Pamela Peele, Ph.D.
Chief Analytics Officer
UPMC Insurance Services
Strategies for Building a Learning Organization
1
July...
• $11 billion integrated global health
enterprise
• 2nd largest Integrated Delivery System
• 21 hospitals operating over 4...
Strong Commitment to Infrastructure and Technology:
UPMC’S Information Technology Investment
$1.6 Billion
over the past 5 ...
Advantages
• Creates synergistic provider and payer
business growth and development
strategies
• Combines provider and pay...
Levels of Analytics Framework
5
Standard ReportsWhat happened?
AlertsWhat actions are needed?
Query DrilldownWhat exactly ...
• Data that is “fit for consumption”
• Data Governance
• Tools
• Staff with strategic plans and skills
The Basics
6
• Many types of disparate data available
• Medical Claims
• Behavioral Health Claims
• Pharmacy Claims (allows medication ...
Identifying Health Conditions by SEPARATE Data Source
Identifying Health Conditions by AGGREGATING Data Source
1,596 1,994 2,197 2,344
4,086 5,698 5,698 6,774
4,324 6,588 6,588...
Stratification Data Flow
Health PlaNET
• Database: SQL, Toad
• Statistics: SAS, STATISTICA, STATA, R
• Data Mining: STATISTICA, R
• Text Mining: STATISTICA
• Mod...
• Excel
• Access
• Crystal Reports
Staff - 2006
12
Business
Analyst (30)
Accounting
Current Staff
13
Clinical
Program
Evaluation
(5)
Epidemiology
Biostatistics
Health Services
Research
Strategic
Business
An...
• Industry Knowledge
• Data visualization skills
• Data ECTL (extraction, cleaning, transformation, loading) skills
• Stat...
• Predictive modeling
• Clinical program evaluation
• Financial modeling
• Practice variation
• Text mining
• Visualizatio...
0 .990 .880 .7 70.6 60.5 50.440 .330 .22
5 00
4 00
3 00
2 00
1 00
0
Pr o b a b ilit y
Frequency
0.70.5
D is tr ibutio n o ...
Clinical Program Evaluation (Supportive Services Program)
18
• No significant change in 30 day readmit rates
• Time to rea...
Clinical Program Evaluation (Supportive Services Program)
19
When they occur, readmissions cost significantly less by $4,0...
0
2000
4000
6000
8000
10000
12000
9/28/2012 10/28/2012 11/28/2012 12/28/2012 1/28/2013 2/28/2013 3/31/2013
InfluenzaLikeIl...
• New Medicare Enrollees
– No prior clinical or claims information
• Medicare Health Assessment Survey
– 24 questions
• Wh...
Rule
Question 2
Response
Question 5
Response
Question 6
Response
Question 7
Response
Question 8
Response
Rule 1
(6.8%, N=6...
23
Average # of Imaging Services Per Admit – CY 2008
DRG 470 – Major Joint Replacement without Major Complications & Comor...
24
Average # of Consultation Services Per Admit – CY 2008
DRG 470 – Major Joint Replacement without Major Complications & ...
Average # of Subsequent Attending Visits Following Hospital Discharge – CY 2008
DRG 470 – Major Joint Replacement without ...
26
Average # of Laboratory Testing Services Per Admit – CY 2008
DRG 470 – Major Joint Replacement without Major Complicati...
EMR Text Mining
27
Provider Network Plot
28
Provider Patient Sharing Patterns
29
30
• Executive team support
– Resources
• Analysis and knowledge creation
– Not an Information Technology (IT) function
– Rep...
• Governance Structure
• IT governs data
• Analytics governs secondary data use
• Build capacity as needed, starting with ...
• Data Shopping
– Addictive
– Highly Infectious
– No known treatment once infected
– Attempts to help can make it worse
• ...
• Many Vendors
• Many Products
– Don’t interface easily
• Need a FLEXIBLE plan
• The wrong plan will costs the one thing y...
• Pamela Peele, Ph.D.
• peelepb2@upmc.edu
• 412 454 7952
Thank You
35
 iHT² Health IT Summit Denver 2013 - Pamela Peele, PhD, Chief Analytics Officer, UPMC Health Plan, Opening Keynote: "Strat...
Upcoming SlideShare
Loading in …5
×

iHT² Health IT Summit Denver 2013 - Pamela Peele, PhD, Chief Analytics Officer, UPMC Health Plan, Opening Keynote: "Strategies for Building a Learning Organization"

1,177 views
982 views

Published on

Opening Keynote: "Strategies for Building a Learning Organization"

Pamela Peele, PhD, Chief Analytics Officer, UPMC Health Plan

0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
1,177
On SlideShare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
33
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide
  • UPMC is the University of Pittsburgh Medical Center...reaching over $9 Billion in revenue over the past year ….(follow through on the rest of the details).
  • Division of Health Economics: 2006
  • Observe that rules may overlap.
  • iHT² Health IT Summit Denver 2013 - Pamela Peele, PhD, Chief Analytics Officer, UPMC Health Plan, Opening Keynote: "Strategies for Building a Learning Organization"

    1. 1. Pamela Peele, Ph.D. Chief Analytics Officer UPMC Insurance Services Strategies for Building a Learning Organization 1 July 24, 2013
    2. 2. • $11 billion integrated global health enterprise • 2nd largest Integrated Delivery System • 21 hospitals operating over 4,200 licensed beds; 187,000 admissions per year • 4.6 million outpatient visits; 480,000 emergency visits per year • >2 million Health Plan members • 400 outpatient locations • 55,000 employees • 3,400 employed physicians • 20,000+ contracted physicians • 5th NIH funding UPMC BACKGROUND
    3. 3. Strong Commitment to Infrastructure and Technology: UPMC’S Information Technology Investment $1.6 Billion over the past 5 years
    4. 4. Advantages • Creates synergistic provider and payer business growth and development strategies • Combines provider and payer expertise to drive improved outcomes • Aligns clinical and financial incentives to create value • Creates administrative efficiencies Challenges • Balancing owned vs. non-owned network • Balancing FFS and capitated models • Balancing insurers and Blue dominance in our market • Managing adverse selection Integrated Delivery and Financing System Innovation Lab UPMC Health Plans UPMC Clinical Enterprise Innovation Lab
    5. 5. Levels of Analytics Framework 5 Standard ReportsWhat happened? AlertsWhat actions are needed? Query DrilldownWhat exactly is the problem? Ad hoc ReportsHow many, how often, where? Statistical AnalysisWhy is this happening? OptimizationWhat’s the best that can happen? Predictive ModelingWhat will happen next? ForecastingWhat if these trends continue? Degree of Intelligence CompetitiveAdvantage From Tom Farre, “The Analytical Competitor”, in Analytics: The Art and Science of Better, ComputerWorld Technology Briefing. UPMC HP: 2009 UPMC HP: 2006
    6. 6. • Data that is “fit for consumption” • Data Governance • Tools • Staff with strategic plans and skills The Basics 6
    7. 7. • Many types of disparate data available • Medical Claims • Behavioral Health Claims • Pharmacy Claims (allows medication possession ratio MPR) • Worker’s Compensation Claims • Short Term Disability • Absenteeism Data from Time Cards • On-Site Biometric Screening Results • Health Risk Assessments – (self-reported) • Care Management Assessments/ Phone interaction • Enrollment & Demographic Data • Lab Values Integrated Data to Support Clinical Management Population Health Strategy and Clinical Support
    8. 8. Identifying Health Conditions by SEPARATE Data Source
    9. 9. Identifying Health Conditions by AGGREGATING Data Source 1,596 1,994 2,197 2,344 4,086 5,698 5,698 6,774 4,324 6,588 6,588 7,658 982 2,715 2,715 2,715 2,200 6,366 7,597 7,597 2,738 2,738 2,738 2,738 0 1,442 5,721 6,119 132 132 8,593 8,878 11,795 16,036 21,005 21,913
    10. 10. Stratification Data Flow Health PlaNET
    11. 11. • Database: SQL, Toad • Statistics: SAS, STATISTICA, STATA, R • Data Mining: STATISTICA, R • Text Mining: STATISTICA • Modeling & Simulation: MATLAB, Mathematica, Vensim, GEPHI • GIS: ArcGIS Tools 11
    12. 12. • Excel • Access • Crystal Reports Staff - 2006 12 Business Analyst (30) Accounting
    13. 13. Current Staff 13 Clinical Program Evaluation (5) Epidemiology Biostatistics Health Services Research Strategic Business Analysis (6) Finance Economics Policy Statistics Database & Data Quality (7) Finance Economics Policy Statistics Modeling (3) Physics Mathematics Biomedical Engineering Statistics Operations (3) Economics Industrial Engineering Operations Communications Statistics
    14. 14. • Industry Knowledge • Data visualization skills • Data ECTL (extraction, cleaning, transformation, loading) skills • Statistics • Health Services Research • Data Mining • Financial modeling & evaluation • Presentation, writing, and communication skills • Formally trained but NOT blinded by their training – Challenge deeply held beliefs Staff Skills and Backgrounds 14
    15. 15. • Predictive modeling • Clinical program evaluation • Financial modeling • Practice variation • Text mining • Visualizations, Linkages What you can do with your groomed data 15
    16. 16. 0 .990 .880 .7 70.6 60.5 50.440 .330 .22 5 00 4 00 3 00 2 00 1 00 0 Pr o b a b ilit y Frequency 0.70.5 D is tr ibutio n o f P r o ba bility fo r R e a dmis s io n F Y0 9 A cute Inpa tie nt Dis cha r ge s (A ll LO B) n = 3 8,8 40 Most impactable opportunity to prevent readmission Discharge Advocate: Risk Models Identify Readmission “Sweet Spot” 16 UPMC Project RED In Brief • Before program, at discharge, patients lacked competency in their own conditions and care: • 37% able to state the purpose of all their medications • 14% knew their medication’s common side effects • 42% able to state their diagnosis • Readmission Model targets patients at admission most likely to be readmitted for avoidable reasons • Not just for UPMC facilities: currently deployed at 10 sites –4 UPMC hospitals and 6 network facilities; additional 4 UPMC and 6 network facilities launching in 2012 Lower Risk of Readmission Less impactable despite high readmission risk Single Acute Episodes Early/Mid Stage Chronic Disease End Stage Chronic Disease 2.
    17. 17. Clinical Program Evaluation (Supportive Services Program) 18 • No significant change in 30 day readmit rates • Time to readmission significantly longer by ~11 days
    18. 18. Clinical Program Evaluation (Supportive Services Program) 19 When they occur, readmissions cost significantly less by $4,000
    19. 19. 0 2000 4000 6000 8000 10000 12000 9/28/2012 10/28/2012 11/28/2012 12/28/2012 1/28/2013 2/28/2013 3/31/2013 InfluenzaLikeIllnessVisitsPer100,000 Influenza Like Illness Epidemic Course With IBNR Adjusted Actual Costs Through January 2013 And Estimated Costs February-April 2013 SNP CHIP CMFI MC MA Pittsburgh ILI Visits $7,908,217 $4,937,557 $5,193,713 $668,217 $1,414,810 $6,690,009 $4,239,679 $3,591,971 $708,044$635,277 Projected Influenza Like Illness course with IBNR-adjusted actual costs through January 2013 and projected costs February-April 2013.
    20. 20. • New Medicare Enrollees – No prior clinical or claims information • Medicare Health Assessment Survey – 24 questions • What can you learn? • Don’t return the enrollment questionnaire – Non-returners have 22% higher annual expenditures • 5 Questions produce 8 rules = high future expenditure – 160% higher annual expenditures Learning the Rules: Using Decision Tree Models 21
    21. 21. Rule Question 2 Response Question 5 Response Question 6 Response Question 7 Response Question 8 Response Rule 1 (6.8%, N=633) X X Rule 2 (5.9%, N=549) X Rule 3 (5.7%, N=531) X X Rule 4 (6.5%, N=605) X X Rule 5 (5.9%, N=549) X X Rule 6 (8.6%, N=801) X X Rule 7 (10.5%, N=978) X X Rule 8 (9.3%, N=866) X X High Expenditure Rules 22 The percentage of members in the test set for which a given rule applies is stated below the rule. 290% 320% 290% 290% 325% 250% 275% 290%
    22. 22. 23 Average # of Imaging Services Per Admit – CY 2008 DRG 470 – Major Joint Replacement without Major Complications & Comorbidities Bubble size is proportional to the 30 day readmit rate Confidence interval bars indicated by vertical extent
    23. 23. 24 Average # of Consultation Services Per Admit – CY 2008 DRG 470 – Major Joint Replacement without Major Complications & Comorbidities Bubble size is proportional to the 30 day readmit rate Confidence interval bars indicated by vertical extent
    24. 24. Average # of Subsequent Attending Visits Following Hospital Discharge – CY 2008 DRG 470 – Major Joint Replacement without Major Complications & Comorbidities Bubble size is proportional to the 30 day readmit rate Confidence interval bars indicated by vertical extent 25
    25. 25. 26 Average # of Laboratory Testing Services Per Admit – CY 2008 DRG 470 – Major Joint Replacement without Major Complications Comorbidities Bubble size is proportional to the 30 day readmit rate Confidence interval bars indicated by vertical extent
    26. 26. EMR Text Mining 27
    27. 27. Provider Network Plot 28
    28. 28. Provider Patient Sharing Patterns 29
    29. 29. 30
    30. 30. • Executive team support – Resources • Analysis and knowledge creation – Not an Information Technology (IT) function – Reports outside of IT • Institutional Wiki and Electronic Filing Cabinet – Document, document, document Lessons Learned 31
    31. 31. • Governance Structure • IT governs data • Analytics governs secondary data use • Build capacity as needed, starting with the data • Need a professionally trained analytics leader • Centralized or decentralized? • Hire for tomorrow • Core analytics group needs diverse skillsets and backgrounds Lessons Learned 32
    32. 32. • Data Shopping – Addictive – Highly Infectious – No known treatment once infected – Attempts to help can make it worse • All those one-off databases and marts – Make something better and they go away • Language fluency – Matching words with meaning Dangers 33
    33. 33. • Many Vendors • Many Products – Don’t interface easily • Need a FLEXIBLE plan • The wrong plan will costs the one thing you don’t have TIME! Dangers 34
    34. 34. • Pamela Peele, Ph.D. • peelepb2@upmc.edu • 412 454 7952 Thank You 35

    ×