The promise of actionable analytics in healthcare poses an inherent challenge as we seek to accelerate the time it takes to go from question to insight to action. The velocity of change, the demand for bigger data, the allure of advanced algorithms, the need for deeper insights, and the cost of inaction make knowledge capture and reuse an all too allusive goal. In an evolving environment, healthcare organizations need to find ways to make greater use of prior investments in analytics products by reusing the commonalities of proven designs, metadata, business rules, captured learnings, and collaborative insights and applying them to future analytics products. By doing so in a strategic manner, they will be able to create rapid and efficient analytics processes and better manage time to value and reuse. In this presentation, authors from two very different health systems with two very different patient populations will share their perspectives of the value of knowledge management and discuss the role of analytics in driving towards a learning health system. The authors will highlight opportunities and challenges using examples across clinical, financial, and operational domains.

1. in Healthcare Analytics
Knowledge
Management
Greg Nelson, MMCi, CPHIMS
Vice President, Analytics and Strategy
Vidant Health
Greenville, NC
Monica Horvath, PhD
Formerly:
Senior Reporting Solution Manager
UNC Healthcare
Chapel Hill, NC

2. AGENDA
Introduction
Why knowledge management matters
KM Defined
What good looks like
Challenges & Lessons
Why don’t we all do it
Future Directions
Where are we in our journey
2

3. Introduction
• Why knowledge management matters
• Why this matters
• The cost of inaction
• The Learning Health Organization
• Analytics’ role in the LHS
• The value of KM
3

4. 4
Analytics is a team sport… [and] requires a multidisciplinary
approach to achieving value.. The Analytics Lifecycle Toolkit, 2018

5. 5

6. Business/ Operational View
6

7. Analytics View
7
Reference
data is not
standard
No one
can agree
on the
measures
How do we
make this
actionable?
Who should
see what
level of data?
What is the
O/E
benchmark?
People aren’t
using the
dashboard
Where does
this data
come from?
Who should
the data
steward be?

8. 8
n (n-1) /2

9. Value of Alignment
HORIZONTAL
COORDINATION
COORDINATED
SYSTEMIC
CHANGE
CONSISTENCY
IN MEANING
STANDARDIZED
BUSINESS
RULES
People Collaboration
Content Context
9

10. LHS Applied to D&A
10
Data
Engineer &
Data Quality
Data
Scientist
BA
Prioritize
Problem
Extract
data
Prepare
data
Explore
features
Develop
models
Evaluate
models
Deploy
models
Monitor
DevOps
Integrated Knowledge Management, Collaboration, Source Control, Prioritization,
Stakeholder Engagement/ Transparency, Team Processes, Peer Review, Quality
Processes, Solution Exploration
Problem Definition

11. * Source: The Analytics Lifecycle Toolkit, Wiley 2018
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12. Value of KM
Tangible
• Lower travel costs
• Increased productivity
• Reduced printing costs
• Improved closure time
• Shorter production times
• Reduced rework
• Improved reused
• Faster time to decision (customer
satisfaction)
Intangible
• Consistent use of data
• Increased metrics/ data accuracy
• Improving data sharing and usability
• Standard validation processes
• Data completeness and consistency
• Engaged team members
• Tighter teamwork
• Faster emergency communications
• Top of licensure teamwork
• Talent / career development
12

13. Knowledge Management Defined
• KM Defined
• Definition
• Components of KM
• KM Strategies
• Bimodal analytics
• Different types of KM
13

14. 14
Knowledge management is a business process that
formalizes the management and use of an enterprise's
intellectual assets. Gartner, 2017

15. Components of KM
15
Our approach to support a data-driven culture is to ensure the alignment of people, processes, and technology
that can be leveraged to accelerate our consistent and widespread use of knowledge.
PlatformContent People Process
Knowledge
Reuse
+ + x =
What? Where? Who? How? Why?+ + x =

16. 16
Different
Goals of
Analytics
• Efficient/smooth DataOps
• Reduce risk
• Control costs
• Information Security
• Data privacy
• Repeatability
• Reliability
• Scalability
• Performant
• Reduce errors
• Creative
• Innovative
• Novel
• Multiple perspectives
• Design thinking/ empathy
• Fail fast (errors welcome)
• Transparency
• Agility
Mode1
Mode2
Gartner, 2017

17. Illustrative Difference
17
Mode 1 Mode 2
BI Report
• Report specification
• Source to Target Mapping
• Requirements traceability matrix
• Refresh cycle
• User Acceptance Criteria
Predictive Model
• Purpose and description of the problem the
model tries to solve
• Define the behavior to be predicted, and how
that behavior will be defined and measured
• Define data sources available to be used as
predictors
• Visual and statistical inspection/ observations
• Feature extraction & selection
• Define modeling sample (e.g.
training/validation/testing, holdout, cross-
validation, etc.)
• Describe modeling techniques used to build
model candidates
• Describe model validation techniques used to
select final model
• Modeling results and discussion
• Bias testing
• Model implementation considerations
• Model drift parameters

18. Demand
vs. Supply
Example Supply Demand
Business
definition
Data steward
Associations/
Groups
Data Consumer
Report Writer
Dashboard developer
QA/ Validation
Source code Developer Developer
Business Analyst
QA/ Validation
Industry trend Anyone Anyone
Calculation Metric owner Developer
Aggregate and arrange content
Organize content to satisfy their own preferences
Produced by resources inside and outside the organization
Knowledge creators generate and combine (mashup) content.
As knowledge is consumed, it is refreshed.
Consumed in "chunks’" rather than in its entirety
Consumed at point of need.
Tags, comments and ratings help define relevance and value
Integration of multiple sources and types of information
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19. Challenges and Opportunities
• Operational processes
• Motivation
• Systems and technologies
• Speed/ velocity of change
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20. No Easy Button?
Instead of taking the comprehensive “boil the ocean” enterprise approach to design and
implementation, you can take a “fundamentals” approach that focuses on the critical data-oriented
improvements such as metadata management, data standards, data quality management and data
governance.
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Process – governance, report development, metrics, imperatives, operationalization,
engagement
People – Culture, incentives, clarity, structural changes (rotations, etc.)
Technology – Collaborative, source control, platforms

21. 1 Leadership for Change Programme Master Class 1: Systems Thinking With Myron Rogers," Leadership for Change.
Myron's Maxims 1
1. People own what they help create.
2. Real change happens in real work.
3. Those who do the work do the change.
4. Start anywhere; follow it everywhere.
5. Connect the system to more of itself.
Creators Editors Viewers
Content Creation Content Consumption
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22. Community Models
22
The Three P's of Communities 2
§ Purpose: The shared domain that
identifies the specific area with
value to its members.
§ People: The individuals operating
in the domain who collaborate in
providing a social foundation to
facilitate interaction and share
knowledge.
§ Practice: The application of
knowledge by practitioners to drive
innovation, expertise and
capability.
Practice
PeoplePurpose
1. Jean Lave and Etienne Wenger, 1991
2. Gartner, 2017
A. Activity Purpose + People + Practice =
Community of interest/
Special interest group
B. Domain Purpose + People + Practice =
Competency center/
Center of excellence
C. Learning Purpose + People + Practice =
Professional learning
community/ Technical
Club
D. Outcome Purpose + People + Practice =
Guild/ Community of
Practice
"Communities of practice are groups of people who
share a concern or a passion for something they do and
learn how to do it better as they interact regularly."1
A
B C
D

23. Analytics CoP
23
Community of Practice Canvas
Target
Group
Who are the target
members for this
community?
Which roles or activities
does this community
support?
How will the community
be organized?
How will the community
collaborate?
How will members
benefit from joining this
community?
What personal member
needs are being
addressed?
How will the community
benefit the
organization?
What business needs
are being addressed?
Community
Vision
Why are you creating this community?
What is the overall purpose of the community?
Business
Goals
Member
Goals
• Report Writers
• Data Scientists
• Dashboard Developers
• Data Engineers
• Business Users
• QA Leads
• Reduce rework
• Accelerate innovation
• Improve efficiency
• Standardize processes
• Clarify R&R’s
• Increase collaboration
• Collaborative technology
• Data Catalog
• Business Glossary
• Stewardship/ Curation
1. Gartner, 2017

24. Example Systems and Processes
Why? How? What?
• Business objectives
• Purpose / goals
• Business challenges
• ROI
• Business prioritization
• Alignment to value
• Linkage to strategy
• Approach
• Technology/ platform
• Architecture
• Execution plan
• Best practices
• Report
• Dashboard
• Metric
• Business Rule
• Glossary
• Definition
• Lineage
Implicit Explicit
Knowledge
Technical
deliverables
Business
definitions
Project Charter
Spreadsheets
Project Plans
Spreadsheets
Diagrams
Lessons Learned
24

25. Solution: Make It Easy to Understand
the Metric
• Full metric details
• Similar metrics are
co-located
• Links to DS Projects
25
Links to FY19
dashboard wiki page
Related metric topics
are grouped
FY18 version

26. Data Catalog
26

27. Key Lessons
27
What we tried? What we learned?
Lots of technology! (Yammer,
EverNote, OneNote, SharePoint,
ShareFile, GitHub, Wikis)
• Prototyping is good; don’t try to operationalize too fast
• Technical fluency matters
• Repeated exposure (sell the change)
• Accountability is key (anchoring the change)
Standardized vs. freeform
content
• Not everyone thinks like me (content organization)
• Establish guardrails
• Standardize (R&Rs, Procedures, Measurement)
Content Repositories • Automate anything that can/ should be automated
• Don’t force “unnatural” behaviors
• Social participation (people will go wherever it serves them)
“Collaboration and social within the company is
80% people, process and content and 20% IT.“

28. Lessons Learned
• What can we learn from others
• From our own “failures”
• Incentives / sustainment
• Measurement objectives
• Technology aids
• Value registry
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29. THANK
YOU
Greg Nelson, MMCi, CPHIMS
Vidant Health
greg.nelson@vidanthealth.com
Monica Horvath, PhD
Formerly of UNC Healthcare
monicahorvathphd@gmail.com
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