The session led by John Wadsworth focused on enhancing data analysis in healthcare, stressing the importance of unlocking data and utilizing effective analytic tools. It highlighted the significant time analysts spend on gathering data rather than analyzing it, and provided exercises to illustrate teamwork in data handling. The discussion also emphasized the need for leadership to prioritize projects and foster a culture of honesty in data interpretation.

1. Session #9
Getting The Most Out Of Your Data Analyst
John Wadsworth
Vice President, Technical Operations, Health Catalyst

2. Today’s Agenda
 Unlock the data
 Analytic tools
 Prove or analyze?
 Analytic whiplash
 Accepting the truth

3. Poll Questions 1 - 2
Question #1 -
• How much time would you estimate your analysts
spend gathering data (vs analyzing data)?
Question #2 –
• How much time would you estimate your analysts
spend analyzing data?
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4. Unlock Your Data
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5. “I told you I wasn’t a hunter gather. I’m an analyst!”
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6. Analysts - Hunting and Gathering
Conversation this week:
• Shared savings partnerships withholding money
because of poor analytics
• Analysts spending 80% or more gathering data
• Data exists in multiple sources (EMR, costing,
billing, patient satisfaction, etc.) that are not
integrated
How hard can it be to gather the data for analytics?
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7. Unlocking Your Data - Exercise
Objective #1: Determine the total value of the money
in your team bucket.
Rules
• Work as a team. Everyone at your table needs to participate.
• Collect your bucket at corresponding colored locations around
the room. Send 1 person (runner) from each table.
• Do not retrieve the bucket until you are given the “Go” signal.
All teams start at the same time.
• With each task that you complete, ring the bell and you will be
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given an additional task.
• Complete as many tasks as possible as a team.
• Time limited, so work quickly!

8. Catalyst Adaptive Data Warehouse
Adaptive Data Warehouse Model
Metadata: EDW Atlas Security and Auditing
Common, Linkable
Vocabulary
Financial
Source Marts
Administrative
Source Marts
Departmental
Source Marts
Patient
Source Marts
EMR
Source Marts
HR
Source Mart
Pneumonia
Diabetes
…MANY more!
More Transformation Less Transformation
FINANCIAL SOURCES
(e.g. EPSi, Peoplesoft,
Lawson)
ADMINISTRATIVE
SOURCES
(e.g. API Time Tracking)
EMR SOURCE
(e.g. Epic, Cerner)
DEPARTMENTAL
SOURCES
(e.g. Apollo)
PATIENT SATISFACTION
SOURCES
(e.g. NRC Picker, Press
Ganey)
Human Resources
(e.g. PeopleSoft)
Surgery

9. Poll Questions 3 - 4
•Question #3
•In your personal opinion, how important is the analyst
role in your organization?
•Question #4
•How important is the role of analyst viewed by your
organization?
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10. Analytic Tools
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11. Analytic Tools - Exercise
Objective #1: Group your coins by denomination
AND stack them at least 5 coins high.
Rules
• Work as a team. Everyone at your table needs to participate.
• Do not open the bucket until you are given the “Go” signal. All
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teams start at the same time.
• With each task that you complete, ring the bell and you will be
given an additional task.
• Complete as many tasks as possible as a team.
• Your entire team MUST use the (hand) tools provided you
for the complete exercise.

12. From Hunter-Gather to Analyst
Tools Support Transformation
• Structured Query Language (SQL or variant)
• Data analysis
• Visual representation of information
• Communicate meaningful story through the data
• Domain knowledge
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13. Recommended Tools
for Data-Driven Health System
• Source systems that support query (SQL)
• Let them get to the data
• Business intelligence development tools to build
meaningful visualizations
• Cognos, Crystal Reports, Tableau, Qlikview, Excel
• An enterprise data warehouse (EDW)
• Start small and grow as needed
• Assumes data architects will extract, transform,
load (ETL) and model data into warehouse
• Scalable platform to grow analytics

14. Poll Questions 5 - 6
Question #5
•How often do you act on information provided to you by
your analysts?
•Question #6
•Analysts – How often does management act on your
analysis and/or recommendations?
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15. Prove This
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16. Analyze the Decision to Build
• “We need to build an observation patient wing”
‒ 3 year upward trend in observation patient volume through ED
‒ Reimbursements dropping for obs patients  get to inpatient or
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ED acuity
‒ Historically we had an observation wing
• Questions they wanted answered
‒ How many beds do we need?
» Clinical data informed bed count estimates
‒ What clinical staffing will be needed for the new wing?
» HR and clinical data justified staffing model
‒ What will it cost to build the new wing?
» Costing data supported estimate of $.5M - $1M/bed for re-purposing
existing beds  $2.5M - $5M for 5 bed wing
• WAIT! Has the decision to build already been made?
‒ If so, do you need an Analyst … or something else?

17. Analyze the Data to Inform a Decision
We asked, “What can the data tell us about the
observation patients?”
 ~70% had chief complaint of chest pain
 ~90% existing patients in the hospital system
 ~80% with chest pain had former diagnosis of heart
failure from cardiology clinic/primary care
 ~75% arrived in ED between 5-10 PM
 Cardiology clinic closed at 5:00 PM
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Analyst recommendation
 Keep the cardiology clinic open until 10:00 PM
 Don’t spend the $2.5M - $5M for an observation wing

18. Analytic Whiplash
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19. “I could catch a trout on a dusty road.”
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20. Whiplash Cycle
• Leadership discovers a problem
• Analyst assigned to provide insight
• Analyst & others study problem to define scope
• Data gathered then analyzed
• Patterns and correlations begin to emerge
• Leadership brings another problem for analysis or
changes direction. Analyst told to “wrap it up and
move to the next problem”.
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21. Considerations for
Improved Analytic Insight
• Analyst & others study problem to define scope
• Data gathered then analyzed
• Patterns and correlations begin to emerge
• Assumptions verified/refuted by knowledge experts
closest to the work process being measured
• Adjust logic based on feedback.
• Iterate through process until all logic validated by
process owners (in the trenches)
Give sufficient time for analysis, discovery
and a recommendation.
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22. Risks of Under-resourced Analytics
• Insufficient time leads to half-baked analysis
• Incomplete analysis undermines credibility
• Lack of credibility creates further dissatisfaction with
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data and analytics

23. Do You Need More Analysts?
• Perhaps… but before you hire more analysts,
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consider asking:
‒ Will more analysts get the needed time to do
analysis?
‒ No? Increased capacity for incomplete analysis
• Analyst needs the time to work smarter, not harder.

24. Leadership and Prioritization
• Remove prioritization burden from analysts
• Leadership become proficient with prioritization
• Leadership determine projects of highest priority
‒ Unified front – individual agendas undermine execution
‒ Decide what projects will and will not be funded
‒ Resist the lure of shiny, new objects
‒ Commit resources for top projects to completion
‒ Communicate results of prioritization to the masses
Minimize the whiplash of “urgent” projects
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25. Accepting the Truth
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26. Poll Questions 7- 8
•Question #7
•On a scale of 1 to 5, how well do you trust information
provided through your analysts?
•Question #8
On a scale of 1 to 5, how well does your culture support
analysts delivering information that may be perceived as
negative or undesirable?
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27. Should I report the whole truth?
• Honesty is the best policy for analytic credibility
• CLABSI reported or actuals?
• Confront the brutal facts
‒ “When you turn over rocks and look at all the squiggly
things underneath, you can either put the rock down, or
you can say, ‘My job is to turn over rocks and look at the
squiggly things,’ even if what you see can scare the [heck]
out of you.” – Jim Collins
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28. Summary
• Unlock the data for your analysts
• Get the right tools for your analysts and
organization
• Leadership become proficient in
prioritization
• Develop a culture of accepting the truth
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29. Analytic
Insights
Questions &
A
Answers

30. Session Feedback Survey
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1. On a scale of 1-5, how satisfied were you overall with this session?
1) Not at all satisfied
2) Somewhat satisfied
3) Moderately satisfied
4) Very satisfied
5) Extremely satisfied
2. What feedback or suggestions do you have?
3. On a scale of 1-5, what level of interest would you have for
additional, continued learning on this topic (articles, webinars,
collaboration, training)?
1) No interest
2) Some interest
3) Moderate interest
4) Very interested
5) Extremely interested

31. Upcoming Keynote Sessions
3:45 PM – 4:40 PM
13. Healthcare Reform 2.0: Anticipating What’s Next
Governor Mike Leavitt
Founder and Chairman of Leavitt Partners
Former Secretary of the Department of HHS
5:15PM – 6:00 PM
Reception
6:00PM – 7:00 PM
Dinner
7:00PM – 7:50 PM
14. The Acceleration of Technology In The 21st Century:
Impacts on Healthcare and
Ray Kurzweil
Chairman, Kurzweil Technologies
Director of Engineering, Google
7:50PM – 8:30 PM
Entertainment
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Location
Main Ballroom