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

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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