Transcript hasummit.com

Session #7 – Organizing For Analytics
Success
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Session #7 Organizing for Analytics Success
Holly Rimmasch
Chief Clinical Officer, Health
Catalyst
Ms. Holly Rimmasch is Chief Clinical Officer at Health
Catalyst. Prior to joining Health Catalyst, Ms. Rimmasch
was an Assistant Vice President at Intermountain
Healthcare and was integral in promoting integration of
Clinical Operations across hospitals, ambulatory settings
and managed care plans. Holly has spent the last 17
years dedicated to improving clinical care including
implementation of operational best practices. Ms.
Rimmasch holds a Master of Science in Adult Physiology
from the University of Utah and a Bachelor of Science in
Nursing from Brigham Young University.
Steve Barlow
Co-Founder and Senior Vice
President of Client
Operations, Health Catalyst
Mr. Barlow is a co-founder of Health Catalyst. He
oversees all technical client operations. Mr. Barlow is a
founding member and former chair of the Healthcare Data
Warehousing Association. He began his career in
healthcare over 22 years ago at Intermountain Healthcare
and acted as a member of the team that led
Intermountain’s nationally recognized improvements in
quality care and reductions in cost. Mr. Barlow holds a BS
from the University of Utah in health education and
promotion.
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Where Do We Start?
80%
Cumulative %
50%
% of Total Resources Consumed for each
clinical work process
7 CPFs
21 CPFs
Number of Care Process Families
(e.g., ischemic heart disease, pregnancy, bowel disorders, spine, heart failure)
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Effective Approach to improvement:
Focus on “Better Care”
Focus on
Best Practice
Care Process
Model
Mean
# of
Cases
# of
Cases
Poor Outcomes
Excellent Outcomes
Poor Outcomes
Excellent Outcomes
Current Condition
Option 2: Identify Best Practice
“Narrow the curve and shift it to the right”
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•
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
Significant Volume
Significant Variation
1 box = 100 cases in a year
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Y- Axis = Internal Variation in Resources Consumed
Internal Variation vs Resource Consumption
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1
4
2
Bubble Size = Resources
Consumed
X Axis = Resources Consumed Bubble Color = Clinical Domain
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Three Systems of Care Delivery Overview
Standard “Measurement” Work
Data driven prioritization
Calculations
Definitions
Enterprise Data Warehouse
Analytic
System
Data visualization
Standard “Organizational” Work
Standard “Knowledge” Work
Evidence gathering &
evaluating
Team Structures
Roles
Fingerprinting
Implementation
Deployment
System
Content
System
Knowledge assets (e.g.
Order Sets)
Starter sets
Value stream maps
Patient safety protocols
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Analytic System Core Activities
Unlocking Data to
Drive Measurements
Automating the
Broad Distribution of
Information
Analytic
System
Discovering Patterns
in Data
Deployment
System
Content
System
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Strong Analytic System
Strong Analytic System
The majority of time is spent
analyzing and interpreting data
Understanding the question
Hunting for data
Gather, compiling or running
Interpreting data
Data distribution
Non value-add
Weak Analytic System
Understanding the question
Hunting for data
Gather, compiling or running
Interpreting data
Data distribution
Value-add
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Enterprise Data Model (Technology Vendors)
EDW
DEPARTMENTAL
SOURCES
FINANCIAL SOURCES
Patient
Provider
Bad Debt
Provider
Survey
Encounter
Cost
ADMINISTRATIVE
SOURCES
Charge
Census
Facility
House
Keeping
Diagnosis
Procedure
Employee
Time
Keeping
Catha Lab
Pt. SATISFACTION
SOURCES
EMR SOURCES
More Transformation
Less Transformation
Enforced Referential Integrity
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Independent Data Marts
(Healthcare Point Solutions, EMRs)
EDW
DEPARTMENTAL
SOURCES
FINANCIAL SOURCES
Regulatory
Labor
Productivity
ADMINISTRATIVE
SOURCES
Pregnancy
Revenue
Cycle
Oncology
Heart
Failure
Asthma
Redundant
Data Extracts
Diabetes
Census
Pt. SATISFACTION
EMR SOURCES
SOURCES
More Transformation
Less Transformation
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Adaptive Data Model
Metadata (EDW Atlas), Security and Auditing
Common, linkable
vocabulary
DEPARTMENTAL
SOURCES
FINANCIAL SOURCES
Financial
Source Marts
Readmissions
Administrative
Source Marts
ADMINISTRATIVE
SOURCES
Departmental
Source Marts
Diabetes
Sepsis
EMR
Source Marts
Patient
Satisfaction
Source Mart
Pt. SATISFACTION
SOURCES
EMR SOURCEs
More Transformation
Less Transformation
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Analytic System Exercise
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The Enterprise Shopping Model
Enterprise Shopping Model
Produce
Dairy
__ Apples
__ Pears
__ Tomatoes
__ Carrots
__ Celery
__ Banana
__ Melon
__ Grapes
Meat
__ Beef
__ Ham
__ Chicken
__ Pork
__ Milk
__ Eggs
__ Cheese
__ Cream
__ 2% Milk
__ Half & Half
__ Yogurt
__ Margarine
Dry Goods
__ Turkey
__ Sausage
__ Lamb
__ Bacon
__ Pasta
__ Flour
__ Sugar
__ Soup
__ Baking soda
__ Rice
__ Beans
__ B. Sugar
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Your Shopping List
Apples
Tomato Soup
Flour
Milk
Turkey
Lettuce
Sugar
Beans
Hot dogs
Banana
Noodles
Yogurt
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Additional Items
Get eggs
Buy flowers
Get tires rotated
Pick up dry cleaning
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Enterprise Data Model (Technology Vendors)
EDW
DEPARTMENTAL
SOURCES
FINANCIAL SOURCES
Patient
Provider
Bad Debt
Provider
Survey
Encounter
Cost
ADMINISTRATIVE
SOURCES
Charge
Census
Facility
House
Keeping
Diagnosis
Procedure
Employee
EMR SOURCES
More Transformation
Time
Keeping
Less Transformation
Cath Lab
Pt. SATISFACTION
SOURCES
Enforced Referential Integrity
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Using a Independent Mart Shopping Model
https://dl.dropboxusercontent.com/u/355034/CATALYST%2090%20Second.mp4.zip
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The Independent Mart Shopping Model
Trip #1 to the Store
Trip #2 to the Store
Independent Mart Shopping Model
Independent Mart Shopping Model
Chocolate Chip Cookies
Cake
Dairy
__ 4 eggs
__ 2 c shortening
Dry Goods
__ 1 c sugar
__ 2 c brown sugar
__ 2 t baking soda
__ 2 t vanilla
__ 1 t salt
__ 4-5 c all-purpose flour
__ 4 cups chocolate chips
Dairy
Dry Goods
__ ½ cup of butter __ 1 cup white sugar
__ 1 ½ cups all-purpose flour
__ ½ cup milk
__ 2 teaspoons vanilla extract
__ 2 eggs
__ 1 ¾ teaspoon baking powder
How many recipes do you need to make?
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Independent Data Marts
(Healthcare Point Solutions, EMRs)
EDW
DEPARTMENTAL
SOURCES
FINANCIAL SOURCES
Regulatory
Labor
Productivity
ADMINISTRATIVE
SOURCES
Pregnancy
Revenue
Cycle
Oncology
Heart
Failure
Asthma
Redundant
Data Extracts
Diabetes
Census
Pt. SATISFACTION
EMR SOURCES
SOURCES
More Transformation
Less Transformation
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The Adaptive Shopping Model
Adaptive Shopping Model
Store: __________________________________________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
__ ______________________
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Shopping List Revisited
Initial List
Additional Items
And Even More
Apples
Tomato Soup
Flour
Milk
Turkey
Lettuce
Get eggs
Buy flowers
Get tires rotated
Pick up dry cleaning
Baking Powder
Baking Soda
Buy a new couch
Get oil change
Chocolate Chips
Buy yarn and knitting supplies
Vanilla extract
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•
•
•
•
•
Sugar
Beans
Hot dogs
Banana
Noodles
Yogurt
•
•
•
•
•
•
Once you are home, can you make these recipes?
Cookies:
2 cups shortening
4 large eggs
1 cup sugar
2 cups brown sugar
2 t vanilla
1 t salt
2 t baking soda
4 cups all-purpose flour
4-5 cups chocolate chips
Cake:
1 cup white sugar
1 ½ cups all-purpose flour
2 teaspoons vanilla extract
1 ¾ teaspoon baking powder
½ cup of butter
½ cup milk
2 eggs
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Adaptive Data Model
Metadata (EDW Atlas), Security and Auditing
Common, linkable
vocabulary
DEPARTMENTAL
SOURCES
FINANCIAL SOURCES
Financial
Source Marts
Readmissions
Administrative
Source Marts
ADMINISTRATIVE
SOURCES
Departmental
Source Marts
Diabetes
Sepsis
EMR
Source Marts
Patient
Satisfaction
Source Mart
Pt. SATISFACTION
SOURCES
EMR SOURCEs
More Transformation
Less Transformation
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Poll Question #1 - Analytic
How would you describe your analytics and enterprise
data warehousing approach? (choose the best answer
that applies)
a. We do not currently have a centralized analytics
data repository (e.g., enterprise data warehouseEDW)
b. We have an EDW based on the enterprise data
model approach
c. We have an EDW based on the independent data
mart approach
d. We have an EDW based on the adaptive or latebinding architecture approach
e. Unsure or not applicable
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Content System Core Activities
Analytic
System
Defining a Clinically
Driven Patient Cohort
Deployment
System
Content
System
Using Evidence to
Identify Three Types
of Waste
Standardizing Care
Delivery through
Shared Baselines.
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Strong Content System
Habit of all
Front-line Clinicians
at Every Facility
New Clinical or
Operational Best Practice
Knowledge Discovered
Time
Strong
Content
System
Measured in Weeks
Weak
Content
System
Measured in Years
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Clinical Content System Components
What Types of Waste are created without standard work?
Ordering Waste: Populations (Heart Failure, Diabetes, etc.)
Workflow Waste: Departmental
Patient Injury Waste: Patient Safety
How do we accelerate Evidence Integration into Care
Delivery?
Evidence Based Population Management Content: Outcome, process
and balanced metrics related to improvement AIM statements, intervention
indications, triage criteria, order sets, indications for referral, patient and
provider education materials, predictive algorithms, care guidelines and
protocols
Evidence Based Patient Safety Content: Outcome, process and
balanced metrics related to improvement AIM statements, At risk
screening criteria, safety protocols, near miss and incident tracking
How can data accelerate Waste Elimination?
Value Stream Maps, A3s, Standard Work starter sets,
Outcome, process and balanced metrics related to
improvement AIM statements
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Content System Exercise
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Find as many numbers
sequentially from 1 to
50 in 20 seconds.
On your mark…
Get set…
GO!
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31
46
22
19
43
1
32
47
12
18
24
25
ROUND #1
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Find as many numbers
sequentially from 1 to
50 in 20 seconds.
On your mark…
Get set…
GO!
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25 1 17 29 5 21 41 35 7 45 53 33 37 15 49 43 9 23 31 13 3 19 27 39 47
14 50 22 4 36 32 8 28 12 24 42 18 30 2 26 40 38 16 46 20 6 34 48 10 44
ROUND #2
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Find as many numbers
sequentially from 1 to
50 in 20 seconds.
On your mark…
Get set…
GO!
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
START
34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17
35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
END
ROUND #3
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Poll Question #2 - Content
Rate the level of content standardization (choose the answer that
best applies)
a. No standardization. Our clinicians use their best
judgment based on their individual training
b. We have begun to standardize some content (e.g. CPOE
to implement standardized order sets – provided by our
EMR vendor) We have not yet created standard content
for both workflow and clinical domains across the
continuum of care
c. High degree of standardization, including standardized
content for ambulatory and inpatient care management
and utilization criteria. The same workflow and care
delivery content is followed and measured regardless of
what unit or facility a patient enters
d. Unsure or not applicable
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Deployment System Core Activities
Analytic
System
Organizing for Scalable
Improvement
Applying Agile Principles to
Care Improvement
Deployment
System
Content
System
Accelerate Root Cause
Analysis by Combining
Analytics and Lean Principles
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Strong Deployment System
Strong Deployment System
Baseline
Performance
Improvement with
permanent
integrated teams
Gains
sustained over
time
Weak Deployment System
Baseline
Performance
Improvement with
focused project
team
Inability to
sustain gains
over time
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Population Health Hierarchy
“Ordering of Care”
Primary
Care
CV
W&C
GI
Respiratory
Neuro
Sciences
Musculoskeletal
Surgery
General
Med
Oncology
Peds
Spec
Mental
Health
Care
Process
Families
Care
Process
Families
Care
Process
Families
Care
Process
Families
Care
Process
Families
Care
Process
Families
Care
Process
Families
Care
Process
Families
Care
Process
Families
Care
Process
Families
Care
Process
Families
Care
Process
Families
e.g.,
Diabetes
e.g.,
Heart
Failure
e.g.,
Pregnancy
e.g.,
Lower GI
Disorders
e.g.,
Obstructive
Lung
Disorders
e.g.,
Spine
Disorders
e.g.,
Joint
Replacement
e.g.,
Urologic
Disorders
e.g.,
Infectious
Disease
e.g.,
Breast
Cancer
e.g.,
Peds
CV Surg
e.g.,
Depression
12 Clinical Programs
133 Care Process Families
1610 Care Processes
Cardiovascular
Heart Failure
Acute Myocardial Infarction
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Organization of teams
Clinical and technical
SENIOR EXECUTIVE
LEADERSHIP TEAM
Provides overall governance
and prioritization of initiatives
GUIDANCE
TEAM
Oversees data governance
Supports development
of clinical content and
analytics feedback
CONTENT AND
ANALYTICS
TEAM
CLINICAL
IMPLEMENTATION
TEAM
WORK
GROUP
Provides steady state
domain governance and
oversight
Refines Work Group
output and leads
implementation
Provides a forum to develop
and/or refine clinical content and
analytics feedback
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Ranking Comparison
Case Count
Rank
LOS Hours
(Capacity)
Rank
Total
Charges
Rank
Total Direct
Cost Rank
Total Direct
Cost
Opportunity
Rank
Trauma
9
2
2
3
3
Ischemic Heart
Disease
3
7
1
2
2
Infectious Disease
6
3
3
1
1
Pregnancy
1
1
7
4
8
Heart Failure
10
8
4
5
5
Joints
11
13
8
6
16
Normal Newborn
2
6
20
24
32
GI Disorders
4
4
6
7
4
Lower Respiratory
5
5
5
8
6
Care
Process
Family
Organizational
Readiness
(1 to 10)
1 = most ready
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Organizational Teams
Women & Children’s Clinical Program Guidance Team
Pregnancy
MD Lead
RN SME
Pregnancy
SAM
Knowledge
Manager
Normal Newborn
MD Lead
RN SME
Normal Newborn
SAM
Gynecology
MD Lead
RN SME
Gynecology
SAM
Data
Architect
= Subject Matter Expert
= Data Capture
= Data Provisioning & Visualization
= Data Analysis
Guidance Team Leads
MD Lead
Nurse Lead
Application
Administrator
• Permanent Teams
• Integrated Clinical and Technical members
• Supports Multiple Care Process Families
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Information Management
= Subject Matter Expert
DATA CAPTURE
= Data Capture
• Acquire key data elements
• Assure data quality
• Integrate data capture into operational
workflow
Knowledge Managers (Data
quality, data stewardship and
data interpretation)
DATA ANALYSIS
• Interpret data
• Discover new information in the data
(data mining)
• Evaluate data quality
= Data Provisioning
= Data Analysis
Application Administrators
(optimization of source systems)
DATA PROVISIONING
• Move data from transactional systems into
the Data Warehouse
• Build visualizations for use by clinicians
• Generate external reports (e.g., CMS)
Data Architects
(Infrastructure, visualization, analysis, reporting)
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Standard “Organizational” Work Overview
Monthly
Tasks and
Checkpoints
Kickoff
AIM Statement
• Mission
• Cohort Discover
• Data Analysis and
Review
• Best Practices
• Building Multiple
Potential AIM
statements
• Supplement
content
• Refine Cohort
• Refine Metrics
• Develop Draft
Visualizations
• Develop
Recommended
AIM statement #1
Select Initial Metric
Build and Refine
7 Steps
(Work Streams)
Implementation
Design
• Cluster Reps
Obtain Front Line
Input
• Finalize Cohort
• Develop Additional
metrics based on
feedback
• Develop Additional
Visualizations to
support
• PDSA cycle
Launch Approval
Results Review
• Cluster Reps Obtain • Collect cluster rep
Front Line Input
feedback
• Improvement Plan
• Prepare Initial
• Implementation Plan
Results from AIM
• Develop cluster rep
statement #1
assignments, and
• Summarized report
deliverables
for historical review
• Refine, recommend
AIM statement #2
1.Gather Knowledge Assets
2.Define Cohort
3.Select AIM Statement
4.Select, Build, Refine
Metrics
Build and Refine
Build and Refine
5.Develop Implementation Plan
for Process Improvement
6. Implementation
7. Measure Progress
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Deployment System Exercise
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Round 1
1 minute to describe
1 minute to draw
• Only the Clinician can talk
• Only the Architect can draw
• The Architect cannot look at
• The Clinician can only watch
the drawing (no mind
reading)
– no talking
• The Architect can’t start
drawing
1M
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Round 2
2 minutes to describe and draw
interactively
• The Architect still cannot
look at the drawing
(still no mind reading
capabilities )
• You can interact as much
as you want
• You can erase and
redraw
1M
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Poll Question #3 - Deployment
How are teams organized to improve the quality of care
and sustain improvements? (choose the answer that
best applies)
a. We have ad hoc, reactive improvement teams
organized on a project basis
b. Our quality department supports service lines
and departments for quality and workflow
improvement initiatives
c. We have organized, permanent, interdisciplinary,
process improvement teams. These teams
permanently own the quality, cost, safety and
satisfaction of their care delivery domain
d. Unsure or not applicable
48
Poll Question #4 - Deployment
How do you align and prioritize improvement priorities
across your organization? (choose the answer that
best applies)
a. We don’t have alignment of our improvement
priorities. We have free form improvement that is
prioritized in silos across the organization
b. We have alignment of our improvement priorities
within our hospital, but not across our entire
enterprise
c. We have a very clear prioritization and
governance process for our improvement
priorities, tied to our strategic plan
d. Unsure or not applicable
49
Problems with Missing Systems
Information System Centric
If we build it they will come. Focus on
reducing information request queue.
Automation Centric
Science Project Centric
Paved Cow Paths (Process is
automated but not improved –
many EMR deployments.)
Pockets of excellence, Limited
roll-out of improvements.
Analytic
System
Organization Centric
NULL SET
(Clinicians stop coming to
meetings if evidence and
measurement are both
missing.)
Deployment
System
Content
System
Research Centric
Academic ideas with no
practical application. Lots
of published papers.
LEAN Centric
Un-sustainable Improvements.
Can’t manually measure after 2 or 3 projects.
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Three Systems to Ignite Change
Scalable & Sustainable
Outcomes
Analytic System
Improved population health
Care delivery is evidenced based,
improvements in cost and quality
are scalable and sustainable
Deployment
System
Content
System
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In Summary
Don’t boil
the ocean!
All 3 systems are needed.
Analytic System
Analytic
System
• Be agile and adaptive
• Enable knowledge discovery
Content System
Deployment
System
Content
System
• Use best practices to understand
and reduce waste
Deployment System
• Leadership is key
• Permanent structures and
processes/systemic approach
• Dedicated resources
52
Analytic
Insights
Questions &
Answers
A
53
Session Feedback Survey
1. On a scale of 1-5, how satisfied were you overall with this session?
1) Not at all satisfied
2) Somewhat satisfied
3) Moderately satisfied
4) Very satisfied
5) Extremely satisfied
2. What feedback or suggestions do you have? (free form text)
3. On a scale of 1-5, what level of interest would you have for
additional learning on this topic (articles, webinars, collaboration,
training)
1) No interest
2) Some interest
3) Moderate interest
4) Very interested
5) Extremely interested
54
Upcoming Breakout Sessions
2:25 PM – 3:25 PM
Location
9. Getting the Most Out of Your Data Analyst
Grand Ballroom D
John Wadsworth, VP, Technical Operations Health Catalyst
* This is a hands-on session
10. How to Make Analytics a Strategic, C-Level
Imperative
Grand Ballroom A
Jon Brown, VP and Associate CIO, Mission Health
Gene Thomas, VP & CIO, Memorial Hospital Gulfport
11. Creating Physician Engagement
Bryan Oshiro, MD, CMO, Health Catalyst
Chris D. Spahr, MD, Enterprise Quality Executive, CHW
12. User Group Kickoff & New Product Roadmap
Thomas D. Burton, SVP, Co-Founder, Health Catalyst
Steve Barlow, SVP & Co-Founder, Health Catalyst
Holly Rimmasch, Chief Clinical Officer, Health Catalyst
Savoy
Venezia
* This is an interactive feedback session
55