iht² health it summit denver 2013 - pamela peele, phd, chief analytics officer, upmc health plan,...

Pamela Peele, Ph.D.

Chief Analytics Officer

UPMC Insurance Services

Strategies for Building a Learning Organization

1

July 24, 2013

• $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

Strong Commitment to Infrastructure and Technology: UPMC’S Information Technology Investment

$1.6 Billion

over the past 5 years

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

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

Com

petit

ive

Adv

anta

ge

From Tom Farre, “The Analytical Competitor”, in Analytics: The Art and Science of Better, ComputerWorld Technology Briefing.

UPMC HP: 2009

UPMC HP: 2006

• Data that is “fit for consumption”

• Data Governance

• Tools

• Staff with strategic plans and skills

The Basics

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• 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 ManagementPopulation Health Strategy and Clinical Support

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 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

Stratification Data Flow

Health PlaNET

• 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

• 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 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

• 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

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• Predictive modeling

• Clinical program evaluation

• Financial modeling

• Practice variation

• Text mining

• Visualizations, Linkages

What you can do with your groomed data

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0.990.880.770.660.550.440.330.22

500

400

300

200

100

0

Probability

Fre

qu

en

cy

0.70.5

Distr ibution of Probability for R eadmiss ion

FY09 Acute Inpatient Discharges (A ll LOB)

n = 38,840

Most impactable opportunity to

prevent readmission

Discharge Advocate: Risk Models Identify Readmission “Sweet Spot”

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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 DiseaseEnd Stage

Chronic Disease

2.

Clinical Program Evaluation (Supportive Services Program)

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• No significant change in 30 day readmit rates

• Time to readmission significantly longer by ~11 days

Clinical Program Evaluation (Supportive Services Program)

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When they occur, readmissions cost significantly less by $4,000

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

Influ

enza

Lik

e Ill

ness

Vis

its

Per

100,

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.

• 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

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RuleQuestion 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

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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%

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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

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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

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

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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

EMR Text Mining

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Provider Network Plot

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Provider Patient Sharing Patterns

29

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• 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

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• 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

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• 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

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• 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

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• Pamela Peele, Ph.D.

• peelepb2@upmc.edu

• 412 454 7952

Thank You

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