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The Value of Agile Self-Service Analytics
Mike Zuschin | Director, Decision Support & Business Intelligence | March 3rd 2016
Agenda
Cleveland Clinic & Early Analytics Success: The Phantom Menace Meeting Increased Demand: Attack of the Clones Challenges to Our Analytics Strategy: Revenge of the Sith Changes to Our Strategy: A New Hope Agile Analytics Development Self-Service/Decentralized Analytics What’s Next Questions
Cleveland Clinic
5.5 million patient visits 157,000 admissions 202,000 surgical cases
4,450 inpatient beds 75 outpatient locations 42,000+ employees 3,000+ physicians and scientists
Early Success with Analytics
Early Success with Analytics
Early Success with Analytics
Pneumonia Vaccination
60%
80%
100%
2010 2011 2012
PrePost
60%
80%
100%
2010 2011 2012
PrePost
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Development Process
The Phantom Menace
Business Intelligence Team
Meeting Increased Demand • Replicate the Process • More of Everything (mostly people) • This Worked for a While
Attack of the Clones
New Challenges to Analytics Strategy • Care Affordability • Unprecedented Changes in Healthcare • Our Enterprise Data Warehouse
Revenge of the Sith
• Development Process • Care Affordability • More Change = More Analytics • Our Enterprise Data Warehouse Metropolis
Challenges to Our Analytics Strategy
Financial Data Clinical Data
Database Developer
Business Analyst
Back to Star Wars
A New Hope • Agile Analytics Development
• Self-Service/Decentralized Analytics
Changes to Our Analytics Strategy
Agile Analytics Development
Eliminating Waste from the Process
Case Study: ACO Risk Stratification
Keys to Success
Impact
Agile Analytics Development
Case Study: ACO Risk Stratification
The Story Begins Here…
Cleveland Clinic ACO ~55,000 attributed lives
Fits with our Model of Care Patient-centered Integrated Care Care Coordination Teams Electronic Medical Record
Value for Patients Higher quality outcomes Lower cost
Problem: Where Do We Start?
Care Coordination Can’t look at 55,000 patients at once
Population Management Solution Vendor selected, but not available until December
Do We Have Any Data? Yes, but it’s all over the place
What Data Do We Have?
What Data Do We Have?
HIC Gender DOB Index †
Died during the
Performance Period
Basis for Attribution †
Date of Last Claim
Filed by TIN
Number of Primary
Care Services † Provided
by TIN NPI Name Specialty
Date of Last Claim Filed by
NPI NPI Nam
Beneficiaries Attributed to Your TIN Medicare FFS Claims Filed by Your TIN EP in TIN Billing Most Primary Care Services † EP in TIN B
HCC Percentile Ranking †
Percent of Primary Care
Services † Billed by TIN
Hospital Admission
NPI Name Specialty
Date of Last Claim Filed by
NPI
Date of Last Hospital
Admission Diabetes
Coronary Artery
Disease
Chronic Obstructive Pulmonary Disease
Heart Failure
EP Outside of TIN Billing Most Non-Primary Care Services † Chronic Condition Subgroup †
Evaluation and
Management
Major Procedures
and
Ambulatory/Minor
Outpatient Physical,
Occupational, or Speech and
Language Pathology
Ancillary Laboratory, Pathology, and Other
Ancillary Imaging
Durable Medical
Equipment and
Inpatient Hospital:
Inpatient Hospital:
Physician Services During
ER Evaluation
& Management ER
ER Laboratory, Pathology, and Other
ER Imaging Home
Skilled Nursing
Inpatient Rehabilitation or Long-Term
Medicare Spending per Beneficiary, by Category of Service Furnished by All Providers
HIC Gender DOB Index † NPI Name Specialty
Beneficiaries Attributed to Your TIN for the Medicare Spending per Beneficiary Measure Apparent Lead Eligible Professional
Total Payment-Standardized Episode
Cost †
Date of Admission
AdmissionVia the ED
ACSC Admission †
Followed by Unplanned All-Cause Readmission within
30 Days of Discharge †Date of
Discharge
Characteristics of Hospital Admission Discharge Disposition
Admitting Hospital Principal Diagnosis † Discharge Status †
What Do We Need?
HIC Gender DOB Index †
Died during the
Performance Period
Basis for Attribution †
Date of Last Claim
Filed by TIN
Number of Primary
Care Services † Provided
by TIN NPI Name Specialty
Date of Last Claim Filed by
NPI NPI Nam
Beneficiaries Attributed to Your TIN Medicare FFS Claims Filed by Your TIN EP in TIN Billing Most Primary Care Services † EP in TIN B
HCC Percentile Ranking †
Percent of Primary Care
Services † Billed by TIN
Hospital Admission
NPI Name Specialty
Date of Last Claim Filed by
NPI
Date of Last Hospital
Admission Diabetes
Coronary Artery
Disease
Chronic Obstructive Pulmonary Disease
Heart Failure
EP Outside of TIN Billing Most Non-Primary Care Services † Chronic Condition Subgroup †
Evaluation and
Management
Major Procedures
and
Ambulatory/Minor
Outpatient Physical,
Occupational, or Speech and
Language Pathology
Ancillary Laboratory, Pathology, and Other
Ancillary Imaging
Durable Medical
Equipment and
Inpatient Hospital:
Inpatient Hospital:
Physician Services During
ER Evaluation
& Management ER
ER Laboratory, Pathology, and Other
ER Imaging Home
Skilled Nursing
Inpatient Rehabilitation or Long-Term
Medicare Spending per Beneficiary, by Category of Service Furnished by All Providers
HIC Gender DOB Index † NPI Name Specialty
Beneficiaries Attributed to Your TIN for the Medicare Spending per Beneficiary Measure Apparent Lead Eligible Professional
Total Payment-Standardized Episode
Cost †
Date of Admission
AdmissionVia the ED
ACSC Admission †
Followed by Unplanned All-Cause Readmission within
30 Days of Discharge †Date of
Discharge
Characteristics of Hospital Admission Discharge Disposition
Admitting Hospital Principal Diagnosis † Discharge Status †
Let’s Play With Some Data!
Quick Iterations
No time for a typical database design/development project
Analysts doing the data integration work right in Tableau
Data Interpreter feature in 9.0 helped with the ugly Excel files
Key Population Attributes
Primary care “leakage”
Patient residence
Chronic condition groups
Care coordination
Risk scores
Primary Care Physician
More Iterations…
Start With Entire ACO Population
Remove patients in care coordination
Remove HIV, Cancer, Renal Failure patients
Remove all but Cuyahoga and surrounding counties
Try different combinations of risk score and leakage ranges
Filter Away!
Our physician sponsor loved having the ability to identify and save multiple populations using custom server views.
Tableau Server Custom Views
1,030 patients who are local, high-risk, not currently under coordination, have most of their care provided internally
1,030 patients who are local, high-risk, not currently under coordination, have most of their care provided internally
Patient lists linked directly from tool showing basic info for coordinators
Other Population Health Analytics Views
Potentially Avoidable ER Cases
NYU Algorithm • Cases not requiring Emergency Care Applied to Our Data • What - most common diagnoses? • Where - are patients coming from? • Which - facilities are they going to? • When - day of week? Inform Strategy • Access to Care Opportunities
• Locations • Hours • Types of Services
Leakage & Advanced Imaging Analytics
Other ACO & Population Health Dashboards
Don’t be overwhelmed by methodology
Agile Development: Keys to Success
Don’t be overwhelmed by methodology
Agile Development: Keys to Success
Agile Manifesto
Value These More Still Value These Individuals and interactions Processes and tools
Working software Comprehensive documentation Customer collaboration Contract negotiation Responding to change Following a plan
Don’t be overwhelmed by methodology
Start Visualizing Data Immediately (connect first)
Put Data Prep in the Hands of the Analyst
Leverage Reusable Data Assets
Frequent Iterations in Working Meetings
Share Work in Progress
Engage Clinical Representative Early
Agile Development: Keys to Success
Time to Delivery
Staffing • High-cost DB & PM vacancies replaced with
entry-level BI analyst positions • Last four hires were entry-level
Lean Data Architecture • Older tools/methods required many copies of data • Extracts & reusable assets have eliminated TERABYTES
of expensive data storage, processing, maintenance, etc.
Fail Fast Environment • React to frequently changing demands • New Insights
Agile Development: Impact
MONTHS THREE TO TWELVE
WEEKS OR DAYS
A New Hope • Agile Analytics Development
• Self-Service/Decentralized Analytics
Changes to Our Analytics Strategy
Self-Service & Decentralization
Empowering Data Owners & Users
Case Study: Labor Productivity
Keys to Success
Impact
Agile Analytics Development
Case Study: Labor Productivity Reporting
Management Engineering • Key labor productivity metrics each period
Background (the old days…)
Management Engineering • Key labor productivity metrics each period • 7 PDF Reports for each area
Background (the old days…)
Management Engineering • Key labor productivity metrics each period • 7 PDF Reports for each area • 1,000-2,000 pages in each set of 7 PDFs
Background (the old days…)
Management Engineering • Key labor productivity metrics each period • 7 PDF Reports for each area • 1,000-2,000 pages in each set of 7 PDFs • 50+ areas (Institute/Hospital/FHC) • Sharepoint site for each area (security) • Only one pay period per set of reports
Background (the old days…)
• Barriers to replacement: • Data extremely complex, from several sources • Reports & security complex, understood by few
• Solution: • Don’t teach BI Team the data/reports • Empower the data owners • BI team set up space on Tableau Server • Management Engineer used Tableau Desktop to
create an interactive, drillable dashboard
Why Would We Keep Doing This?
• Barriers to replacement: • Data extremely complex, from several sources • Reports & security complex, understood by few
• Solution: • Don’t teach BI Team the data/reports • Empower the data owners • BI team set up space on Tableau Server • Management Engineer used Tableau Desktop to
create an interactive, drillable dashboard
Labor Productivity Dashboard
Labor Productivity Dashboard
• One starting point • View trends • Drill to detail • Select pay period • Efficient • Increased usage • Developed & managed
by SMEs
“3-4 clicks gets me the same information as 1000’s of pages of PDF reports”
~ “Easy to understand & not intimidating”
~ Dashboard Users
Our Progress to Date
2014 Started with two model
areas
Created standards
& best practices
guide
Established similar teams in each new
area
Today More than 30 areas have begun this
journey
Shift Focus from Development to Enablement
Empower Local Data Owners • Establish Project Owner Role • Rounding Schedule
Establish Governance • Create Guide for Standards & Best Practices • Monitor Activity (Tableau Server)
Leverage Reusable Data Assets (again)
Cultivate User Community
Self-Service & Decentralization: Keys to Success
Eliminate the “Learn Their Data” step
Self-Service & Decentralization: Impact
X
Eliminate the “Learn Their Data” step
More Analytic Content
More Analytically Capable Caregivers
Self-Service & Decentralization: Impact
+
Eliminate the “Learn Their Data” step
More Analytic Content
More Analytically Capable Caregivers
Self-Service & Decentralization: Impact
+
Self-Service & Decentralization: Impact
+
Anesthesia HVI Research and Registries Pharmacy
Cancer Imaging QHS
Center for Connected Care International Operations Quality & Patient Safety
Clinical Genomics ITD Client Services and Support Revenue & Reimbursement
Clinical Integration Operations ITD PMO Revenue Cycle
Education Institute Management Engineering Risk Analytics
ESI Analytics Market & Network Services Strategy
Financial Planning OBGYN & WHI Supply Chain
Functional Medicine Operations Surgical Operations
Eliminate the “Learn Their Data” step
More Analytic Content
More Analytically Capable Caregivers
Local Initiatives Don’t Need Enterprise Priority
Self-Service & Decentralization: Impact
+
Enterprise Data Sources
Self-Service Data Discovery
Next Steps for Us
Questions?
Thank You
Mike Zuschin | Director, Decision Support & Business Intelligence | March 3rd 2016
Slide Number 1AgendaCleveland ClinicSlide Number 4Early Success with AnalyticsSlide Number 6Slide Number 7Slide Number 8Slide Number 9Slide Number 10Slide Number 11Slide Number 12Slide Number 13Slide Number 14Slide Number 15A New Hope�Changes to Our Analytics StrategyCase Study: ACO Risk StratificationThe Story Begins Here…�Problem: Where Do We Start?What Data Do We Have?What Data Do We Have?What Do We Need?Let’s Play With Some Data! Key Population AttributesMore Iterations…�Slide Number 27Slide Number 28Tableau Server Custom ViewsSlide Number 30Slide Number 31Other Population Health Analytics ViewsSlide Number 33Slide Number 34Other ACO & Population Health DashboardsSlide Number 36Slide Number 37Slide Number 38Slide Number 39A New Hope�Changes to Our Analytics StrategyCase Study: Labor Productivity ReportingSlide Number 43Slide Number 44Slide Number 45Slide Number 46Slide Number 47Slide Number 48Slide Number 49“3-4 clicks gets me the same information as 1000’s of pages of PDF reports”�~�“Easy to understand & not intimidating”Slide Number 51Slide Number 52Slide Number 53Slide Number 54Slide Number 55Slide Number 56Slide Number 57Slide Number 58Slide Number 59Slide Number 60