Unleashing Massive Healthcare Improvements with Data

Unleashing Data:
The Key to Driving Massive
Improvements
Tom Burton, MBA
Co-Founder & Chief Improvement
Officer, Health Catalyst

© 2017 Health Catalyst
2
Learning Objectives
• Illustrate the importance of investing in analytics
training and infrastructure to prepare for massive
improvement in healthcare outcomes.
• 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.

3
To unleash the full potential of data,
organizations should adopt a
balanced approach to improvement
across the spectrums of both
effort and value.

Proprietary and Confidential4
Our Mission: Be the Catalyst for Massive Improvement in Healthcare Outcomes
510+
Team Members
113+
Improvement
Case Studies
22
Best Places to
Work Awards
90+ Million
Patients
400+
Hospitals
4,000+
Clinics
Our CustomersOur Company
Academic Medical CentersCommunity Hospitals
Children’s Hospitals
Managed Service Organizations
Integrated Delivery Systems
Accountable Care Organizations
• Stanford – Palo Alto, CA
• Albany Medical – Albany, NY
• Indiana University – Indianapolis, IN
• KUHA – Kansas City, KS
• UTMB – Galveston, TX
• Dartmouth-Hitchcock – Lebanon, NH
• Health Share of Oregon – Portland, OR
• CEPAmerica – Oakland, CA
• USMM – Troy, MI
• North Memorial – Minneapolis, MN
• Gulfport Memorial – Gulfport, MS
• Thibodaux – Thibodaux, LA
• NorthBay – Fairfield, CA
• John Muir – Walnut Creek, CA
• Gunnison Valley – Gunnison, UT
• Texas Children’s – Houston, TX
• Children’s Hospital Wisconsin – Milwaukee, WI
• MultiCare – Tacoma, WA
• Providence – Portland, OR
• Alberta Health Services – Edmonton, CA
• Memorial Care – Fountain Valley, CA
• Cedars-Sinai – Los Angeles, CA
• Kaiser – Denver, CO
• Unity Point – Des Moines, IA
• Queens Health – Honolulu, HI
• Community Health Network – Indianapolis, IN
• Health Quest – Poughkeepsie, NY
• Hawaii Pacific Health – Honolulu, HI
• UPMC – Pittsburg, PA
• King’s Daughters Medical Center – Ashland, KY
• Piedmont – Atlanta, GA
• Mission – Asheville, NC
• Orlando Health – Orlando, FL
• Cone Health – Greensboro, NC
• Christiana Care – Wilmington, DE
• Westchester – Valhalla, NY
• Allina – Minneapolis, MN
• Partners – Boston, MA
• OSF – Peoria, IL
• Springfield Clinic – Springfield, IL
• Crystal Run – Middletown, NY
• OneCare Vermont – Colchester, VT
• Adirondacks ACO – Pittsburg, NY
• Adirondacks Health Institute – Glens Falls, NY
Health Catalyst Clients:

Unleashing Data to Achieve Massive Improvements
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
$3M in 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…

Financial Value
Clinical Value
Experience Value
X
Effort
High
Light High
Value
Improvement Type
The Improvement Spectrum Matrix – Value and Effort

Effort
High
Light High
Financial Value
Clinical Value
Experience Value
Improvement Type
Overemphasis on Deep Improvement Projects

Financial Value
Clinical Value
Experience Value
Effort
High
Light High
Value
Improvement Type
Overemphasis on Light Effort Projects

Financial Value
Clinical Value
Experience Value
Effort
High
Light High
Value
Improvement Type
Overemphasis on One Value Type

Effort
Financial Value
Clinical Value
Experience Value
High
Light High
Value
Improvement Type
IDEAL: Even Spread Across the Improvement Spectrum Matrix

Which type of improvements does your organization focus on? 147
respondents
1. Clinical – 20%
2. Financial – 13%
3. Patient Experience – 10%
4. Balanced mix – 58%
Poll Question #1
12

Which quadrant does your organization tend to gravitate toward? 137
respondents
1. High Effort and High Value – 24%
2. High Effort and Light Value – 9%
3. Light Effort and High Value – 24%
4. Light Effort and Light Value – 6%
5. Balanced approach across both spectrums avoiding high effort,
light value – 37%
Poll Question #2
13

Analogy Is the Key to Understanding

Introducing the Game: SPECTRUM
• Adapted by Health Catalyst from
the popular strategy game,
7 Wonders.
• Objective of the game:
Gain the most improvement
points.
• There are multiple ways to earn
improvement points .
• 3 years, 7 cards per year.
• Play a card and pass the rest to
your neighbor.

Each Table Represents a Care Delivery System
Each Player Represents a Department in that
System

• Competition/conflict Between Departments
(+ or - Points Calculated at the End of Each Year)
• Budget for Improvement Efforts
(1 point / 3 coins)
• Data-Driven Culture (on game board)
• Light Effort Improvements
• Collaboration Between Departments
• System-Wide Adoption
• High Effort Clinical, Financial, and
Patient Experience Improvements
Scoring: Improvement Point Categories
Improvement
point symbol
At the end of Year III, once the budget disputes have been resolved, the players total
their improvement points on the scoring sheet in each of the following categories:

Game
Board
Layout
Key

Principles – Spectrum Year 1 – Invest for Success
• Invest in Data Infrastructure (The Brown Cards)
– Acquire Data
– Grant Access to Data
– Build Actionable Metrics
– Find Insights in Data
• Invest in People - Train Key Roles and Skills (The Gray Cards)
– Analytics Engineer
– Change Agent
– Key Stakeholder
• Progress on your journey toward a Data-Driven Culture (Game Board)
– From Scorecards to Embedded Analytics
– Data-driven Culture Core Capabilities

Only 8% of data required
for the population health
and precision medicine
strategy resides in
today’s EMR/EHR.
Source: Alberta Innovates Health Solutions, Secondary
Data Use Project, March 2016
http://www.aihealthsolutions.ca/initiatives-
partnerships/secondary-use-data-project/presentations/
Acquire Data . . .

Just Beginning: Digitization of Health
The Growing Ecosystem of Human Health Data
Healthcare
Encounter
Data
7x24
Biometric
Data
Consumer
Data
Genomic
&
Familial
Data
Social
Data
Outcomes
Data

Imagine the Richness of the Picture
22
The Growing Ecosystem of Human Health Data
Healthcare
Encounter
Data
7x24
Biometric
Data
Consumer
Data
Genomic
&
Familial
Data
Social
Data
Outcomes
Data
What is your 1, 3, and 5 year strategic data acquisition plan?

Acquire 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”

From Data Warehouse to Data Operating System
Traditional Data Warehouse
Health Data Convergence Hub
HC: Data Operating System
1. Collects data from EHR & Claims
2. Enables creating static reports
3. Enables SQL queries
4. Data is updated nightly
5. Not available in the EHR workflow
6. Requires replacing your existing DW
7. Proprietary schemas
8. Deals with tables and columns
1. Collects data from many sources
2. Enables creating static reports and web/mobile apps
3. Enables SQL, R, Python, Deep Learning queries
4. Data is always up-to-date
5. Insights are easily available in the EHR workflow
6. Works with your existing DW (or use our DW)
7. Industry standard schemas e.g., FHIR
8. Deals with tables, columns and clinical entities like
registries, measures
9. Provides centralized security at app and data levels
10. Machine Learning is as easy to use as SQL
11. Content Marketplace to share executable content with
other health systems

Acquire Data
Key Concepts To Accelerate Data Acquisition
Late-binding – don’t early bind complex data and waste valuable time during
data acquisitions by binding raw data into strict definitions, unless there is
persistent and global agreement, rather co-locate data into Source Data Marts or
Data Lakes with minimal transformation, allowing for flexibility in multiple future
use-cases.
Automation Tools – Use tools which automate the tedious and predictable
steps of building data marts. (e.g. Source Connectors, SMD, SAMD)
Leverage Big Data Capabilities – Make sure your data platform can leverage
silicon valley technologies like Hadoop/Spark, Machine Learning, Natural
Language Processing etc.

Grant Access
One of the most challenging
polarities for organizations to
balance is how to grant
access to data.

Data Access Polarity:
Data Protection AND Data Sharing
Symptoms of Extreme:
• Legitimate data request denied.
• IT controls all final signoff for
data access.
• 6 month process to get access.
Symptoms of Extreme:
• Data breach.
• Inappropriate data use.
Evidence of Balance:
• Streamlined access
approval process.
• Consistent regular
auditing.
• Appropriate use of
data.
• Data stewards
grant access.

Grant Access
#1 Polarity: Data Sharing AND Data Protection
Key Concepts in Granting Access effectively
Data Steward Ownership - Shift access decision away from IT into
clinical/business owners hands – IT usually overemphasizes data protection.
Trust but Verify – Assume good intent and grant access to data more liberally
but increase your frequency and depth of your auditing capability.
Create Team- or Role-based data access policies – Streamline granting
access to entire teams or role groups to access data for improvement purposes.
When a new individual joins a group or gets a new role, access is automatically
granted.

Build Actionable Metrics
The most sophisticated and accurate
predictive model is worthless
unless it promotes an action
that would otherwise
not have happened.

The 5 Rights of Information Delivery
The How:
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
… you produce the Right Action,
to Improve Outcomes
The Why:

Discover Insight
Data becomes valuable
when an insight is discovered,
such as a trend, pattern, correlation, or
causation.

Average Before=8 hours delay
Average After=3 hours delay
Is This Result Good?
Adapted from The Health Care Data Guide, p. 16-17
Poor sample size,
looks like
improvement but no
improvement
occurred in reality
Trend occurred
naturally,
Intervention didn’t
cause improvement
Change wasn’t
permanent,
numbers are
slipping
Improvement
happened before
intervention, not
because of
intervention
Outlier caused
process to look like
it needed
improvement
Real
improvement
caused by
intervention

Why Traditional Scorecards Lack Insight
Metric Region 1 Region 2 Overall Score
Financial Metric XX.X XX.X XX.X
Quality Metric YY.Y YY.Y YY.Y
Experience Metric ZZ.Z ZZ.Z ZZ.Z
Scorecard Shows:
Current Measurement vs.
Target.
Does not show:
Trend, Variation, Noise vs.
Signal, Drill-down detail.
Static Report Shows:
Current Measurement
compared with Historic.
Does not show:
Variation, Noise vs. Signal,
Trend (very well), drill-down
detail.
Metric Last Year Current Year
LY Current Month LY Year To Date Current Month Year to Date
Financial Metric XX.X XX.X XX.X XX.X
Quality Metric YY.Y YY.Y YY.Y YY.Y
Experience Metric ZZ.Z ZZ.Z ZZ.Z ZZ.Z
Scorecard
Static Report

Insight Trap:
Rush to Judgment and Punish the Outliers
Current Condition
• Significant Volume.
• Significant Variation.
Option 1: “Punish the Outliers” or
“Cut Off the Tail”
Strategy
• Set a minimum standard of quality.
• Focus improvement effort on those people not meeting the
minimum standard.
# of
Cases
Mean
Excellent OutcomesPoor Outcomes
# of
Cases
Focus on
Minimum
Standard
Metric

Leveraging Insight
Learning Approach: Focus on Better Process
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 process variation by
following the “Shared Baseline.”
• Often those performing the best make the greatest improvements.
Excellent Outcomes
# of
Cases
Focus on Best
Practice Care
Process Model
Poor Outcomes
Current Condition
• Significant Volume.
• Significant Variation.
# of
Cases
Mean

Variation Over Time
Mean
Upper Control Limit
Lower Control Limit
Adapted from R.C. Lloyd & Associates
Current Condition

Mean
Upper Control Limit
Lower Control Limit
Adapted from R.C. Lloyd & Associates
Variation Over Time
Variation Reduction

New Mean
Reduce Variation and Improve Outcomes
Intervention

Using Control Charts to Discover Insights
Adapted from The Health Care Data Guide, p. 116

– Acquire Data
– Grant Access to data
– Find Insights in data
– Change Agent
– Key Stakeholder
• Progress on your Journey Toward a Data-Driven Culture (Game Board)

“I told you I wasn’t a hunter gather. I’m an analyst!”
28
Train Analytics Engineer

Once Better Infrastructure Is in Place Analytics
Engineers Have More Time to Interpret Data
Non value-add work Value-add work
Understanding the question
Hunting for data
Interpreting data
Distributing data
Gathering, compiling or running
Weak Analytic System Strong Analytic System
Understanding the question
Hunting for data
Interpreting data
Distributing data
Gather, compiling or running
*poll of Analyst customer pre Catalyst Analytics Platform install
75%* of analyst time is spent in hunting & gathering in a weak analytics system

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

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 …
Insights Require Technical Skill AND Context
Higher ice
cream sales
We taste
better
More shark
attacks
- -
Stop selling ice cream!
Warmer weather
Higher ice
cream sales
More shark
attacks

Recruit Change Agent
"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

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)

Select Early Adopters Leaders
• 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).

Change Agent Role: Preparing People for Change
Awareness
Desire
Knowledge
& Ability
Actively
Sustaining
Time
Productivity&Performance
High
Low
Keep the chaos period as
short as possible
Minimize the loss of
Productivity & Engagement
Change!
 Ensure managers/leaders are informed and prepared
to lead their teams through the change – available and
active.
 Warn people that the process may be uncomfortable,
but that they will survive.
 Identify champions to represent key player groups in the
design and implementation process.
 Leadership defines the why the change is needed, sets
the vision, then defines the sandbox for those impacted
to participate in designing the future.

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

Four Levels of Stakeholder Information Needs
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.

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

Provide Visibility to All Stakeholders
Shared Accountability Date: 10/1/2015 Overall Status
Risks and Uncertainties
Recruit /Train Kickoff AIM Intervention Rollout plan Results
Aug 2015 Aug 2015 Sep 2015 -- Sep 2015 Oct 2015
Guidance team
Mission, charter,
roles confirmed
Review draft
cohort and data
Finalize cohort
Define rollout
plan
Review initial
results
Content and
Analytics Lead
Review AIM
options
Data quality
issues identified
Identify
intervention(s)
Guidance team
validation
Implementation
plan adjusted
Implementation
team
Best practice
gathering
Direct
observation
Direct
observation
Solicit front-line
plan input
Review lessons
learned
Workgroup
team
Profile prelim.
data and cohort
Prioritize, select
AIM
Solicit front line
input
Finalize rollout
plan
Create AIM
statement #2
Workgroup
training
Guidance team
validation
Refine cohort and
metrics
Analytics dev &
test
Guidance team
validation
Repeat process
Guidance team
validation
Improvement Initiative Progress
Key
not
started
In
process
done
well
some
concerns
strong
concerns
Accomplishments Next Steps Issues / Help needed
• Feedback sessions with SMEs • Consolidate app versions
• Define rollout plan
• QV access
Long-term
AIM Goal
To realize X% in shared savings in the JCL ACO by the end of 2015. Going forward, achieve and sustain a
X% reduction in the PMPM, a X% reduction in patient leakage, and a X% decrease in out-of-network referrals
by February 2016.
AIM Performance
Goal #1:
Source CMS Claims data into the EDW by September 15th, 2015. Wire up the Shared Accountability application to
claims data, and show incremental value via the ability to determine JCL ACO network leakage as well as PMPM costs,
by October 1st, 2015.
Launch/Rolloutdate:10/1/2015

– Acquire Data
– Change Agent
– Key Stakeholder

Developing a Data Driven Culture
“We’ve got to use every piece of data and
piece of information, and hopefully that will
help us be accurate with our player
evaluation. For us, that’s our life blood.”
- Billy Beane, General Manager Oakland A’s

The Journey Towards a Data-Driven Culture
Spreadsheet
Silos
• Silos or pockets of analysis.
• Conflicting spreadsheet reports and
interpretations of data.
• Battles over data ownership.
• Most time spent on hunting for and
gathering data.
• Focus is on is the data “right.”
Centralized
Reporting
Diabetes
Sepsis
Readmissions
Common, linkable
vocabulary
Financial
Source Marts
Administrative
Source Marts
Departmental
Source Marts
EMR
Source Marts
Patient
Satisfaction
Source Mart
FINANCIAL SOURCES
(e.g. EPSi, Peoplesoft, Lawson)
ADMINISTRATIVE SOURCES
(e.g. API Time Tracking)
EMR SOURCEs
(e.g. Cerner, Allscripts, NextGen)
DEPARTMENTAL SOURCES
(e.g. Apollo)
Pt. SATISFACTION
SOURCES
(e.g. NRC Picker, Press Ganey)
• Centralized single source of truth
established in EDW.
• Significant time spent on
standardizing definitions.
• Data begins to be trusted.
• Report queue begins to build.
• Focus is on requirements for
dashboard applications and
reports.
Data-Driven
Improvement Culture
• Improvement teams use analytics to
accelerate best practice adoption.
• Data drives decisions and actions.
• Focus is on growing and sustaining
outcomes improvement through
elimination of waste and variation
leveraging analytics.

Analytics Embedded in Workflow
Imagine: Facebook as an EHR
From a blog Dale Sanders wrote in 2010
• Patient’s evolving health story at the
center of the record, not the encounter.
• Embedded video and images.
• Text and discrete data.
• Secure messaging.
• Social support from family & friends
• Flexible security, defined by the patient.
They expand our sense of
connectedness.
Analytics is embedded…
it’s ambient to the
experience.

Analytics Embedded in Workflow
Imagine: Amazon as a Clinical Order Entry System
• Drug and device availability
• Pricing
• Home delivery
• Automatic refills
• Patient reported outcomes
From a blog Dale Sanders wrote in 2010

Kawamoto et al, University of Utah, BMJ, 2005
Physicians are 15x more likely to change their ordering and
treatment protocols if presented with substantiating data at the
point of care vs. presented with the same data in a clinical
process improvement meeting.

InformationCost
Information Benefit
High
Low High
Bubble Size = Count of Information Type
Lower the cost of
building reports through
more automated data
acquisition tools.
Analytics Embedded in
Workflow Software
Predictive Models/
Machine Learning
Dynamic Visualizations
(e.g. Qlik Tableau)
Parameter Based
Reports
Static Reports &
Score Cards

Workflow Software
Predictive Models/
Machine Learning
(e.g. Qlik Tableau)
Parameter Based
Reports
Static Reports &
Score Cards
InformationCost
Information Benefit
High
Low High
Lower the cost of
building more advanced
analytics by leveraging
better design tools and
well trained Analytics
Engineers.

InformationCost
Information Benefit
High
Low High
Replace & consolidate static
reports and score cards with more
advanced analytics over time. Analytics Embedded in
Workflow Software
Predictive Models/
Machine Learning
(e.g. Qlik Tableau)
Parameter Based
Reports
Static Reports &
Score Cards

InformationCost
Information Benefit
High
Low High
Dramatically increase analytics
embedded in workflow as they are
15 X more likely to be adopted.
Workflow Software
Predictive Models/
Machine Learning
(e.g. Qlik Tableau)
Parameter Based
Reports
Static Reports &
Score Cards

Capabilities to SCALE Outcomes Improvement
Leadership, Culture, and Governance
Financial Alignment
Where do we focus?
How are we compensated?
What should we
be doing?
How are we doing?
How do we change?
Clinical Outcomes
Cost Outcomes
Experience Outcomes

– Acquire Data
– Change Agent
– Key Stakeholder

Which infrastructure component does your organization struggle with?
154 respondents
1. Acquiring data – 29%
2. Granting access to data – 10%
3. Building actionable metrics – 32%
4. Finding insights in data – 29%
Poll Question #3
65

Which role is the most scarce in your organization? 154 respondents
1. Analytics engineer – 51%
2. Change agent – 43%
3. Key stakeholder – 6%
Poll Question #4
66

Which capability/question does your organization struggle with most?
151 respondents
1. Analytics: How are we doing? – 14%
2. Best Practice: What should we be doing? – 7%
3. Adoption: How do we change? – 51%
4. Financial Alignment: How are we compensated? – 10%
5. Governance: Where do we focus? – 18%
Poll Question #5
67

Principles – Spectrum Year 2 – Leverage the Entire Spectrum
• On-Going Opportunity Analysis (What card should I play?)
– Return on Luck
– Build an Opportunity Pipeline (at least 3 X your capacity)
– Evaluate Each Opportunity (Effort, Value, Capability, Capacity, and Willingness)
• Light Effort Improvements (The Blue Cards)
– The Prerequisites of Organic Improvement
– Be Opportunistic – TCH – Chest X-ray Story
• Deep Continuous Improvement (The Green Cards)
– Avoiding the Tower of Babel – Pick ONE Improvement Methodology
– Organize Consistent On-going Interdisciplinary Teams
• No Margin, No Mission (The Coins)
– Involve the Finance Team Early and Establish Baselines for ROI Calculations
– Fund Clinical Outcomes with Financial Results in Other Domains
– Overhead Value Analysis

On-Going Opportunity Analysis
“Luck is not the key.
How you handle good or bad luck
is what matters.”
- Jim Collins

Return on Luck – Jim Collins
Return on Luck Keys
• Do both great and mediocre
companies encounter the same
amount of luck, good and bad? -
YES
• What can you do to capitalize on
your luck?
• Have you turned your bad-luck
events into a big part of what
makes your company great?
• Are you squandering your good
luck events?
Good Luck
• Governor with healthcare background is elected.
• Payer agrees to shared savings upside
opportunity.
• Local provider wants to join healthcare system
and open an institute.
Bad Luck
• Joint Commission visits at a bad time.
• New regulatory reporting required.
• Major philanthropy contributor stops giving.
• Competitor opens new facility.
• PCP refers out of network.
Common “Luck” Events in Healthcare

Build a Pipeline of Opportunities
One of the Analytics Engineer’s primary roles is to fill the pipeline of insights
and potential improvement opportunities.
Pipeline should exceed the execution capacity
of the organization.
• Motivates an increase in capacity.
• Teaches the organization to prioritize based on highest value.
• Allows for back-up opportunities to be advanced should a high priority
initiative stall indefinitely.
Do not limit pipeline to high-effort opportunities. Many organic improvements
can occur simply by exposing the opportunity.

Value
Type
Financial
(Hard $)
Clinical /
Operational
Experience
Light
< 1 FTE or
$XXX
Process
Metric
Improved
Save
Clinician or
Patient Time
Medium > 1 < 3 FTE
Patient
Outcome
Improved
Overall
Satisfaction
score
improves
High > 3 FTE Life Saved
Evaluate Each Opportunity
Return on Investment
Effort to Achieve
• Light: < 1 FTE investment
• Medium: > 1 but < 3 FTE
• High: > 3 FTE investment
Value of Opportunity
Capability
• Do we have the skills required? What would it take
to gain skills?
• Do we have the equipment/tools required?
Capacity
• Do the stakeholders required to make the change
have the time to focus on this improvement? What
can we take off their plate?
Willingness
• Is there an acceptance of the need for change? Do
we have front-line buy-in?
• How much resistance will be encountered? From
whom? (mini stakeholder analysis)
Organizational Readiness

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.

– Return on Luck

Light Effort Organic Improvements
To promote organic improvements organize Analytic Resources in a Hub and Spoke model
Centralized (Hub)
• Analytics Infrastructure.
• Analytic Engineer Training.
– Tool Training.
– Visualization standards.
– Statistical Analysis.
– Machine learning.
• Data Governance.
– Data Steward Training.
– Common Metric definitions.
– Data Quality Standards.
– Standardized Visualization Look & Feel to
promote ease of use.
De-centralized (Spoke)
• Responding to departmental
questions using Ad-hoc queries
or analysis.
• Custom dashboard
development.
• Interpretation of data, based on
local context and knowledge.

76
• 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

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

– Return on Luck

Rosetta Stone: Translation between different
improvement methodologies.
Key Principle: Pick ONE Methodology and use it
consistently across your organization
Avoiding the Tower of Babel –
Pick ONE Improvement Methodology

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
The Seven Essential Elements of Improvement

Organization of Teams
Clinical and Technical
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

Women & Newborn Guidance Team - Prioritization
Key
Key Stakeholder
Change Agent/SME
Analytics Engineer
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 time.

Process Aim:
By X date increase the number of patients who have
follow-up completed within 7 days from X% to Y%.
Outcome Goal:
By X date decrease readmits from 22.1% to 17.7%.
Track follow-up compliance
(hospitalists, advanced practitioner,
cardiology nurse) discharge and
post results weekly.
Develop, educate, and implement
on a discharge scheduling protocol
to facilitate improved appointment
follow-up 7 days per week.
Intervention #1 Intervention #2
Heart Failure Example, Focus on Transitions

– Return on Luck

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

Funding Improvement Work
Involve the Finance Team Early in the Process
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

The Right Granularity: Best Practice Compliance
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

Involve Finance Early: Payment Model Considerations
= Negative Impact = Positive or Negative = Positive Impact
Improvement Type
Discounted
FFS
Per Diem
Per Case Bundled Per Case
Condition
Capitation
Full
Capitation
CMS Commercial CMS Commercial
Workflow
Diagnostic Variation
Standing Orders
MedicationSelection
Triage
Patient Safety
Ambulatory Treatment
and Monitoring
Indications for
Intervention
Operational Workflow
Diagnostic Variation
Standing Orders
Substance Selection
Triage Criteria
Patient Safety
Treatment and Monitoring
Algorithms
Indications for Intervention
25
Administrative Workflow
Depending on the
type of
improvement,
the financial impact
could be positive
or negative based
on the payment
model mix.
Therefore,
proactively involve
finance and
negotiate shared
savings with payers
up-front when
possible.

Funding Improvement Work:
Balancing Value Mix Helps Fund Clinical & Experience Improvements
High
Light High
Value
IDEAL: Even spread across the Improvement Spectrum Matrix
Effort
Financial Value
Clinical Value
Experience Value
Improvement Type
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.

Funding Improvement Work:
Overhead Value Analysis
• Frequently an organization has built up a large
inventory of regularly produced reports (with an
associated cost)
• In addition, many vended technical
point-solutions continue to be maintained over
decades (with an associated maintenance fee)
• Overhead Value Analysis is the “spring
cleaning process” of reviewing the cost to
maintain or produce these analytic assets
compared with the benefit each currently is
providing the organization.

– Return on Luck

How does your organization prioritize opportunities for outcomes
improvement? 122 respondents
1. We can’t say no to anything. – 14%
2. We estimate the value to the organization. – 9%
3. We estimate both effort and value. – 20%
4. We estimate effort, value, and readiness. – 25%
5. It’s all about the politics. – 32%
Poll Question #6
92

Which of the prerequisites of organic improvement is weakest in your
organization? 121 respondents
1. Broadly accessible data. – 22%
2. Alignment around an analytic toolset. – 18%
3. Analytic training and support. – 25%
4. Communication of analytic value and roadmap. – 31%
5. All of these components are working well. – 4%
Poll Question #7
93

True or False: My organization has stale analytic reports that are
never used but continue to be produced? 144 respondents
1. True – 73%
2. False – 27%
Poll Question #8
94

Principles – Spectrum Year 3 – Sustain and Spread
• Interdepartmental Collaboration Requires Good Data Governance (The Yellow
Cards)
– Improve Data Quality.
– Train for Data Literacy.
– Promote Appropriate Data Access.
• System-Wide Adoption (The Purple Cards)
– Establishing an Analytic Services Working Group (User Group).
– Marketing your Analytics Like a Small Business (The Improvement Vignette).
– Establish Improvement Governance.
• Avoiding Conflict and Contention (The Red Cards)
– The Worst 10 Practices in Healthcare Analytic Interactions.
– The Productive Zone – Helping Everyone Engage in the Work.
– Improving Interdepartmental Communication (Intent and Impact).

Interdepartmental Collaboration
“Coming together is a beginning.
Keeping together is progress.
Working together is success.”
-Henry Ford

Definition of Data Governance
Data governance refers to the plans, processes, and
principles 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.

The Triple Aim of Data Governance
1. Ensuring Data Quality
• Data Quality = Completeness x Accuracy x Timeliness.
2. Building Data Literacy
• Train on analytics basics (Data Awareness).
• Technical tools and analytic techniques (Analytics Team).
• Data content (Context provided by Data Stewards).
3. Maximizing Data Utilization
• Promote cross-department appropriate usage of data. Track # of users
per month by dashboard. Measure direct access.
https://www.healthcatalyst.com/demystifying-healthcare-data-governance
Utilization

Data Use Defines Data Quality Requirements
Balance the cost of achieving data quality with benefit.
Data Use Category Improvement Comparison or
Accountability
Research
Aim Outcomes Improvement Comparison, Choice,
Spur Change
Discover New
Knowledge
Test observability Test Observable No Test Test Blinded
Sample size Just Enough Obtain 100% Just in case
Flexibility of hypothesis Flexible, Changes as
Learning Takes Place
No Hypothesis Fixed
Is change an
improvement?
Run Charts and
Shewart Charts
No Change Focus T-Test, F-Test, p-value
Cost of data quality Low/Medium High Medium/High
Common challenges Tendency to Apply
More Rigor Than
Needed
Data Often Used for
Punishment
Access to Data
Sometimes
Problematic
Adapted from The Health Care Data Guide, p. 27

Data Literacy - Typical Current State
• Large backlogs of
analytic/report
requests.
• Knowledge workers = clinical
or operational knowledge
AND access to tools and
data.
Knowledge
Workers
Drillers
Viewers
User Distribution

Authors or
Knowledge
Workers
Viewers
Drillers
Knowledge
Workers
Data Literacy - Desired Future State
Increase number of knowledge workers by
doing the following:
• Expand data access.
• Simplify data structures.
• Continue use of naming standards.
• Provide better tools.
Promote shift in culture by rewarding process
knowledge discovery rather than punishing
outliers.
Desired User Distribution

Trust but Verify:
The Importance of Broad Access AND Auditing
Key Polarity: Grant Broad Access to Promote Utilization AND Rigorously Audit Appropriate Use of Data
https://www.idera.com/productssolutions/sqlserver/sqlcompliancemanager
Idera Compliance Manager
Audit tool example:
Idera Compliance Manager

Cards)

Analytical Services Working Group (ASWG) User Group
Purpose of ASWG:
Serve as a forum where analysts from the various
domains within the organization can collaborate to
define standards and share knowledge.

Analytical Services Working Group (ASWG)
Responsibility Summary
Define information and analytical standards.
• Standardize calculations and definitions.
• Recommend tools and processes.
• Establish data quality standards.
Provide technical and domain cross-training.
Information consumers (analysts) provide feedback to technical staff on tools, information,
performance, processes, etc…
Technical staff (analytics engineer team) provides status updates and notices to analysts on
infrastructure and content.

System-Wide Adoption
“Run your analytics department
like a small business.”
- Dale Sanders

Data-Driven Outcomes Improvement & Decision Making
Service
Oriented
Engaged,
High Contributing
Analytics Engineers Satisfied, Empowered
Analytics Customers
Scalable, Sustainable
Products
Employee
Focused
Technically
Sound
Analytic Asset Adoption
Running Your Analytics Department Like a Small
Business

System-wide Adoption: Effective Marketing
Effectively Marketing your Analytics Department requires
Two key elements:
• Educate
– Let the organization know what’s available.
– Help them become more sophisticated users of analytics.
• Share Results
– Success breeds success.
– Publish Improvement Vignettes.

NO READMITS Bundle Successfully Lowers
COPD Readmissions
Chronic Obstructive Pulmonary
Disease (COPD) is responsible for
approximately 135,000 deaths
annually, making it the third
leading cause of death in the U.S.
Nationally, there are
approximately 700,000
hospitalizations with the principal
diagnosis of COPD each year,
with one in five patients being
readmitted within 30 days. The
national average cost for a COPD
readmission is between $9,000
and $12,000.
95% of COPD patients assessed for readmission risk.
17% reduction in readmission rate. Approximately 34 fewer
patients with COPD readmitted each year, saving an
estimated $360,000 annually based on national benchmarks.
9% improvement in PCP notification.
97% of patients with COPD get an order set.
Building from work done by the Heart Failure team, MultiCare's
Medicine Collaborative developed a NOREADMITS bundle
consisting of nine interventions for patients with COPD who are not
mechanically ventilated. The intent of the bundle was to decrease
the likelihood of readmission. After less than nine months, they
achieved the following results:

[Headline – the One Sentence Grabber]
The Setting:
What is the situation and
context?
The Challenge:
What was the complication?
What problem are we solving?
The Result:
What happened?
What was improved?
The Turning Point:
What interventions where used to make a difference?
How were analytics used in solving the problem?
What expertise was needed for the improvement?
Organization Logo

System-Wide Adoption: Improvement Governance
Stakeholders:
Starting at the
top, engage all
stakeholders
around a common
vision.
Shared
Understanding:
Have a common
understanding of
organizational needs,
capabilities, and
readiness.
Alignment:
Use a consistent
improvement
methodology, align
incentives, and
balance polarities.
Focus:
Practice disciplined
decision-making to
prioritize, fund,
organize, and
sustain initiatives.
Key Objective of Improvement Governance:
Move from a loose federation of hospitals and clinics
that share supply purchasing . . .
. . . to an integrated care delivery system that delivers
consistent high quality, coordinate care everywhere
across the continuum at the lowest appropriate cost.
Governance Quest
HAS 16 Game

Cards)

Avoiding Conflict & Contention: Creating a Pause
“Freedom is the ability to
pause between stimulus
and response and in the
pause to choose.”
- Viktor Frankl

The 10 Worst Practices in Healthcare Analytic Interactions
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.

Signs Someone Is Outside the Productive Zone
Blame others, distract attention, denial

Start With Compassion
When you don’t know what to try first,
lower the heat.
• Validate feelings, acknowledge loss.
• Simplify and clarify.
• Address the technical aspects.
• Break the problem into parts.
• Restore, add, or reallocate resources.
• Temporarily reclaim responsibility for tough issues.
• Give your attention.
• Take stock of what is available.
• A lot more time, enrich knowledge and skills.
Nobody misbehaves from a place of strength.

Avoiding Contention: Understand Intent & Impact
“I know that you believe you
understood what you think I said, but I
am not sure you realize that what you
heard is not what I meant.”
- Robert McCloskey

Other’s impact on me
My intention Intention of other
My impact
Harvard Negotiation Project
Mine Other’s
Intention
Impact



Mine Other’s
Intention
Impact



Our assumptions
about intentions
are often wrong.
Good intentions
do not make bad
impact unimportant
or irrelevant.
Mine Other’s
Intention
Impact

Other’s
This is what I
meant.
This is how it
felt/seemed to me.
Is that what you
meant?
How did it feel/land
with you?
Mine
Intention
Impact

Cards)

Within the triple aim of data governance, our organization struggles
most with? 113 respondents
1. Data Quality. – 25%
2. Data Literacy. – 31%
3. Data Utilization. – 41%
4. We’re great at all three of these. – 3%
Poll Question #9
123

Which of these 5 practices have you seen at your organization?
(select all that apply) 109 respondents
1. Use Data as a Weapon. – 32%
2. Misrepresent Data. – 41%
3. Prevent Appropriate Data Access. – 39%
4. Disengage from the Process. – 54%
5. Highlight Data Imperfections / Discredit. – 55%
Poll Question #10
124

Spectrum - Key Takeaways and Lessons Learned
Set your organization up for success
• Invest in Infrastructure (Acquire Data, Grant Access, Build Actionable Metrics and Discover Insights).
• Train Key Roles and Skills (Train Analytics Engineers, Recruit Change Agents and Engage Key Stakeholders).
• Create a Data-Driven Culture (Embed Analytics in Workflow, Invest in 5 Core Capabilities for improvement 1) Leadership,
Governance & Culture, 2) Analytics, 3) Best Practice, 4) Adoption and 5) Financial Alignment).
Unleash data across the Spectrum of Improvement
• Perform On-going Opportunity Analysis – Asses Value, Effort, Capabilities, Capacity and Willingness.
• Enable Light Effort Improvements – Prerequisites 1) Grant Broad Access, 2) Analytic Toolset Alignment, 3) Training and Support,
4) Communication and 5) Analytic Leadership; Be Opportunistic – improve your Return on Luck.
• Invest in Deep Effort, High-Value Improvement - Pick ONE Improvement Methodology, and Organize consistent on-going
interdisciplinary teams.
• Understand No Margin, No Mission – Involve the Finance time early, Fund clinical outcomes with financial results in other
domains, and use Overhead Value Analysis to eliminate less value reports and point solutions.
Sustaining and Spreading Improvement
• Collaborate with information & resources – Establish Data Governance – increase Data Quality, Data Literacy and Data Access.
• Promote system-wide adoption – Create an Analytic Services Working Group to promote standards & knowledge sharing, Market
you Analytics like a small business and establish improvement governance.
• Reduce wasteful contention – Avoid worst practices such as using data as a weapon, help everyone engage in the work by staying
in the ”Productive Zone, and improving communication by understanding intent and impact.

A
Questions &
Answers

Thank You

Unleashing Massive Healthcare Improvements with Data

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