PREDICTION MARKETSBusiness Intelligence Community

March 10, 2009

Tony Tsai (anttsai@umich.edu)Matthew Comstock (comstock@umich.edu)

University of Michigan Health System

Prediction Markets

University PresidentMary Sue Coleman. PhD

Hospitals &Health Centers

Doug Strong

Medical School/Fac. Group Practice

Jim Woolliscroft, MD

Michigan HealthCorporation

Robert Kelch, MD

Executive Vice President of

Medical AffairsRobert Kelch, MD

University of MichiganHealth System

UMHS Overview

Mission: The University of Michigan Health System improves the health of patients, populations and communities through excellence in education,

patient care, community service, research and technology development, and through leadership activities in Michigan, nationally and internationally.

• Medical School Established – 1850

• First University Hospital Opened - 1869

• Today a large and vibrant organization:– 15,000 staff– 2,600 faculty in 27 academic departments– 680 medical students, 475 graduate

students & 1,000 house officers (residents)– 5.6M in total square footage of educational,

research and clinical space

• FY2008 total revenues exceeding $2.8B

• National leader in education, research and patient care

UMHS Overview

• Planning Process at UMHS– Varied and complex

– Strategic & tactical

– Occurring at all levels of the organization and across all components of our mission

• UMHS Finance: Short- & Long-Range Projections– Monthly, Quarterly and Annual projections on faculty recruitment,

key activity drivers, operational performance, margin, compensation, research productivity, endowment returns, payer mix, enrollment, etc.

– 10-Year Strategic Financial Plans & Major Capital

UMHS Planning

UMHS Planning

• UMHS Financial Planning - Techniques Deployed:– Annual Budget Processes (School & Hospital)

– Major Project Planning (capital construction)

– Structured Discussion & Meetings

– Statistical Modeling & Trend Analyses

– Simulation Models

– Internal Surveys & Ad Hoc Data Requests

– Expert Opinion & Management Decisions

– Prediction Markets?

UMHS Planning

Prediction Model Pilot

• Original Intent: to explore the use of prediction markets to support capital investment decisions by confirming operational assumptions and future business environment

• UMHS Finance was chosen to do the pilot because of its quantitative ability and manageable size of around 80 (also considered MSIS/MCIT)

• Test usability, predictive ability, and fit with organizational managerial culture

Potential Organizational Uses

• Predict likely demand for certain type of treatment (support capital investment strategy)

• Predict likely external economic conditions (e.g. reimbursement rates, payor mix )

• Predict on time launch of organizational initiatives (e.g. opening of new facility, success of union negotiations)

• Predict internal cost savings from organizational initiatives

Prediction Markets

• “Outcomes” are the commodities or stocks

• Insiders and outsiders are invited to participate in the “market”

• Participants are given virtual currency to trade on the most likely outcomes

• Market lasts until the outcomes resolve themselves (~6 month window)

• Market winners often receive incentives

Examples of Prediction Markets

Examples of Prediction Markets

Examples of Prediction Markets

Cummulative number of companies that have implemented an internal prediction market

(lower bound estimate)

0

5

10

15

20

25

30

35

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Source:

Policy Analysis Market

Organizations Using Predictive Mkts.

Wisdom of Crowds

Expert Non-Expert

Better Answer

Good Answer

Benefits of Prediction Markets

• Leveraging untapped institutional knowledge (incl. external stakeholders and customers)

• Increase in information awareness and transparency

• More robust predictions to support strategic decision-making

Prediction Model Pilot

• Training Round (6 weeks)– Will the football team have more than 5 wins? (Y/N)

– How much will the movie “Hancock” gross in its opening weekend? ($)

– Who will win the U.S. Open, Roger Federer, Rafa Nadal, or Other? (Choice)

• Operational Round (6 weeks)– Will total outpatient clinic visits year-to-date (7/1/08 to 10/31/08) exceed

605,000 visits?

– What will be the number of total inpatient discharges (excl. newborns) year-to-date (7/1/08 to 10/31/08)?

– What will the IP average length of stay year-to-date (7/1/08 to 10/30/08)?

Results

• Predictability: All three markets had relatively high levels of accuracy to the final outcome.

• Usability: highly variable in terms of confidence with trading. 30% of participants actively traded. 35% of participants bought and held. 35% did not participate. More training needed.

• Fit with Culture: Information “out in the open” would concern many within more conservative managerial culture. The “game” aspect was hard to process for many participants.

Results (Question 1)

• Will total outpatient clinic visits year-to-date (7/1/08 to 10/31/08) exceed 605,000 visits?

• Starting Value: 50%

• Final Predicted Value: 25%

• Actual volume: 602,339

Results (Question 2)

• What will be the number of total inpatient discharges (excl. newborns) year-to-date (7/1/08 to 10/31/08)?

• Starting Value: 14,000

• Final Predicted Value: 14,050

• Actual volume: 14,259

Results (Question 3)

• What will the IP average length of stay year-to-date (7/1/08 to 10/30/08)?

• Starting Value: 8.3% (6.25-6.3)

• Final Predicted Value: 49.9%

• Actual LOS: 6.29

Pilot Decision

Interesting technology, but barriers to implementation:

• Lack of familiarity and perception

• Work load & change management

• Information management & decision making

Hold on implementation but look for future opportunities

Lessons Learned

• The “market” medium is one that is foreign to many people. Lack of familiarity leads to perceptions of inequity

• Building engagement requires intrinsic incentive (confidence that someone will do something with the information) as well as extrinsic incentive (prizes and rewards)

• Despite advantages of the tool, it has major organizational cultural implications which may affect its implementation

• Potential a viable alternative for decision making

Q & A

Tony Tsai (anttsai@umich.edu)Matthew Comstock (comstock@umich.edu)