3 Phases of Healthcare Data Governance in Analytics

3 Phases of Healthcare Data Governance
in Analytics
By Mike Doyle
© 2014 Health Catalyst
www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
www.healthcatalyst.com
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.

Show the challenges involved with adaptive healthcare data governance
Show the challenges involved with adaptive healthcare data governance
Healthcare Data Governance
Healthcare data governance. It
sounds like it could be a dry topic,
right?
The truth is that data governance
is a very exciting and complicated
challenge in healthcare analytics.
The purpose of this commentary is to:
Demonstrate how effective data governance evolves throughout the
lifecycle of a health system’s analytics program

The specification of decision rights and an accountability
framework to ensure appropriate behavior in the valuation,
creation, storage, use, archiving, and deletion of information. It
includes the processes, roles and policies, standards, and
metrics that ensure the effective and efficient use of information
in enabling an organization to achieve its goals.”
What Data Governance Is
Data governance is:
The scope of data governance includes
data stewardship, storage, and
technical roles and responsibilities.
It also requires leadership and
processes to get the most out of an
investment in analytics.

Because leadership buy-in is
so important, the focus here
will be on the important role a
core group of leaders — the
key stakeholders who form
the healthcare data
governance committee — will
play in setting, growing, and
sustaining a successful
analytics program.

Stakeholders must understand
how important it is to ensure
the right group of leaders is
engaged in the governance of
the BI program as a whole.
The best data governance
committees drive efficient,
equitable use of a health
system’s information, and
enable the organization to
achieve its goals of higher-quality,
lower-cost care.

Few health systems claim to have
figured out effective healthcare data
governance.
One difficulty is that no single
template for data governance can be
applied to every organization.
Data governance approached as a
rigid, idealized plan often ends up
being scrapped.
Effective Healthcare Data
Governance Is Adaptive

In General there are three phases
of adaptive data governance that
characterize successful analytics
implementations.
1. The Early Stage of Healthcare Data
2. The Mid-Term Stage of Healthcare
Effective Healthcare Data
Governance Is Adaptive
Governance
Data Governance
3. The Steady State of Healthcare
Data Governance

1
Committee Makeup
In the early stages of planning sponsors of the health system’s data
governance initiative must have some decision-making authority.
The Early Stage of Healthcare
Data Governance
THREE PHASES
DATA
GOVERNANCE
Ideally, these leaders have a
passion for using data, and are
generally described as being
both exciting and decisive.
They may be executives,
directors, influential managers
in the quality department,
nurses, or physicians.

1
Committee Focus
Some of the most important questions this group will address are:
The Early Stage of Healthcare
Data Governance
THREE PHASES
DATA
GOVERNANCE
Where should the initiative begin?
Where should resources focus?
The committee’s first role will be to
keep the peace, and ensure everyone
impacted understands the priority.
They must protect the integrity of the
initiative to drive real quality and cost
improvement.

The Mid-Term Stage of
2
Committee Makeup
As your implementation of analytics continues, the nature of the
decisions made by the data governance committee will change.
THREE PHASES
DATA
GOVERNANCE
It’s possible some of the initial
committee members will lose interest
at this stage, and miss meetings.
They may view program maintenance
as more of an operational problem. It’s
okay to let them walk away.
Their replacements may bring new
energy to the initiative.

2
Committee Focus
At this phase the committee will begin to implement the solution in
new areas of the enterprise. You may take the lessons learned in
Cardiology to branch out and serve Labor and Delivery.
The Mid-Term Stage of
THREE PHASES
DATA
GOVERNANCE
The group will also be responsible for
monitoring the progress of existing
initiatives. They will ask questions like:
• How are things going?
• Who is using the system?
• What additional training or tools are
needed to increase utilization?
• What should we keep doing/stop
doing/do more of?

3
Council Makeup
In this stage, the data governance committee may want to start
calling itself something more interesting, like a governance council.
Let it happen. Only a few of the initial leaders will still be involved,
but the council itself will continue to evolve.
The Steady State of Healthcare
Data Governance
THREE PHASES
DATA
GOVERNANCE

Data Governance
3
Council Makeup
The most effective agents of governance
at this point are those who are the
“ethical politicians” and have a talent for
peacekeeping and consensus building.
They will have a natural, one-on-one
leadership style that fosters team
communication, in or out of the meeting.
They won’t be solving big problems in
real time; rather, they’ll be addressing
frequently heard common concerns.
THREE PHASES
DATA
GOVERNANCE

Data Governance
3
Council Focus
If the analytics effort is going well, there
will be dissatisfied customers. Some will
believe they haven’t received as much
support as other areas.
Leaders’ role will be to hear these
customers’ concerns, make their voices
heard, help them feel there’s hope for
improvement, and then keep them in the
loop until their concerns are addressed.
THREE PHASES
DATA
GOVERNANCE

Data Governance
3
Council Focus
At this point the group must stay the
course in spite of vocal resistance from
other departments.
If the initiative is working for most of the
organization, the council will have to
actively engage the disenfranchised and
bring them around.
Finally the team will continue to be
responsible for monitoring the success
of the initiative and prioritizing efforts.
THREE PHASES
DATA
GOVERNANCE

These metrics should stay solid, or trend
upwards in rare cases:
• Technology/analytics team satisfaction
These metrics should hopefully trend down:
• Time and resources it takes to answer
common analytic questions
How to Know If Healthcare Data
Governance is Working
To know if data governance is working, explore using data to
measure the effectiveness of your data governances if you can.
Here are some of the key metrics to study:
If governance is successful,
these metrics will trend up:
# of users
# of requests
# of queries, reports and page views
# of success stories from your users!
# of key stakeholders who are aware
of your group and what you do
• User/customer satisfaction.
• # of requests you get to evaluate
competing analytic systems

At various points in the lifecycle
of the project, you may be
tempted by the simplicity of a
“one size fits all” framework for
data governance.
Healthcare data governance is
most effective when it is allowed
flexibility to adapt and change.
If you are prepared to allow your
data governance strategy to
evolve as analytic maturity
develops, your organization will
be well positioned for success.
How to Know If Healthcare Data
Governance is Working

More about this topic
7 Essential Practices for Data Governance in Healthcare
Dale Sanders, Senior VP of Strategy
Why a Data Governance Is One of the Six Reasons for Data Warehouse Failures
Steve Barlow, Co-Founder and Senior VP of Client Ops
The Best Organizational Structure for Healthcare Analytics
John Wadsworth, VP of Client Technical Operations
Keys to a Successful Analytics Implementation
Jared Crapo, VP; Eric Just, VP of Technology; and Dan Lidgard, VP
Demystifying Healthcare Data Governance
Dale Sanders, Senior VP of Strategy

For more information:

Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Mike Doyle joined Health Catalyst in May of 2013 as a Vice President. He has
been connected with the Health Catalyst senior leadership team since 2006.
Prior to Health Catalyst, Mike led the Enterprise Data Warehouse (EDW)
program at Allina Health as Director of Healthcare Intelligence. He helped Allina grow
its EDW program from a nascent clinical improvement initiative to an enterprise-wide
strategic asset, in heavy demand by thousands of users across all of Allina’s 11
hospitals and 100+ clinics. Prior to his work with Allina, Mike was employed on the
Northwestern Medicine campus in Chicago, beginning as a Systems Administrator at
the Medical School and eventually leading the Analytics and Systems Integration team
at Northwestern Medical Faculty Foundation. In addition to his experience building
strong technology teams, Mike has experience in technical roles such as database
administrator, web programmer, data architect, and business intelligence developer.
Mike holds a Master of Music degree from Northwestern University and a Bachelor of
Fine Arts degree from Carnegie Mellon University.

3 Phases of Healthcare Data Governance in Analytics

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