Most organizations struggle to turn their data into a strategic asset. Oftentimes they lack the data they need, and don’t trust the data they have. This results in a struggle to surface meaningful opportunities, quantify the value of those opportunities, and transform insight into action. In this webinar, your host Tom Burton shares strategies for improving data literacy, ensuring data quality, and expanding data utilization. This interactive, “choose your own adventure” style experience, allowed attendees to discover how investing in a deliberate, principle-based strategy can help them navigate the complexities of data governance and maximize the value of data for outcomes improvement. View the webinar and learn: - Demonstrate how to unleash data at your organization with efforts across the improvement spectrum. - Recognize how to sustain and spread improvements across your entire organization. - Illustrate the importance of investing in analytics training and infrastructure to prepare for massive improvement in healthcare outcomes. - Understand the 5 key stages of the Data Life Cycle. - Demonstrate strategies to overcome the common challenges around data quality, data utilization, and data literacy. - Show how a data governance framework can accelerate improvement in clinical, cost, and experience outcomes.

1. The Data Maze Game:
Navigating the Complexities
of Data Governance
Tom Burton
President, Professional Services
Health Catalyst

2. © 2018
Health
Catalyst
• Demonstrate how to unleash data at your organization with efforts across the
improvement spectrum.
• Recognize how to sustain and spread improvements across your entire
organization.
• Illustrate the importance of investing in analytics training and infrastructure to
prepare for massive improvement in healthcare outcomes.
• Understand the 5 key stages of the Data Life Cycle.
• Demonstrate strategies to overcome the common challenges around data
quality, data utilization, and data literacy.
• Show how a data governance framework can accelerate improvement in
clinical, cost, and experience outcomes.
Learning Objectives
2

3. © 2018
Health
Catalyst
We believe when you elevate data as a
strategic asset
it enables significantly
better decision making
and promotes
massive improvement
across the spectrums of both
effort and value.
The Why:
3

4. © 2018
Health
Catalyst
The Data Life Cycle
4

5. • Lack of data skills, knowledge
and attitudes
• Wrong mix of resources (e.g.
too many report writers not
enough analytic engineers)
• Lack of interoperability
• Lack of contextual training
causes incorrect interpretation
/ conclusions
• Fear of loss of privacy
prevents appropriate
utilization for improvement
• Culture of data fiefdoms –
that’s “our” data
• Data capture is incomplete,
delayed, or inaccurate
• Consolidating to a single
EMR can take too much time
and money
• Integrating data into fixed
models from different sources
is error prone
Data Driven Culture
• Data is not driving better
decision making
• Data infrastructure is seen
as an expense not an asset
The Data Life Cycle
5

6. © 2018
Health
Catalyst
Data governance refers to the people,
processes, and technology that are proactively
applied to ensure that an organization’s data is
managed in such a way to maximize the value
of that data to the organization.
Definition of Data Governance
6

7. Elevate the status of data
as a strategic asset of
your organization
What would make your
data a distinguishing asset
of your clinical and
business objectives?
Build your data
governance org
structure
Who are the best
individuals and how
should you organize to
realize the vision?
Identify, prioritize and
execute on data
governance improvements
in the data lifecycle
How do you ensure all are
equipped with data for better
decision making – from the
bedside to the boardroom?
How do you ensure
your data investments
are built to last?
Sustain and extend
the initial gains
Elevate Establish Execute Extend
Data Governance Framework: The 4 E’s
7

8.  Overview – 10 min
• Core Data Governance
Principles – 25 min
• Advanced Data Governance
Principles – 25 min
• Conclusion – 5 min
Agenda

9. Core Data Governance
Principles

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Health
Catalyst
Core Principles – Elevate, Establish, and Execute
Elevate Data as a Strategic Asset
Get engagement around a “burning platform”
priority and elevate data as strategic asset in that
challenge — e.g. financial pressure/budget cuts
Action: Choose Priorities
Governance chooses priorities around burning
platform area based on data life cycle deficiencies
Capture: Improve Data Quality
Improve data quality at the source
Capture: Utilize Data Beyond the EMR
Identify meaningful data to capture beyond the
EMR to improve decision making.
Integrate: Build Data Integration Plan
Balance reusability, scalability, flexibility and time
to value
Grant Appropriate Access
Trust AND verify — grant broad access AND audit
Deliver Insights: 5 Rights of Data Delivery
Promote better decision making with the 5 Rights
of Data Delivery
Deliver Insights with KPA and Self Service
Identify Opportunities – Key process analysis & self
service — e.g. where could we reduce costs and
improve care
Establish Around Frontline Processes
Organize and establish everything around frontline
work processes - evaluate challenges in the data
life cycle
10
Go to Advanced Principles
Poll Question #2Poll Question #1

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Health
Catalyst
Which of these 4 principles would you most like to see discussed today?
1. Elevate Data as a Strategic Asset — 23%
2. Deliver Insights with KPA and Self Service 28%
3. Establish Around Frontline Processes — 31%
4. Action: Choose Priorities — 17%
Poll Question #1
11
Go to Core Principles

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Health
Catalyst
Which of these 5 principles would you most like to see discussed today?
1. Capture: Improve Data Quality — 25%
2. Capture: Utilize Data Beyond the EMR — 21%
3. Integrate: Build Data Integration Plan — 30%
4. Grant Appropriate Access — 6%
5. Deliver Insights: 5 Rights of Data Delivery — 18%
Poll Question #2
12
Go to Core Principles

13. Deliver Insight
Principle:
Identify opportunities and insights across
the spectrums of value and effort
(typically performed by an analytics
engineer or outcomes analyst)
Data-driven opportunity:
Identify variation with
key process analysis for
deep improvements
Pain-point opportunity:
Identify data hunger
pain points where self-
service may be helpful
13

14. Organic Improvement
Let innovation happen - Light Effort
Fast track Improvement
Medium Effort
Comprehensive outcomes
High Effort
Value Across the spectrum of improvement effort, the value may be light, medium, or high value.
Enablers Highly trained and engaged team members and a robust analytics infrastructure (both platform & applications)
Volume
1,000s of day-to-day better data driven
decisions
100s of quick win improvements using data
10s of deep changes, eliminating unwarranted
clinical, operational and/or financial variation
Examples
• 2 hour ad-hoc analysis by senior analyst
reveals insight that expanding clinic hours,
versus building an observation wing, will save
$3Min capital expense.
• Automated dashboard saves 4 hours of
manual data collection/reporting per week.
• Data helps clinicians identify high maternal
hypertension rates; insights + interventions
results in 15% improvement in hypertension
rates.
• Dashboard helps identify missing
documentation on high dollar accounts,
improving AR days by 10%.
• Deep process redesign, leveraging
predictive models, reduces sepsis mortality
rate by 15%, saves 125 lives per year, and
reduces costs by $1.6 M.
• Redesigning care management workflow
using mobile technology increases care plan
effectiveness by 28% and saves $3.4 M.
Sample Results
Measures
Technology utilization, number of lives impacted/saved, intervention rates, number/percent improvement, additional revenue, cost savings, cost
avoidance…
Sample
Communications
Vignettes, improvement snapshots, case study briefs, case studies, webinars, publications…
Unleashing Data to Achieve Massive Improvements
1414

15. Financial Value
Clinical Value
Experience Value
X
Effort
High
Light High
Value Improvement Type
Self-service
dashboards
Key process analysis
– variation analysis
The Improvement Spectrum Matrix – Value
and Effort
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Health
Catalyst
.
Common Pain Points to Assess
• Hunger for data: Data access takes days or weeks,
or is never granted — no self-service strategy
• Wasted time reconciling report discrepancies
• Long waits for data requests in report queues
• Duplicate efforts around data integration and data distribution
• Spread-marts abound (Sneaker ETL)
• Various analytic tools across departments
• Lack of basic data literacy
Self-service
dashboards
16

17. • Access to content enabled
through a security model
endorsed by senior
leadership
• Provisioning process well
defined and
operationalized
Broadly Accessible
Data
• Analytic tool capabilities
support what end users
are trying to do
• Analytic community has
the ability to share and
distribute content
Analytic Toolset
Alignment
• Teams are provided
education on the core
capabilities to support
their use of the data
• Support function available
to answer and direct
questions
Training & Support
• Continuants understand
what is available, what is
changing, and what is
coming
• Value being delivered by
the platform is
consistently and broadly
being messaged
Communication
• Individual or group is on point to grow analytics capabilities
• Ensure evolving roadmap aligns with business/clinical priorities
Analytics Leadership
The Prerequisites of Organic
Improvement
Self-service
dashboards
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Health
Catalyst
Organic Improvements –
Be Opportunistic
Texas Children’s Example
Context
• Working on Asthma Action Plan initiative
Discovery
• While exploring the data MD Leader and
Analytics Engineer find anomalies in data
around chest x-rays unrelated to the asthma
action plan initiative
• Appears high percentage of chest x-rays
from ED are unwarranted
• Analytics Engineer, performs deeper
analysis within a few hours and discovers
highly utilized order set used by Resident
MDs in ED
Intervention
• New default orders set using best practice
intervention criteria for x-ray designed to replaces
old order set
• Resident MDs in ED instructed to use new order
set for children presenting with Asthma.
Result
• Achieved and sustained a 49 % decrease in
unnecessary chest X-ray orders
– Better Care for Patients
– Elimination of Unnecessary Cost
– No extra x-ray exposure to kids
• Value = High
• Effort = Light
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Self-service
dashboards

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Health
Catalyst
Dr. J.
15 Cases
$60,000 Avg. Cost Per Case
Mean Cost per Case = $20,000
$40,000 x 15 cases =
$600,000 opportunity Total Opportunity = $600,000
Total Opportunity = $1,475,000
$35,000 x 25 cases =
$875,000 opportunity
Total Opportunity = $2,360,000
Total Opportunity = $3,960,000
Cost Per Case, Vascular Procedures
Variation Analysis Example
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Health
Catalyst
• Adjust for severity of illness (apples to apples comparisons)
• Recognize the two primary causes of variation
• Clinical variation = differences in the way care is delivered
• Data system variation (e.g., differences in the way data are captured, care is
documented and reported)
Variation Principles
Objective: Focus improvement initiatives on areas with the greatest
clinical variation, but commit to reducing both causes of variation.
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Health
Catalyst
Excellent OutcomesPoor Outcomes
# of
Cases
Excellent OutcomesPoor Outcomes
# of
Cases
Excellent Outcomes
# of
Cases
Poor Outcomes
Excellent Outcomes
# of
Cases
Poor Outcomes
1
2
3
4
Variability
High
Low
SizeLow High
Improvement Approach - Prioritization
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Health
Catalyst
Compare Variation with Size
1
2
3
4
22
Go to Core Principles

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Health
Catalyst
• Deming’s quote applies especially to data governance
• Many organizations mistakenly organize Data Governance around IT systems (e.g. EMR,
HR system, budgeting or accounting tools)
• Data stewards are the owners of clinical and operational processes which produce data (or lack
thereof, or bad data) as a result of the current process.
• Have you identified your key processes?
• Which cost the most? How much less could they cost?
• Which processes currently produce the right data at the right time to effectively manage them?
• How well is the clinical, financial, and experience data integrated about a process?
Framework Principle: Establish
“Organize everything around front-line value-added work processes”
- Deming
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Health
Catalyst
Clinical Program subgroups (vertical)
• Data expertise for the processes a patient would go
through for a particular condition or disease across
the continuum of care such as a heart failure patient
or a pregnancy patient
Support Service subgroups (horizontal)
• Data expertise for the 1) care unit support services
(e.g. ICU, Cath Lab), 2) ancillary support services
(e.g. Lab, Imaging, Pharmacy) and 3) non-clinical
services (e.g. rev cycle, laundry). These processes
have their own unique data life cycles needs.
Centralized data functions subgroups (global)
• Some functions should be centralized and likely need
improvement such as analytic tool standardization or
streamlined data access
Consider Establishing 3 Types of Data
Governance Subgroups
Women&
Children
Cardiovascular
Laboratory Services
Acute Medical Services
. . .
...
Data Access Policy Analytic Tool Standardization
24

25. Run a Large Process with Significant Variation
through some Data Life Cycle Questions
Do we have all the data we need
to ideally manage this process?
Is some data missing or
inaccurate?
Have we integrated clinical,
financial and experience data
together?
Do those making decisions have
access to ALL the data that could
promote the best decisions?
What insights could be
presented at the right time in
the workflow to encourage
better decision making?
Do we measure how well we act?
What % of the time are achievable
benefits not achieved?
25
Return to Core Principles

26. Capture
Principle:
Improve data quality (timely, accurate,
complete) at the source
Missing or
incomplete
data
Inaccurate
or untimely
data
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Health
Catalyst
• Fix it at the source (data capture – not downstream manipulation)
• Encourage transparency (don’t hide data flaws – flag suspect data)
• Focus on process not people (data for understanding, not
judgement)
Strategies to Improve Data Quality
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Catalyst
Perform a Data Quality Assessment
for a Key Process
Completeness
Find what data is missing to effectively manage the
clinical or business process.
Decision-making latency
Measure how long it takes to get actionable data to
front-line decision makers. (What latency is required for
the process 3 minutes or 3 months?)
Accuracy
Measure data capture failures, including wasting
clinician and patient time with redundant or poorly
designed data capture interfaces. Could you accurately
recreate what happened in the process from the data?
Transactional system value
Should be evaluated based on how they help manage
the underlying process and how natural it feels to end
users. (Does it save them time or cost the time – do
they like the system?)
Compare the ROI
Evaluate consolidating to a single EMR vs. the ROI of
consolidating and integrating the data and processes.
(Where will the greater value come from?)
Data fidelity
Evaluate how much data fidelity is lost when forced to
comply to “standard” vendor based data submission
sheet (must have atomic detail).
Evaluate interoperability
How easily can data be integrated back and forth
between transactional systems?
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Health
Catalyst
Data Stewards:
• Have fundamental knowledge of front line work processes
• Are technically savvy
• They work closely with technical experts to improve the quality of data captured in
transactional systems – they never advocate for downstream “cleansing” they fix the
problem at the source (data capture)
• They work closely with analysts to interpret data and present insights clearly
• They understand where in the workflow analytic insights will add the most value to decision
making
• Grant and remove access appropriately (business/clinical decision –
NOT a technical decision)
The Critical Role of Data Stewards
in Improving Data Quality
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Health
Catalyst
The Major Healthcare Delivery Processes
that Produce Data
Each Clinical Program
and Support Service
should eventually have a
Data Steward
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Health
Catalyst
• Don’t try to fix it all at once – fix it when you prioritize an
improvement initiative in that operational or clinical area.
• Don’t wait to start improvement until the data is perfect – fix
operational/clinical process variation AND data system variation at
the same time.
Don’t move the data steward SME pawn all over the
board fixing all data problems before you go after actually
capturing improvements.
• Directionally correct vs. perfect – aim defines the system.
When Is the Right Time to Improve
Data Quality?
31
Return to Core Principles

32. Integrate
Principle:
Balance reusability, scalability, flexibility,
and time-to-value when you build your
strategic data integration plan
32

33. Integrate Data Gartner: Health Data
Convergence Hub
“Definition: The health data convergence hub is the orchestration platform
that brings together data from across the consumer/citizen/patient health
and wellness continuum and prepares the data for delivery to downstream
consumption platforms, applications, analytics and "things." It automates
the ingestion of data — both structured and unstructured — from all
identified and permissioned sources; provides tracking and traceability; and
manages identity, compliance and security. It may process algorithms and
deliver the output to the correct modality.”
- Laura Craft, Vi Shaffer, “Gartner: Hype Cycle for Healthcare Providers, 2017”
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Health
Catalyst
EMR at the Center vs Data at the Center
• Data moves left to right
• Amount of data available decreases left to right
• Data is stuck in silos in apps
• Static EMR Process drives data
• Apps & EMR consume AND publish data
• All data available to all apps
• Data can dynamically inform and change
process on the fly
DATA
APP
APPEDW
EMR
Data at the centerEMR at the center
EMR
Data
EDW
Data
APP
Data
APP
Data
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Health
Catalyst
Data at the Center
EMR
EDW
App
App
Data Operating System
enables interaction
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Health
Catalyst
7 Attributes Needed in a Data
Convergence Hub (DOS)
1. Reusable clinical and business logic: Registries, value sets, and other data logic lies on top of the raw
data and can be accessed, reused, and updated through open APIs, enabling third-party application development.
2. Streaming data: Near- or real-time data streaming from the source all the way to the expression of that data
through DOS that can support transaction-level exchange of data or analytic processing.
3. Integrates structured and unstructured data: Integrates text and structured data in the same
environment. Designed to integrate images too.
4. Closed-loop capability: The methods for expressing the knowledge in DOS, include delivering that knowledge at
the point of decision making, for example, back into the workflow of source systems, such as an EHR.
5. Microservices architecture: In addition to abstracted data logic, open microservices APIs exist for DOS
operations such as authorization, identity management, data pipeline management, and DevOps telemetry. These
microservices also enable third-party applications to be built on DOS, and constant delivery of software updates, rather
than massive, major updates.
6. Machine learning: DOS natively runs machine learning models, and enables rapid development and utilization of
ML models, embedded in all applications.
7. Agnostic data lake: Some or all of DOS can be deployed over the top of any healthcare data lake. The reusable
forms of logic must support different computation engines; e.g., SQL, Spark SQL, SQL on Hadoop, et al.
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37. © 2018
Health
Catalyst
Balance: Standard Data Models
AND Late Binding
Enterprise Data Model:
Everything early bound to single
standard definitions (approach
adopted by most EMRs, Big Tech
and Benchmarking companies)
Ultra Late-binding:
Don’t bind anything until the
visualization layer (the wild, wild
west of visualization tools)
Balanced Hybrid Approach
• Preserve atomic detail in Source Marts
or Data Lakes
• Shared Datamarts are standardized but
only for a subset of data that qualifies with
commonly agreed upon definitions
• Subject Area Marts can draw from either
Shared Data Marts, Source Marts or other
Subject Area Marts
37
Return to Core Principles

38. Deliver Insight
Principle:
Promote better decisions with the 5 rights
of data delivery
38

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Health
Catalyst
The most sophisticated and accurate
predictive model is worthless unless
it promotes an action
that would otherwise not have
happened
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Health
Catalyst
The How:
… you produce the
Right Actions
to improve outcomes
The Why:
We believe if you get the
• Right Information, to the
• Right Audience, at the
• Right Granularity, at the
• Right Time, in the
• Right Visualization/Modality
40
The 5 Rights of Information Delivery

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Health
Catalyst
Data becomes valuable
when an insight is discovered,
that can positively impact outcomes
such as a trend, pattern, correlation or causation.
These insights can be built into
predictive models
that can inform decision makers of likely
outcomes given certain actions.
41
The Right Information: Deliver an Insight

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Health
Catalyst
Stakeholder Group Role & Responsibility
Executive Controls resources and funding allocations
Domain Leadership
Understands domain interactions and tradeoffs
(clinical, operational or financial)
Adoption
Influences others and encourages change
(adoption of new processes)
Innovation
Identifies root cause of poor outcomes and designs
better processes to produce better outcomes
42
The Right Audience:
Four Levels of Stakeholders

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Health
Catalyst
Admits/1000 members
IP days/1000 members
OP visits/1000 members
Procedures/1000 members
ED visits/1000 members
Readmissions/1000 members
Utilization
Who should
get the care?
Cost/case
Cost/procedure
OR minutes
L&D minutes
Other LOS
Order Sets
Clinical
Support
Workflow
Cost per case
Nursing hours by unit
OR minutes
L&D minutes
Cycle times
Cost per ancillary test
Environmental services
What care
should be
included?
How can care
be delivered
efficiently ?
Indications for Intervention
Diagnostic algorithms
Indications for Referral
Triage Criteria
Treatment and Monitoring
Algorithms
Health Maintenance and
Preventive Guidelines
Standardized Follow-up Checklist
Post-acute care order sets
IP (SNF, IRF)
Home health, Hospice
Clinical Ops Procedure Guidelines
Granularity
Substance Selection Clinical Supply Chain
Management
Admission Order Sets Supplementary Order Sets
Pre-Procedure Order Sets
Post-procedure Order Sets
Bedside Care Practice Guidelines
Discharge Checklist
Patient Injury Prevention Protocol
Risk Assessment
Transfer Checklist
Question Examples of Best Practice Standard Possible Measures
Administrative
Support
Workflow
How can
administrative
operations be
performed
efficiently ?
AR Escalation Process
Network Design Process
Recruiting/Onboarding Process
AR Days
% out of network utilization
% Turnover
Team member
satisfaction/engagement
AR Escalation Process
Budgeting Process
Supply Chain Procurement
43
The Right Granularity: Best Practice
Compliance

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Catalyst
Population Loop timing: annually or decades (long-term strategic)
• As you renegotiate payer contracts
• As you redesign your network, or
• As you design care pathways and patient stratification predictive models
• As you design your building/capital plan: e.g. public health investments
Protocol Loop timing: monthly or weekly (medium-term)
• As you redesign processes and adopt Best Practice
• As you attempt to sustain gains previously achieved
• As you make prioritization trade-off decisions
Patient Loop timing: daily or real-time (short term – tactical)
• As you deliver care to support better decisions, within the workflow
Board Room
Conference
Room
Point of Care
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The Right Time: 3 Key Closed Loops

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Catalyst
Care Coordination with Care Team Patient Care Tracking & Wellness
with Family/Friends Support
*Mobile technology for communication
68% of American adults have a smartphone
92% of American adults have a cell phone
Use Case: Real-time Decision Support
Use mobile delivery modalities*
most convenient to patients and providers
Advanced Visualization / Discovery Dashboard
Use Case: Prioritization or Process Redesign
Use dynamic modalities with drillable data
Metric Region 1 Region 2 Overall
Score
Financial
Metric
XX.X XX.X XX.X
Quality
Metric
YY.Y YY.Y YY.Y
Experienc
e Metric
ZZ.Z ZZ.Z ZZ.Z
Use Case: Monitor Stable Process
Use run charts to sustain gains
(use reports and scorecards sparingly –
always drill down to a run chart)
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The Right Modality: Based on Use Case

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Catalyst
1. Use Data as a Weapon
2. Misrepresent Data
3. Prevent Appropriate Data Access
4. Disengage from the Process
5. Highlight Data Imperfections /
Discredit
6. Analysis Paralysis
7. Political Favoritism
8. Budget Cuts Across the Board
9. Delay a Decision with Stall
Tactics
10. Stick to the Status Quo
46
The 10 “Wrongs” in Healthcare
Analytic Interactions
Return to Core Principles

47.  Overview – 10 min
 Core Data Governance
Principles – 25 min
• Advanced Data Governance Principles
– 25 min
• Conclusion – 5 min
Agenda

48. Advanced Data Governance Concepts

49. © 2018
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Catalyst
Streamline Granting Access Process
Establish streamlined Data Access processes
Advanced Principles – Execute and Extend
Governance Framework
Increase strategic coordination by having an
executive champion of data governance (CAO) who
is tightly connected to improvement governance
Capture the Right Data
Capture the data needed to manage and improve
processes in the most efficient way possible
Integrate: Prioritize Data Integration
Prioritize data integration early in your journey
Deliver Insight: Data Literacy Training
Hire and train for Data Literacy
Deliver Insight: Hub and Spoke Approach
Adopt a Hub and Spoke Structural Strategy
Action: Outcome and Data Utilization Measures
Measure cost, quality and experience outcomes in
conjunction with measuring data utilization
Assess and Prioritize Data Governance
Assess and prioritize data governance initiatives by
the three common challenges: Data Literacy, Data
Quality, and Data Utilization
49
Go to Conclusion
Poll Question #3 Poll Question #4

50. © 2018
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Catalyst
Which of these 4 principles would you most like to see discussed today?
1. Governance Framework — 47%
2. Capture the Right Data — 25%
3. Integrate: Prioritize Data Integration — 24%
4. Streamline Granting Access Process — 4%
Poll Question #3
50
Return to Advanced Principles

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Catalyst
Which of these 4 principles would you most like to see discussed today?
1. Deliver Insight: Data Literacy Training — 28%
2. Deliver Insight: Hub and Spoke Approach — 16%
3. Action: Outcome and Data Utilization Measures — 34%
4. Assess and Prioritize Data Governance — 22%
Poll Question #4
51
Return to Advanced Principles

52. Governance Framework
Advanced Principle:
Increase strategic coordination by appointing
a Chief Analytics Officer (CAO) who is tightly
connected to improvement governance to
lead data governance
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53. © 2018
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Improvement InitiativeImprovement Initiative Improvement Initiative
Data
Governance
Improvement
Governance
The Chief Analytics Officer is a
member of Improvement Governance
The Chief
Analytics
Officer chairs
Data
Governance
53
Data Governance Within an
Improvement Framework

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Catalyst
Rosetta Stone: Translation between different improvement
methodologies.
Key Principle: Pick ONE Methodology and use it
consistently across your organization
Job 1 of Improvement Governance:
Pick ONE Improvement Methodology
54

55. 1 2 3 4 5 6 7
Analyze the
Opportunity
and Define the
Problem
Scope the
Opportunity
and Set Goals
Explore Root
Causes and
Set Process
Aims
Design
Interventions
and Plan Initial
Implementation
Implement
Interventions
and Measure
Results
Monitor,
Adjust, and
Continually
Learn
Diffuse and
Sustain
Is it an adoption
problem?
Are data valid?
Do we need to
adjust
our interventions?Do we need
to reevaluate
root cause?
Start with a directive from executive leadership based on high-level opportunity analysis and readiness assessment
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The Seven Essential Elements of Improvement

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Catalyst
Prioritization
Adoption
Innovation
etc.
Outcomes Improvement
Executive Leadership Team
Content & Analytics Team(s)
Data Governance
Committee
Domain Guidance Team
Provides domain oversight
and drives priorities
Outcomes
Improvement Team(s)
Drives innovation & adoption
Workgroup(s)
as needed
Workgroup(s)
as needed
Innovates
Domain 1
Domain 2
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Job 2: Improvement Governance Integrate
Improvement & Data Governance Work

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Position Purpose:
To provide leadership to the development
and standardization of the analytics and
knowledge asset infrastructure and the
methodology and vehicles used to deliver
insights to support process and outcomes
improvements in clinical/quality, cost and
patient/clinician experience across all
domains of Clinical Integration (Clinical
Programs and Clinical Support Services)
Skills:
Data Capture Quality
Data Integration
Data Literacy Training
Insight Development/Data Story Telling
Chairs Data Governance Council
Knowledge:
Healthcare Data
Healthcare Operations
Healthcare Finance and Budgeting
Streamlined Data Access Policies
Analytic Technologies
Attitude/Character:
Pragmatic Innovation
Improvement Champion
57
Key Job Functions of a Chief Analytics Officer
Return to Advanced Principles

58. Capture
Advanced Principle:
Capture the data needed to manage and
improve processes in the most efficient
way possible
XX
X
58

59. © 2018
Health
Catalyst
Capturing Data for Outcomes Improvement
1. Build/refine a conceptual model
• Care Process Improvement Map or
process flow diagram
2. Generate a list of desired measures
• Use conceptual model plus root cause
heuristic
• Format: outcome, process, balance
• Validate with target end users
3. Generate a list of data elements
• Think numerators and denominators
• Link data elements to existing reports
• Validate data capture quality at front lines
XX
X
59
4. Negotiate what you want with what you
have
• Identify data sources for each element:
existing/new, automated/manual
• Consider value of measures vs. cost of
getting necessary data
• Create self-coding data sheets or IDEA app
or ABD app to capture missing measures
5. Design technical structure
• Data sources, subject area data mart,
manual data collection, etc.
6. Build analytic routines
• Visualizations/dashboards, reports,
predictive models, closed loop alerts
7. Test updated Data Life Cycle

60. © 2018
Health
Catalyst
What Data Should We Capture:
Who Decides?
“We already capture what
we need in the EMR.”
(required by CMS or
required to bill)
“Capture everything
possible.” -or-
“Regulatory agencies know
care delivery better than
care deliverers”
“Recreational”
Data Collection
“Flying Blind”
Too Little Too Much
“Collect enough data to
manage and measure
clinical processes
effectively, and encourage
better decisions.”
Just Right
Care-Process-
Model-Driven
Data Collection
WastefulDangerous Ideal
Answer: What: Capture Key Indicators within Care Process Models
Who: Interdisciplinary Clinical Program Team – Data Stewards
60
XX
X

61. © 2018
Health
Catalyst
Key concepts for innovation in data capture
• Dynamically adjust data capture by making it content/metadata driven based on a care process
models
• Real-time Analytics can re-define data capture workflow on the fly
• Clinicians can edit and author their own workflows and care process models based on best
practice guidelines, they can also test new theories
• Because ABD is context aware from the underlying care process model, clinicians can dictate
with upfront Natural Language Processing and discrete data is captured– dramatically reducing
clinicians time in manual keyboard documentation
• Because the fundamental building blocks for care process models are clinical activities – you
automatically have more precise events to derive accurate granular costs
Activity-Based Design Concepts
for Data Capture
61
XX
X
Return to Advanced Principles

62. Deliver Insight
Advanced Principle:
Deliberately hire and train
for Data Literacy
62

63. © 2018
Health
Catalyst
Evaluate data skills, knowledge, and attitudes
Evaluate mix of resources
• Transactional System Engineer (e.g. handles EMR upgrades, maintenance)
• Data Scientist
• Outcomes Analysts
• Analytics Engineers
• Data Stewards
• Report Writers
• Super Users / Data Evangelists
Leadership literacy evaluation - Understand signal vs. noise, control charts, variation
Data Literacy Assessment
63

64. © 2018
Health
Catalyst
Analytic Streams of Interoperability
Answering anticipated questions. Single vended system.
Tells what happened.
Measures outcomes through custom data models
populated from multiple sources. Highlights gaps between
current state and best practices.
Coupling of technical expertise and domain
knowledge. Illuminates the ‘why’ behind results.
Discovers new paths for future improvements.
Reactive
Descriptive
PrescriptiveAnalyticComplexity
Technical Skill + Contextual Understanding
64

65. © 2018
Health
Catalyst
Required Technical Skills by
Analytic Work-Stream
Expert
Novice 0
1
2
3
4
5
Reactive Descriptive Prescriptive
Analytic Work-Stream Skill Continuum
Health Care Data* Data Query Data Movement
Data Modeling Data Analysis Data Vizualization
Process Improvement
65

66. © 2018
Health
Catalyst
Expert
Novice 0
1
2
3
4
5
Reactive Descriptive Prescriptive
Analytic Work-Stream Skill Continuum
Health Care Data* Data Query Data Movement Data Modeling
Data Analysis Data Vizualization Process Improvement
Technical Skills Assessment
= Client Score
Skill
Capacity
Skill Gap
Skill Gap
66

67. © 2018
Health
Catalyst
Common Leadership Problems with Data
Literacy: “Punish the Outliers”
# of
Cases
Current Condition
• Significant Volume
• Significant Variation
# of
Cases
Option 1: “Punish the Outliers” or
“Cut Off the Tail” or “Rank and Spank”
Strategy
• Set a minimum standard of quality
• Focus improvement effort on those not
meeting the minimum standard
Mean
Focus on
Minimum
Standard
Metric
Excellent OutcomesPoor Outcomes Excellent OutcomesPoor Outcomes
1 box = 100 cases in a year
67

68. © 2018
Health
Catalyst
Effective Leaders with Good Data Literacy Focus on
“Better Care”
Excellent OutcomesPoor Outcomes
# of
Cases
Current Condition
• Significant Volume
• Significant Variation
Excellent Outcomes
# of
Cases
Option 2: Identify Best Practice
“Narrow the curve and shift it to the right”
Strategy
• Identify evidenced based “Shared Baseline”
• Focus improvement effort on reducing
variation by following the “Shared Baseline”
• Often those performing the best make the
greatest improvements
Mean
Focus on
Best Practice
Care Process
Model
Poor Outcomes
1 box = 100 cases in a year
68

69. © 2018
Health
Catalyst
New Mean
Intervention
How many of your leaders
would confuse this data noise
as a signal that something is
wrong?
Data Literacy Litmus Test:
How many of your senior leaders could explain why a statistical process
control chart (SPC) with upper and lower control limits is more impactful than
a “variance to last year” report?
69

70. © 2018
Health
Catalyst HC Data Guide p. 116
Rules for Determining a Special Cause
70

71. © 2018
Health
Catalyst
Competency Categories
• Knowledge
• Skills
• Character/Attitudes
Upgrade Key Roles through extensive mentoring and training
• Analytical: Report Writer >> Analytics Engineer / Data Scientist
• Technical: EMR Upgrader >> Data Capture Designer
• Financial: RVU/RCC Analyst >> Activity Based Cost Analyst
• Domain Expert: Subject Matter Expert >> Change Agent Leader
• Senior Leadership: Stakeholder >> Improvement Champion
Data Literacy Training
71

72. Albany Data Literacy Story

73. © 2018
Health
Catalyst
Technical
IT system owners – can change data
capture (EMR/Source System) and
audits physical access to data, can
acquire new data and design efficient
data capture integrated into workflow
Analytical
Discovers insights - Can integrate,
corelate, analyze, visualize and
distribute data – tell a story with data
Key Role Capabilities
Domain Expert
Provides context (clinical, operational
or financial), knows how to safely
change workflow, owns the data and
grants access (data stewards)
Leadership
can span organizational boundaries,
fund and champion improvement and
remove roadblocks
73

74. © 2018
Health
Catalyst
Continuous Improvement Flywheel
74
IDENTIFY OPPORTUNITY
• Evaluate potential value sources
• Quantify opportunity
• Prioritize/governance
MEASURE AND MARKET
RESULTS
• Measure effort and value
• Publish and promote
• Generate more opportunities
REALIZE VALUE
• Resource effort (plan and procure)
• Accelerate change
• Sustain and spread
Tools
Data
People
74

75. Defines
Find, develop, and retain the
right people and get them in the
needed right seat so they and
the organization can be
successful
Needed Competencies (KSCs)
• Knowledge
• Skills
• Character
Performance Metrics
Talent (The best of the best)
Systematic Process
(Continuous Improvement Flywheel)
Learning Flywheel (Mentor Based)
Informs
7
LEARNING
EXPERIENCES
AND RESOURCES
LEARNING
ASSESSMENTS
COMPETENCIES
(KSCs)
75

76. © 2018
Health
Catalyst
Core
Create insights
Present insights in a
compelling way
Understanding of
healthcare data
Core Technical
PL/SQL
Data modeling
Visualization &
reporting tools
Needed for the Future
• Stats, predictive, machine learning, AI, etc. • Visualization principles (e.g. Tufte, Few)
• Quality improvement (e.g. Lean, 6 Sigma) • Project management
The Skills Needed in the Analytics Space
76

77. © 2018
Health
Catalyst
You must have both the technical skill AND the clinical or
operational context (this is usually best achieved by partnering
with a change agent who has deep domain knowledge).
Otherwise, you might jump to the wrong conclusions …
Higher ice
cream sales
We taste
better
More shark
attacks
- -
Stop selling ice cream!
Warmer weather
Higher ice
cream sales
More shark
attacks
Insights Require Technical Skill AND Context
77

78. © 2018
Health
Catalyst
"In times of change, learners inherit the future,
while the learned find themselves beautifully
equipped to deal with a world that no longer exists.“
- Eric Hoffer
Recruit Change Agents
78

79. Diffusion of Innovation
Change Agents Are Typically Early Adopter SMEs
innovators
early
adopters
early
majority
laggards
(never adopters)
* Adapted from Rogers, E. Diffusion of Innovations. New York, NY: 1995.
late
majority
Innovators. Recruit
innovators to re-design
care delivery
processes
TheChasm
N = number of individuals in group
N
N = number needed to influence group
(but they must be the right individuals)
Early adopters. Recruit early adopters to
chair improvement and to lead
implementation at each site.
(key individuals who can rally support)
79

80. © 2018
Health
Catalyst
• You need both willing and able leaders.
• Identify those wanting to lead permanent improvement efforts –
throw their hat in the ring (willingness).
• Allow those not wishing to lead to participate in the selection
process (recommend top 3 picks – those with natural leadership =
ability ).
• Executive leadership can select from top recommendations the
most open minded leaders and give them decision rights.
• Involvement in the selection process leads to much, much better
adoption later (“Onboard for the take-off not just the crash-landing.”
– Dr. David Burton).
Select Early Adopters Leaders
80

81. © 2018
Health
Catalyst
How Do You Influence a Group to Change?
81

82. © 2018
Health
Catalyst
“Things get done only if the data we gather can inform
and inspire those in a position to make a difference.”
–Mike Schmoker
Engage Key Stakeholders
82

83. © 2018
Health
Catalyst
Stakeholder
Group
Key Data Need Group Role
Executive Prioritization & Visibility Controls resources and funding allocations.
Domain
Leadership
Prioritization & Visibility Understands domain interactions and
tradeoffs (clinical, operational or financial).
Adoption
Best Practice Tracking &
Actionable Metrics
Influences others and encourages change
(adoption of new processes).
Innovation
Process Design &
Outcomes Prediction
Identifies root cause of poor outcomes and
designs better processes to produce better
outcomes.
Four Levels of Stakeholder
Information Needs
83

84. Readiness Assessment
• Quickly asses readiness with on-line surveys. (e.g. use something like survey monkey or Health
Catalyst provides a free on-line Outcomes Improvement Readiness Assessment at
https://oira.healthcatalyst.com
• As you focus in on specific initiatives spend the time to interview key stakeholders of the most
important improvement initiatives and assess capability, capacity and willingness.
84

85. Example Stakeholder Analysis
STAKEHOLDER IMPACT IMPORTANCE MATRIX AREA
(see Stakeholder Matrix)
Current HEAT
Projected
HEAT
Projected HEAT
Name of functional
role/group affected by the
change
Degree of impact
on this stakeholder
Level of
stakeholder's
influence on the
success of the
change
Where do they land
on the stakeholder
matrix?
Today
After CEO Email
goes out
After the details
of the role changes
are shared
SVPs (SEL) significant medium a. KEY PLAYER productive zone productive zone productive zone
SVPs (IL) significant high a. KEY PLAYER overwhelmed overwhelmed overwhelmed
EL significant medium a. KEY PLAYER underwhelmed productive zone productive zone
STDs significant high a. KEY PLAYER underwhelmed overwhelmed overwhelmed
TDs significant high a. KEY PLAYER underwhelmed productive zone productive zone
SDAs / DAs
(Tech Ops Pool)
significant high a. KEY PLAYER underwhelmed underwhelmed overwhelmed
Domain Experts (IL) significant high a. KEY PLAYER underwhelmed overwhelmed overwhelmed
Analytic Dirs (IL) significant a. KEY PLAYER underwhelmed overwhelmed overwhelmed
SDAs / DAs (IL) significant high a. KEY PLAYER underwhelmed underwhelmed overwhelmed
Analysts (Prod Dev) significant a. KEY PLAYER underwhelmed underwhelmed overwhelmed
Leadership Team moderate high a. KEY PLAYER overwhelmed overwhelmed overwhelmed
HR minor or none medium c. keep informed productive zone productive zone productive zone
Finance - FPA moderate medium a. KEY PLAYER underwhelmed productive zone productive zone
Accounting moderate Low c. keep informed underwhelmed productive zone productive zone
Marketing minor or none Low c. keep informed underwhelmed productive zone productive zone
Customers moderate Low d. Keep satisfied productive zone underwhelmed productive zone
Identify Champions
to represent large
groups.
Keep Satisfied
Meet Their Needs
Key Player
Manage Closely
Monitor
Minimum Effort
Keep Informed
Show Consideration
Low High
High
Low
Interest of Stakeholders
Power/Influence
ofStakeholders
85

86. © 2018
Health
Catalyst
Technical Data Capture Expert
Knowledge
• Technically understands
how to change
transactional systems
(EMR, ERP, HR systems
etc.)
• Informatics (Combination
of clinical and technical
knowledge)
• Analytic workflow
integration (where in the
data capture process
would this analytic
information most improve
decision making)
Skills Attitude / Character
• User Centered Design
expert (make it easy to do
the right thing)
• Balance under or over
alerting (avoid alert fatigue)
• Database Administration
(physically granting access
to systems)
• Advanced technology
knowledge (NLP, Medical
Devices, Hadoop, etc.)
• How can we make
clinicians lives easier from
a technical standpoint?
• Discerns between shiny
objects and pragmatic
innovation
86

87. Women & Newborn Guidance Team - Prioritization
Structure Typically Needed for Deep Effort Improvements
• Meet quarterly to prioritize allocation of
technical staff
• Approves improvement AIMs
• Reviews progress and removes road
blocks
OB NewbornGYN
Women & Newborn Guidance Leadership Dyad:
1) MD Clinical Program Director 2) Administrative Director
Domain Leadership Dyads:
1) MD Lead & 2) RN Lead
SME
Data Steward
Analytics
Engineer
Analytics Team covers
entire guidance team
Financial
Analyst
Small Teams - Innovation • Integrates Data from all relevant sources
• Meet weekly in iteration planning meeting to identify improvement opportunity and insights
• Build DRAFT processes, metrics, interventions & presents DRAFT work to Broader Teams
• Grants access of analytic assets to broader team
Domain Leadership Dyad
+ Analytics Team
OB Workgroup
Broad Teams – Adoption
• Broad RN and MD representation across system
• Meet monthly to review, adjust and approve DRAFTs
• Act as change agents to lead rollout of new process and measurement
Guidance Leadership Dyad
+ Domain Leadership Dyad
+ Analytics Team
+ Clinical representation from across system
*All resources serve in these improvement roles part time ranging from
5% (MDs) to 50% (Analytics Engineer) of their time87
Return to Advanced Principles

88. Action
Advanced Principle:
Measure cost, quality, and experience
outcomes in conjunction with measuring
data utilization
88

89. © 2018
Health
Catalyst
Act – Outcome, Process, and
Balanced Metrics
Outcomes Metrics
(Measure Results)
Process Metrics
(Measure Intermediate Processes)
Balanced Metrics
(Measure Checks and Balances)
• The high-level clinical,
financial, or experience
outcomes
• Result you are aiming at
improving
• Often reported to government
and commercial payers
• Examples: mortality rates,
readmissions rates, surgical
site infection rates
• Intermediate measures
• Often track steps in the
process that lead to a positive
of negative outcome
• Examples: door to MD time,
OR setup time, % of patients
with follow-up call
• Ensure that improvement in
one metric isn’t negatively
impacting another
• Example: patient satisfaction
(balanced metric for length of
stay reduction)
89

90. © 2018
Health
Catalyst
Act – Measure Utilization
How are you using your data as an asset?
Executive
Dashboards
Analytic
Accelerators
Closed-Loop Software
Integration (Factor 15X)
• Roll-up improvement
initiatives across the
organization
• Show leading indicators as
vitals of processes
• Help pinpoint cause of
variation and sustain
improvement over time
• Systematically ensures
analytics are driving better
decision making
90

91. © 2018
Health
Catalyst
Funding Improvement Work
“No Margin, No Mission”
»Sister Irene Kraus
Founding Chief Executive of the Daughters of Charity National Health System
American Hospital Association Chair
91

92. © 2018
Health
Catalyst
Effort
High
Light High
Value
The Improvement Spectrum Matrix –
Value and Effort
Financial Value
Clinical Value
Experience Value
Improvement Type
X
92

93. © 2018
Health
Catalyst
Effort
High
Light High
Value
Overemphasis on Deep Improvement Projects
Financial Value
Clinical Value
Experience Value
Improvement Type
93

94. © 2018
Health
Catalyst
Effort
High
Light High
Value
Overemphasis on Light Effort Projects
Financial Value
Clinical Value
Experience Value
Improvement Type
94

95. © 2018
Health
Catalyst
Effort
High
Light High
Value
Overemphasis on One Value Type
Financial Value
Clinical Value
Experience Value
Improvement Type
95

96. © 2018
Health
Catalyst
Effort
High
Light High
Value
Overemphasis on One Value Type
Financial Value
Clinical Value
Experience Value
Improvement Type
96

97. © 2018
Health
Catalyst
Effort
High
Light High
Value
IDEAL: Even Spread Across the
Improvement Spectrum Matrix
Financial Value
Clinical Value
Experience Value
Improvement Type
97

98. Funding Improvement Work:
Balancing Value Mix Helps Fund
Clinical & Experience Improvements
As your governance team
prioritizes improvement initiative
make sure that the projected
hard $ cost savings can fund
the improvement efforts required
across all value types
IDEAL: Even spread across the Improvement Spectrum Matrix
98

99. Note: For green arrows,
savings from waste
elimination accrue to
the care delivery
organization; for red
arrows, savings go to
payer organizations.
Case-rate utilization
(# cases per population)
Within-case utilization
(# and type of units per case)
Efficiency
(cost per unit of care)
FFS
Per
case
Provider
at risk
WASTE REMOVAL
LEVEL
PAYMENT METHOD
1.
2.
3.
% of all
waste
45%
50%
5%
*James Brent C and Poulsen Gregory P. The case for capitation: It’s the only way to cut waste
while improving quality. Harv Bus Rev 2016; 94(7-8):102-11, 134 (Jul-Aug).
Experts Estimate $1 Trillion of Waste in Healthcare*
Financial incentive alignment under different
payment mechanisms
99

100. Case-rate utilization
(# cases per population)
Within-case utilization
(# and type of units per case)
Efficiency
(cost per unit of care)
1.
2.
3.
% of all waste
45%
50%
5%
Waste class
a) Inappropriate cases (risk outweighs benefit)
(e.g., many cath lab procedures; CTPA)
b) Preference-sensitive cases
(when given a fair choice, many patients opt out)
(e.g., elective hips, knees; end-of-life care)
c) Avoidable cases(hot spotting; move upstream)
(e.g., team-based care)
Waste subclasses
a) Supply chain
b) Administrative & Technical inefficiencies
(e.g., regulatory reporting burden; redundant manual reporting;
current EMR function; billing/rev cycle thrash; long patient wait times)
a) Clinical variation
(e.g., QUE studies; surgical equipment)
b) Avoidable patient injuries
(e.g., serious safety event systems; CLABSI)
Examples of Removing Waste
100

101. Types of Best Practice Knowledge Assets
Admits/1000 members
IP days/1000 members
OP visits/1000 members
Procedures/1000 members
ED visits/1000 members
Readmissions/1000 members
Utilization
Who should
get the care?
Cost/case
Cost/procedure
OR minutes
L&D minutes
Other LOS
Order Sets
Clinical
Support
Workflow
Cost per case
Nursing hours by unit
OR minutes
L&D minutes
Cycle times
Cost per ancillary test
Environmental services
What care
should be
included?
How can care
be delivered
efficiently ?
Indications for Intervention
Diagnostic algorithms
Indications for Referral
Triage Criteria
Treatment and Monitoring
Algorithms
Health Maintenance and
Preventive Guidelines
Standardized Follow-up Checklist
Post-acute care order sets
IP (SNF, IRF)
Home health, Hospice
Clinical Ops Procedure Guidelines
Knowledge
Asset Type
Substance Selection Clinical Supply Chain
Management
Admission Order Sets Supplementary Order Sets
Pre-Procedure Order Sets
Post-procedure Order Sets
Bedside Care Practice Guidelines
Discharge Checklist
Patient Injury Prevention Protocol
Risk Assessment
Transfer Checklist
Question to
ask
Examples Possible Measures
Administrative
Support
Workflow
How can
administrative
operations be
performed
efficiently ?
AR Escalation Process
Network Design Process
Recruiting/Onboarding Process
AR Days
% out of network utilization
% Turnover
Team member
satisfaction/engagement
AR Escalation Process
Budgeting Process
Supply Chain Procurement
101

102. = Negative Impact = Positive or Negative = Positive Impact
Knowledge Asset
Type
Discounted
FFS
Per Diem
Per Case Bundled Per Case
Condition
Capitation
Full
Capitation
CMS Commercial CMS Commercial
Financial Alignment AND Best Practice
Operational Workflow
Diagnostic Variation
Standing Orders
Substance Selection
Triage Criteria
Patient Safety
Treatment and Monitoring
Algorithms
Indications for Referral
Indications for Intervention
Administrative Workflow
Case-rate
utilization
(# cases per population)
Within-case
utilization
(# and type of units per
case)
Efficiency
(cost per unit of care)
FFS Per case Provider at risk
102

103. © 2018
Health
Catalyst
Working with CFO sanctioned financial analyst or other key stakeholders:
• Set baseline costs for current process
• Calculate improvement value:
– Hard Cost Savings = $ will be removed from the budget next year
– Soft Cost Efficiency Gain = Improvement efficiency will allow for employee to work on higher
priority tasks
– Cost Avoidance = Project the value of reversing a trend such as an upward cost trend that
becomes flat due to improvement efforts
• Negotiate with Payers on shared savings opportunities
Funding Improvement Work Involve the
Finance Team Early in the Process
103
Return to Advanced Principles

104.  Overview – 10 min
 Core Data Governance Principles – 25
min
 Advanced Data Governance Principles –
25 min
• Conclusion – 5 min
Agenda

105. Conclusion — Principle and
Analogy Review
Lessons Learned

106. © 2018
Health
Catalyst
The Data Life Cycle
106

107. ExtendExecuteEstablishElevate
Data Governance Framework: The 4 Es
evate
Elevate the status of data
as a strategic asset of
your organization
What would make your
data a distinguishing asset
of your clinical and
business objectives?
Build your data
governance org
structure
Who are the best
individuals and how
should you organize to
realize the vision?
Identify, prioritize and
execute on data
governance improvements
in the data lifecycle
How do you ensure all are
equipped with data for better
decision making – from the
bedside to the boardroom?
How do you ensure
your data investments
are built to last?
Sustain and extend
the initial gains
107

108. Essential Elements for Improving a Process
Each key
process has an
embedded data
lifecycle
108

109. © 2018
Health
Catalyst
Data Governance Within an
Improvement Governance Framework
Improvement InitiativeImprovement Initiative Improvement Initiative
Data
Governance
Improvement Governance
109

110. © 2018
Health
Catalyst
Run a Large Process with Significant Variation
through some Data Life Cycle Questions
Do we have all the data we
need to ideally manage this
process?
Is some data missing or
inaccurate?
Have we integrated clinical,
financial and experience data
together?
Do those making decisions have
access to ALL the data that could
promote the best decisions?
What insights could be
presented at the right time
in the workflow to
encourage better decision
making?
Do we measure how well we act?
What % of the time are achievable
benefits not achieved?
110

111. Governance Framework
Advanced Principle:
Increase strategic coordination by appointing a
Chief Analytics Officer (CAO) who is tightly
connected to improvement governance to lead
data governance
Advanced Principle:
Assess and prioritize data governance initiatives
by the three common challenges: Data Literacy,
Data Quality and Data Utilization
111

112. Capture
Advanced Principle:
Improve Data Quality (timely,
accurate, complete) at the
source
Advanced Principle:
Identify meaningful data to
capture beyond the EMR, which
will improve decision making
Advanced Principle:
Capture the data needed to
manage and improve processes
in the most efficient way possible
112

113. Integrate
Principle:
Balance reusability, scalability, flexibility and
time to value when you build your strategic data
integration plan
Advanced Principle:
Prioritize data integration early in your journey
113

114. Grant Access
Principle:
Trust AND verify – grant broad access but audit
Advanced Principle:
Establish streamlined Data Access processes
114

115. Deliver Insight
Principle:
Identify opportunities
and insights across
the spectrums of
value and effort
Advanced
Principle:
Promote better
decisions with the 5
rights of data
delivery
Advanced
Principle:
Deliberately hire
and train for Data
Literacy
Advanced
Principle:
Adopt a hub-and-
spoke structural
strategy
115

116. Action
Principle:
Use improvement governance to encourage a
data-driven culture
Advanced Principle:
Measure cost, quality, and experience outcomes
in conjunction with measuring data utilization
116

117. © 2018
Health
Catalyst
• Understand the 5 key stages of the Data Life Cycle
• Demonstrate strategies to overcome the common challenges around
Data Quality, Data Utilization, and Data Literacy
• Show how a Data Governance Framework can accelerate
improvement in clinical, cost, and experience outcomes
• Have FUN while learning!
Learning Objectives
117

118. © 2018
Health
Catalyst
The Why:
We believe when you elevate data as a
strategic asset
it enables significantly
better decision making
and promotes
massive improvement
in health, cost, and experience outcomes.
118

119. Q&A

120. Thank You!

121. © 2018
Health
Catalyst
The Data Maze Game Teaser
Purpose: Learn how to use data as a strategic asset in outcomes improvement
121