1. Making the Most
of Predictive Analytics
Presented by JJ Schmidt, Senior Vice President

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2

3. Making the Most
of Predictive Analytics
Presented by JJ Schmidt, Senior Vice President

4. PREDICTIVE ANALYTICS | AGENDA
What we will cover
– Definitions
– How predictive analytics work
– Analytics
– What can analytics do
– Why predictive analytics is an important topic
– Key considerations
– The three A’s of analytics
– Case study
– Practical implications
– Key takeaways
– Questions
4

5. Not everything that can be
counted counts, and not
everything that counts can be
counted.
‐ Albert Einstein
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PREDICTIVE ANALYTICS

6. Definitions
– Predictive analytics
– Predictive modeling
– Data mining
– Big data
– Data warehouse
– Algorithms
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PREDICTIVE ANALYTICS | DEFINITIONS

7. Analytics in everyday life:
– Soccer moms
– NASCAR dads
– Amazon, Google, Online/Catalog Retailers, Banks & Credit
Cards
– What might be some typical types of claimants or
accidents/injuries that we “know” will be those claims that will
be bad
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PREDICTIVE ANALYTICS | ANALYTICS IN EVERYDAY LIFE

8. How do predictive analytics work?
– Use your claim and managed care data
– Create specific algorithms
– Review data looking for patterns, issues or targets
– Gain insights on claims and patient populations
– Influence claim progression by alerting to the use of
specific actions
– Change or improve clinical pathways for injured
workers
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PREDICTIVE ANALYTICS | HOW DO PREDICTIVE ANALYTICS WORK?

9. Analytics
– Analytics can be transformative
– “Past performance is not necessarily indicative of future
results”
– Analytics in everyday life
• Netflix, Amazon, Facebook, Google
– Data is everywhere
• Transactions
• Spending
– Sometimes analytics are not the answer
• Timing is an issue? Before you have all of the information?
• A decision maker can add unique perspective – clinical
issues
9
PREDICTIVE ANALYTICS | ANALYTICS

10. What can predictive analytics do?
– Improve decision making
– Predict or anticipate changes
– Manage risk
– Reduce claim costs
– Reduce claim durations
– Improve operational efficiency and effectiveness
– Help resources to work together
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PREDICTIVE ANALYTICS | WHAT CAN PREDICTIVE ANALYTICS DO?

11. Why are predictive analytics important?
– Understand claims and improve outcomes
• Analytics can help to better understand the dynamics of a
claim at a specific point in time
• Analytics can help to improve claim performance metrics
such as costs or durations
• Analytics can help to better understand what processes
are, and also are not, working on claim administration
• Analytics can help to improve the efficacy of specific claim
related services such as case management, peer review
or utilization review
• Analytics can allow for claim professionals to individualize
claim strategies to distinguish between injured workers
who will or will not benefit from specific treatment plans
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PREDICTIVE ANALYTICS | WHY ARE PREDICTIVE ANALYTICS IMPORTANT?

12. Key considerations
– Tie analytics to specific objective targets
• Improved claim durations
• Lower claim or medical costs
• Improved return to work
– Leverage existing information technology and data
• Better insight into claim current and future status
• Earlier or faster execution of specific activities
• Increased and enhanced value of information
technology investments
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PREDICTIVE ANALYTICS | KEY CONSIDERATIONS

13. Key considerations
– Make the program adaptive
• Learn from the results – positive and negative
– Create new insights
– Look for changes
• Learn from the process – what works and what does not add
value
– Feedback from staff
– How does this fit into strategy and objectives?
• Focus on the specific metrics and indicators that impact
results
• Review and examine the program continuously
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PREDICTIVE ANALYTICS | KEY CONSIDERATIONS CONTINUED

14. • Key considerations
– Data
• Structure
• Unique
• Integration
• Quality
• Access
• Privacy
• Governance
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PREDICTIVE ANALYTICS | KEY CONSIDERATIONS CONTINUED

15. Key considerations
– Challenges
• Systems and information technology
• Data – quality and input
• Organizational and customer acceptance
• Process consistency
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PREDICTIVE ANALYTICS | KEY CONSIDERATIONS CONTINUED

16. The three A’s of analytics
– Alert
• Notification
• Something has happened
• Something is happening
• Something could happen based on prior experience
• Who does it go to as an alert?
• What does it tell them?
• What is the benefit?
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PREDICTIVE ANALYTICS | THE THREE A’S OF ANALYTICS

17. The three A’s of analytics
– Action
• Process
• How do I review the information?
• What do I need to do now?
• What is my timeframe?
• Who do I need to involve?
• Feedback
• Process improvement
• Alert evaluation
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PREDICTIVE ANALYTICS | THE THREE A’S OF ANALYTICS

18. The three A’s of analytics
– Accountable
• Consistency and quality
• What is expected?
• What if I do not follow guidelines?
• Who monitors what I do – or do not do?
• Continuous improvement
• Program feedback
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PREDICTIVE ANALYTICS | THE THREE A’S OF ANALYTICS

19. • Case Study – Predictive Analytics
– Start with regression analysis
– Identify common factors on outlier claims
– Develop data plan
– Develop algorithms
– Results
• Two year study focusing on impacts to back claims
• Statistically significant reductions in
– Total average claim costs
– Total average medical costs
– Number of disability days
– Number of days claim open to close
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PREDICTIVE ANALYTICS | CASE STUDY

20. Practical Uses
– Pharmacy
• Physician dispensing
• Medication uses or combinations
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PREDICTIVE ANALYTICS | PRACTICAL USES

21. • Key takeaways
– Make decisions
• More accurately
• More consistently
• More timely
– Data is an asset – we collect it and we need to use it
– Data can be a tool to guide and assist in decision making
• Past
– What happened?
– Why and how did it happen?
• Present
– What is happening now?
– What should we do next?
• Future
– What has the potential to happen?
– What are the best or worst outcomes?
– How do we plan?
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PREDICTIVE ANALYTICS | KEY TAKEAWAYS

22. • Resources for additional knowledge
– Books
• Big Data @ Work – Thomas Davenport
• Keeping Up with the Quants – Thomas Davenport
• Data Science for Business – Foster Provost & Tom Fawcett
– Articles
• “Big Data for Skeptics” by Adi Ignatius. Harvard Business
Review
• “A Predictive Analytics Primer” by Thomas Davenport.
Harvard Business Review
• “Big Data in Healthcare” Health Affairs July 2014
• “A Decision Support Tool for Predicting Patients at Risk of
Readmission” Decision Sciences October 2014
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PREDICTIVE ANALYTICS | RESOURCES

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PREDICTIVE ANALYTICS | QUESTIONS
jj.schmidt@wellcomp.com
(954) 665-7140

24. Join Us for our Next Webinars
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