Most health systems suffer from data clutter and efficient problems. As a result, analysts spend most of their time searching for data, not performing high value work. There are three steps that can help you address your data management issues: 1) find all your dispersed analysts in the organization, 2) assess your analytics risks and challenges, 3) champion the creation of an EDW as the foundation for clinical data management.

1. © 2014 Health Catalyst
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© 2014 Health Catalyst
www.healthcatalyst.com
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Lose the Mess: How to Improve Clinical Data
Management and Reduce Wasted Time
By Jane Felmlee

2. © 2014 Health Catalyst
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Data Complexity Drives
Analytic Complexity
Much like that co-worker’s messy
desk, a cluttered and data
management system is inefficient
and nearly impossible to
manage.
Now extend this type of clutter
and inefficiency across a health
system. With so much scattered
data, how can health systems
practice good clinical data
management?
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The challenge is to create an effective and efficient way to harvest the
precious information stored in the EMR to improve quality and reduce cost.
The EMR investment is huge, and the potential payoff to reap valuable,
actionable insights from the data is significant.

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Challenges for Traditional Clinical Data
Management
The standard approach to clinical
data management, however,
presents a couple of serious
challenges. Consider this:
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1. Without the proper analytic structures and tools, data
analysis is extremely labor intensive. There is high
risk that something will go amiss in the data capture
or linking process and cause the analysis to become
flawed before it has even begun.
2. The risk of developing “spreadsheet mania” – a
proliferation of spreadsheets with data that may or
may not be the most accurate or up-to-date, is a real
consideration. The spreadsheets become “shadow
systems,” often conflicting with corporate data.

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Reduce Wasted Time
Roughly 80 percent of an analyst’s time isn’t spent performing high-
value work, such as interpreting data and making recommendations
about how to improve the outcomes. Instead, most of the analyst’s
time is spent hunting for and gathering the data.
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3 Strategies to Improve Clinical Data
Management
1. Locate the dispersed analysts in your organization
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To align the analysts, a good first step is to
simply identify the current analyst pool
sprinkled throughout your organization.
Finding all of your analysts can sometimes
be a challenge, but one way is by working
with HR to get a list of positions with names
similar to “analyst,” “specialist,” or
“informaticist.”

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3 Strategies to Improve Clinical Data
Management
2. Assess the analytics risks and challenges in your organization
Having identified your analyst pool, you should elect a core analyst
team responsible for assessing the risk within your organization.
Some of team’s new duties are listed in the table below:
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TASK RISKS AND CHALLENGES
Create a report inventory and find the logical
“owners” of each report. Reports not run in
over a year are candidates for “archive.
Folder security can sometimes force
duplication (possible modification) of the same
reports with no audit trail for what is different.
Develop a list of core competencies and a
program to provide on-going training
Without core competencies, there is no way to
guarantee high quality, consistent results.
Assess the degree of silos and political will to
improve alignment.
Analytic silos breed duplication and potential
waste. The political climate often complicates
remedying this risk.
Determine the current method for requesting
reports and analytics.
Without disciplined formal intake, triage,
prioritization and assignment of work
processes, priorities can become unfocused.
Identify current data governance processes
and ownership within your organization.
To ensure each step of the data management
process is controlled and how processes effect
the data are well documented and understood.

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3 Strategies to Improve Clinical Data
Management
After your new analyst team
completes this initial assessment,
you’ll have a better understanding
about the current the level of risk
and potential inefficiencies that
exist in your analytic organization.
This information will position you
to share your findings with
executive leadership and make
the case to change to a new
organization model.
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3 Strategies to Improve Clinical Data
Management
3. Champion the creation of an EDW as a
foundation for clinical data management
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Once you have an EDW, you
need to ensure you are reaping
the full benefits and getting a
great return on your investment.
Aligning the analysts and
developing some important
clinical data governance and
management policies will
strengthen the entire analytics
environment.

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Improving Care and Delivering Better
Outcomes
Health organizations that embrace
the value of data analytics and
harness it strategically by using
effective clinical data management
will be in the best position to
survive and thrive in the new
healthcare model.
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Other Clinical Quality Improvement Resources
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Jane Felmlee joined Health Catalyst in January, 2014 but has
worked with the company since 2010 serving as administrative
sponsor for the 2nd Health Catalyst client, North Memorial
Healthcare, located in the Twin Cities. Jane has worked in healthcare
for over 25 years serving in various roles at Mayo Clinic, Park
Nicollet, and North Memorial Health Care. Her contributions span the
areas of Information Technology, Decision Support, Audit, Accounting/Finance,
and Lean Promotion Office. Jane holds a bachelor degree in accounting from
the University Of Minnesota Carlson School Of Business and is a Certified
Public Accountant (inactive), Certified Information Systems Auditor, and is Lean
Certified.