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in Healthcare Analytics
Knowledge Management
Greg Nelson, MMCi, CPHIMSVice President, Analytics and StrategyVidant HealthGreenville, NC
Monica Horvath, PhDFormerly:Senior Reporting Solution ManagerUNC HealthcareChapel Hill, NC
NCHICA
Annual Conference
2019
AGENDA
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IntroductionWhy knowledge management matters
KM DefinedWhat good looks like
ChallengesWhy don’t we all do it
Lessons LearnedWhat have we learned
Future DirectionsWhere are we in our journey
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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
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Analytics is a team sport… [and] requires a multidisciplinary approach to achieving value.. The Analytics Lifecycle Toolkit, 2018
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n (n-1) /2
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Business/ Operational View
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Analytics View 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?
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Value of Alignment
HORIZONTAL COORDINATIO
N
COORDINATED SYSTEMIC CHANGE
CONSISTENCY IN MEANING STANDARDIZED
BUSINESS RULES
People Collaboration
Content Context
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Closed Loop System of Learning
* Source unknown
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Learning Health System
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LHS Applied to D&A
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
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The Analytics Lifecycle
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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
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Knowledge Management Defined
• KM Defined• Definition• Components of KM
• KM Strategies• Bimodal analytics• Different types of KM
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Knowledge management is a business process that formalizes the management and use of an enterprise's intellectual assets. Gartner, 2017
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Components of KM
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 =
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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
Mod
e 1
Mode 2
Gartner, 2017
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Requires Different Approaches*
Top-downBest-practice drivenLeaders in chargeDrives alignment and governance
External sources of “excellence”
Bottom UpCollective, continuous learningParticipant-driveEvolving structures and themesProblem/ Solution Focus
Center of ExcellenceCo
mm
unity
of P
ract
ice
* Adapted from Gartner, 2017
Mode 1
Mode 2
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Experience: Uses and value of data
Source: The Jurney–Warden data-value pyramid of (Agile Data Science 2.0)
Drive,value,
effect, alter, change, deliverCurate,
recommend, understand, infer,
learnStructure, link, metadata,
tag, explore, interact, share
Clean, aggregate, visualize, question
Collect, display, plumb individual records
Actions
Predictions
Reports
Charts
Records
… where we extract enough structure from our data to display its properties in aggregate and start to familiarize ourselves with those properties.
The data-value stack begins with the simple display of records
Next comes identifying relationships and exploring data through interactive reports.
This enables statistical inference to generate predictions.
Finally, we use these predictions to drive user behavior in order to create and capture value.
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Illustrative Difference
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
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Example Supply Demand
Business definition Data stewardAssociations/ Groups
Data ConsumerReport WriterDashboard developerQA/ Validation
Source code Developer DeveloperBusiness AnalystQA/ Validation
Industry trend Anyone Anyone
Calculation Metric owner Developer
Aggregate and arrange contentOrganize content to satisfy their own preferencesProduced by resources inside and outside the organizationKnowledge creators generate and combine (mashup) content.As knowledge is consumed, it is refreshed.
Consumed in "chunks’" rather than in its entiretyConsumed at point of need.Tags, comments and ratings help define relevance and valueIntegration of multiple sources and types of information
Supply vs. Demand
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Challenges and Opportunities
• Operational processes• Motivation• Systems and technologies• Speed/ velocity of change
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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
NCHICA 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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Lessons Learned
• What can we learn from others• From our own “failures”• What it takes?
• Onboarding/ checklist/ peer reviews/ • Structural integrity to ensure this is sustainable
• Best practice repository
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Community Models
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, 19912. 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 CD
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Analytics CoP
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
VisionWhy 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
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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 ExplicitKnowledge
Technical deliverables
Business definitionsProject CharterSpreadsheets
Project PlansSpreadsheetsDiagramsLessons Learned
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Our Content and Collaboration Strategy
GitHub• Project-based
technical artifacts
• Design• Requirements• Code
SharePoint• Product Catalog• Stakeholder
communications
• Learning Home• Value Registry
Data Catalog• Definitions• Glossary• Data sources• Technical
Metadata• Data
Classification
Other• EverNote Library• ShareFile - Plans,
Documents• LucidChart -
Diagrams• Miro –
Brainstorming• Aha! – Product
Portfolio Mngmt• ServiceNow –
Incidents, Tasks,
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Solution: Make It Easy to Understand the Metric
• Links to EADSpedia page
• Full metric details
• Other similar metrics are co-located on the page
• Where relevant, links to advanced analytics projects are
included
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Solution: Document Everything Well and By Topic
Links to FY19 dashboard wiki page
Related metric topics are grouped
FY18 version
Metric details & governance
links
Table of Contents for Metrics, analytics
work, helpful links
Quality context
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Data Catalog
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Data Science Platform
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Key Lessons
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.“
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Future Direction
• Our respective journeys• Where we are and where we want to be• Measurement objectives• Technology aids• Value registry• Incentives
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THANKYOU
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Greg Nelson, MMCi, CPHIMSVidant [email protected]
Monica Horvath, PhDFormerly of UNC [email protected]
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