services or show up
at the doorstep?
• Do they know the intake process?

Intake
Answering these simple questions in order to
define the mechanism for analytics access will
help improve the intake process and the
expectations for both the team and customers.
Referring back to Step 1, depending on the
customer, they might need a tour guide, or
they might just need access and training as
a self-service user.

Fulfillment
The second component of this step is
defining the fulfillment process.
Let’s say a business selling widgets had
more demand than they could satisfy at
any given time.
How would the business prioritize those
customers? Is it simply a first-come, first-
serve basis, and if so, is that the best
way to operate?

Fulfillment
In a free market, price and a willingness or
ability to pay play a critical role in
determining who gets access to services
and products, and in what priority order.
If your customers aren’t paying to utilize
your products and services, what is your
mechanism for prioritizing?
In the business of analytics, the value
exchange works a little differently. The
payment you collect is the opportunity
afforded you to create value.

Fulfillment
The higher the opportunity for value
creation, the more appealing the customer.
One customer might come in with a
$200,000 problem and one might have
a $1,000,000 problem.
Realize, however, that these problems don’t
always have a price tag that can be defined
in dollars and cents.
Sometimes patient safety is on the line.

Fulfillment
Other times, regulatory penalties or institutional
reputation is on the line. Or, patient and team
member satisfaction may be the goal.
The value of these efforts may not be easily
quantified.
As such, it will be important to establish decision-
making rights and process through effective
governance (discussed briefly later and in various
other articles produced by Health Catalyst).

Fulfillment
Defining the process for prioritization is
essential for success and goes hand-in-hand
with the ability to measure success.
For instance, if an analytics director can
say he has both a $1,000,000 problem
and a $200,000 problem in the queue, but
not enough staff to tackle them, he can
then take that information to the executive
team in order to ask for more resources.

Step 5: Define the Organization
What’s the most efficient way to organize?
Most businesses spend a lot of time and
energy on finding and keeping the right team.
Before they do that, they need to know how
many people they need on their team and
what the functions of each position are.
For analytics operations, defining the
organization in order to maximize
effectiveness is critical for success.
Deciding how is open to a lot of debate.

One question to start with is how specifically
to define the expertise on an analytics team.
This could include data modeling skills, data
science and predictive analytics, statistical
modeling skills, etc.
An analytics enterprise might organize
teams around each of those areas of
expertise and each team would provide
their expertise separately.
This model is not very efficient.

On the other end of the spectrum, each
analyst would be a generalist on one large
team and over time, each analyst gains
expertise in every aspect of specialization.
This model would be ideal if it were possible
to hire and retain these people.
The reality is likely somewhere in between.

It is recommended to err on the side of generalization
and only specialize if and when there’s enough
demand for that specialization to be self-sustaining.
For example, if there’s a team of five data
analysts and one data scientist, and one
out of every 20 requests involves a data
scientist, then the demand for that data
scientist is going to periodically spike while
the rest of the time he will experience a lull.
During those down times, the business
still has to pay his salary and all the
associated expenses.

Establish Stakeholders and an
Effective Data Governance Plan
As part of defining the organization, it’s
important to establish stakeholders that will
approve funding, growth, and strategy.
Just as many businesses are responsible to
their investors and a board of directors, an
analytics enterprise may also benefit from an
invested group of stakeholders, such as an
Information Management Executive Council.

Establish Stakeholders and an
Effective Data Governance Plan
In another of my articles, Governance in
Healthcare: Leadership for Successful
Improvement, the first principle is to
engage the right stakeholders.
An effective data governance strategy
includes both data stewardship and
establishing the leadership needed to
maximize an investment in analytics.
Because leadership buy-in is so important, it’s important to tap the right
stakeholders to form the healthcare data governance committee who play key
roles in establishing, growing, and sustaining successful analytics programs.

Beware the Entrepreneurial Myth
In 1988, Michael E. Gerber released the best-
selling book, The E Myth: Why Most Businesses
Don’t Work and What to Do About It.
Today, the book is still relevant and popular
because the basic principle is true: just because
someone is good at something doesn’t mean he
can turn that into a successful business.
The reality is that most people are not good
businessmen.

In looking at creating a successful analytics
enterprise, an important consideration is who to
have in the leadership and accountability role for
this business.
That person isn’t necessarily a great technician
or analyst that moved up through the ranks.
The person in this role needs to be a visionary
that is closely connected to the customer.
A common downfall in healthcare organizations
occurs when analytics falls under the
accountability model of the CIO, and many
CIOs are not the visionary that is needed.

That’s not to say the CIO is never a visionary, but
don’t make the mistake of assuming that is the
right person to lead an analytics operation because
it’s the most obvious.
Instead, be thoughtful about who is in that role,
and, ideally that person should be elevated to
be a member of the leadership that reports
directly to the chief strategy officer or CEO.

Ryan Smith, Senior Vice President of Professional
Services at Health Catalyst and former healthcare
CIO, makes a compelling case for executive
sponsorship of a healthcare analytics program as a
key strategy for its success. In the article, Smith says:
This strategy involves identifying the right executive
sponsor, at the right level in the organization, to
provide oversight of an enterprise data management
and analytics program.”
Ryan Smith
SVP of Professional Services

Long-term Success for a Healthcare Analytics
Operation Means Running It Like a Business
Just as many businesses don’t succeed, a
healthcare analytics operation that fails to
understand its customers, provide services in
the most effective way, measure success, or
organize efficiently is also likely to fail.
Running a successful analytics operation
involves many of the same steps for success
as running a successful business.

This includes:
Understanding the unique needs of each
analytics customer.
Creating a roadmap for success.
Measuring the success of the operation
and communicating that clearly and
frequently to hospital leadership.
Making sure the process is transparent
and efficient.
Organized in a way to maximize
effectiveness.
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Taking these steps helps ensure a successful
analytics operation that can improve healthcare
outcomes, save the organization money, and
impact patient lives.

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

More about this topic
Link to original article for a more in-depth discussion.
How to Run Your Healthcare Analytics Operation Like a Business
Addressing Healthcare Waste Through Centralization
David Grauer, MBA, MHSA , Sr. VP Professional Services
Why Clinical Quality Should Drive Healthcare Business Strategy
Health Catalyst Editors
How to Achieve Your Clinical Data Analytics Goals – Elaine St. James, BSN, RN, CPHQ, Patient Safety Services;
Josh Ferguson APRN, ACNP, ANP-BC VP of Clinical Outcomes Improvement
Nancy Casazza, BSN, MMI, RN , Clinical Outcomes Improvement, Director
The Digitization of Healthcare: Why the Right Approach Matters and Five Steps to Get There
Health Catalyst Editors
Healthcare NLP: Four Essentials to Make the Most of Unstructured Data
Mike Dow , Technical Director

Dan has been developing and implementing the core products and services of Health
Catalyst since February of 2011. He started as a data architect, moved into a technical
director role and is now a Vice President of Client and Technical Operations. Prior to joining
Health Catalyst, Dan owned and operated a management consultancy for five years that
assisted ambulatory practices in the implementation of electronic health records and
data-driven management methodologies. In this venture he served as data architect, business-
intelligence developer, and strategic advisor to physicians and practice owners in the strategic
management and growth of their practices. Dan holds Master’s degrees in Business Administration
and Health-Sector Management from Arizona State University and a Bachelor of Arts degree in
Economics from Brigham Young University.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Dan LeSueur

Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Health Catalyst is a mission-driven data warehousing, analytics and outcomes-improvement company
that helps healthcare organizations of all sizes improve clinical, financial, and operational outcomes
needed to improve population health and accountable care. Our proven enterprise data warehouse
(EDW) and analytics platform helps improve quality, add efficiency and lower costs in support of more
than 65 million patients for organizations ranging from the largest US health system to forward-thinking
physician practices.
Health Catalyst was recently named as the leader in the enterprise healthcare BI market in
improvement by KLAS, and has received numerous best-place-to work awards including Modern
Healthcare in 2013, 2014, and 2015, as well as other recognitions such as “Best Place to work for
Millenials, and a “Best Perks for Women.”

How to Run Your Healthcare Analytics Operation Like a Business

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