The Value of Agile Self-Service Analytics. Mike Zuschin Director, Decision Support & Business Intelligence March 3 rd 2016

Transcription

1 The Value of Agile Self-Service Analytics Mike Zuschin Director, Decision Support & Business Intelligence March 3 rd 2016

2 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

3 Cleveland Clinic

4 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

5 Early Success with Analytics

6 Early Success with Analytics

7 Early Success with Analytics Pneumonia Vaccination Pre Post Pre Post 100% 100% 80% 80% 60% %

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10 The Phantom Menace Development Process Business Intelligence Team

11 Attack of the Clones Meeting Increased Demand Replicate the Process More of Everything (mostly people) This Worked for a While

12 Revenge of the Sith New Challenges to Analytics Strategy Care Affordability Unprecedented Changes in Healthcare Our Enterprise Data Warehouse

13 Challenges to Our Analytics Strategy Development Process Care Affordability More Change = More Analytics Our Enterprise Data Warehouse Metropolis

14 Financial Data Clinical Data

15 Database Developer Back to Star Wars Business Analyst

16 A New Hope Agile Analytics Development Self-Service/Decentralized Analytics

17 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

18 Case Study: ACO Risk Stratification

19 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

20 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

21 What Data Do We Have?

22 What Data Do We Have? 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 HIC Gender DOB Index Ranking Beneficiaries Attributed to Your TIN for the Medicare Spending per Beneficiary Measure Died during the Performanc e Period Number of Primary Care Services Provided Percent of Primary Care Basis for Date of Last Claim Services Hospital Date of Last Claim Filed by Attribution Filed by TIN by TIN EP Billed Outside by TINof TIN Billing NPI Most Non-Primary Care NameServices Characteristics of Hospital Admission Specialty Admission NPI Chronic Condition NPISubgroup Discharge Disposition Nam Apparent Lead Eligible Professional Date of Admission Followed by Unplanned All- Admission ACSC Cause Readmission within Date of Admitting Hospital Principal Diagnosis Via the ED Admission 30 Days of Discharge Discharge Discharge Status Date of Last Claim Filed by Total Payment- NPI Name Specialty NPI Standardized Episode HIC Gender DOB Index NPI Name Specialty Medicare Spending Cost per Beneficiary, by Category of Service Furnished by All Providers Date of Last Hospital Admission Diabetes Coronary Artery Disease Chronic Obstructive Pulmonary Disease Heart Failure 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

23 What Do We Need? 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 HIC Gender DOB Index Ranking Beneficiaries Attributed to Your TIN for the Medicare Spending per Beneficiary Measure Died during the Basis for Date of Last Claim Services Date of Last Hospital Claim Filed by Attribution Filed by TIN by TIN EP Billed Outside by TINof TIN Billing NPI Most Non-Primary Care NameServices Characteristics of Hospital Admission Specialty Admission NPI Chronic Condition NPISubgroup Discharge Disposition Nam Apparent Lead Eligible Professional Performanc e Period Number of Primary Care Services Provided Percent of Primary Care Followed by Unplanned All- Date of Admission ACSC Cause Readmission within Date of Admission Admitting Hospital Principal Diagnosis Via the ED Admission 30 Days of Discharge Discharge Discharge Status Chronic Date of Last Date of Last Coronary Obstructive Claim Filed by Hospital Artery Pulmonary Heart Total Payment- NPI Name Specialty NPI Admission Diabetes Disease Disease Failure Standardized Episode HIC Gender DOB Index NPI Name Specialty Medicare Spending Cost per Beneficiary, by Category of Service Furnished by All Providers Evaluation and Management Major Procedures and Ambulatory/ Minor Outpatient Physical, Occupational, or Speech and Language Pathology Ancillary Laboratory, Pathology, Ancillary and Other Imaging Durable Medical Equipment Inpatient and Hospital: Inpatient Hospital: Physician Services During ER Evaluation & Management ER ER Laboratory, Pathology, and Other ER Imaging Home Inpatient Skilled Rehabilitation Nursing or Long-Term

24 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

25 Key Population Attributes Primary care leakage Patient residence Chronic condition groups Care coordination Risk scores Primary Care Physician

26 More Iterations

27 Start With Entire ACO Population

28 Filter Away! 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

29 Tableau Server Custom Views Our physician sponsor loved having the ability to identify and save multiple populations using custom server views.

30 1,030 patients who are local, high-risk, not currently under coordination, have most of their care provided internally

31 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

32 Other Population Health Analytics Views

33 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

34 Leakage & Advanced Imaging Analytics

35 Other ACO & Population Health Dashboards

36 Agile Development: Keys to Success Don t be overwhelmed by methodology

37 Agile Development: Keys to Success Don t be overwhelmed by methodology Agile Manifesto Value These More Individuals and interactions Working software Customer collaboration Responding to change Still Value These Processes and tools Comprehensive documentation Contract negotiation Following a plan

38 Agile Development: Keys to Success 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

39 Agile Development: Impact 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 THREE TO TWELVE MONTHS WEEKS OR DAYS

40 A New Hope Agile Analytics Development Self-Service/Decentralized Analytics

41 Changes to Our Analytics Strategy Self-Service & Decentralization Empowering Data Owners & Users Case Study: Labor Productivity Keys to Success Impact Agile Analytics Development

42 Case Study: Labor Productivity Reporting

43 Background (the old days ) Management Engineering Key labor productivity metrics each period

44 Background (the old days ) Management Engineering Key labor productivity metrics each period 7 PDF Reports for each area

45 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

46 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

47 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

48 Labor Productivity Dashboard 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

49 Labor Productivity Dashboard One starting point View trends Drill to detail Select pay period Efficient Increased usage Developed & managed by SMEs

50 3-4 clicks gets me the same information as 1000 s of pages of PDF reports ~ Easy to understand & not intimidating ~ Dashboard Users

51 Our Progress to Date 2014 Started with two model areas Established similar teams in each new area Created standards & best practices guide Today More than 30 areas have begun this journey

52 Self-Service & Decentralization: Keys to Success 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

53 Self-Service & Decentralization: Impact Eliminate the Learn Their Data step X

54 Self-Service & Decentralization: Impact Eliminate the Learn Their Data step More Analytic Content More Analytically Capable Caregivers +

55 Self-Service & Decentralization: Impact Eliminate the Learn Their Data step More Analytic Content More Analytically Capable Caregivers +

56 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 +

57 Self-Service & Decentralization: Impact Eliminate the Learn Their Data step More Analytic Content More Analytically Capable Caregivers Local Initiatives Don t Need Enterprise Priority +

58 Next Steps for Us Enterprise Data Sources Self-Service Data Discovery

59 Questions?

60 Thank You Mike Zuschin Director, Decision Support & Business Intelligence March 3 rd 2016