Presented at Predictive Analytics Innovation Summit, Chicago 2017 #PAChicago https://theinnovationenterprise.com/summits/predictive-analytics-innovation-summit-chicago-2017/speakers This presentation centers on currently published findings focused on the use of predictive analytics in healthcare venues of sports medicine and orthopedic rehabilitative settings. Aspects of data access via national patient registries as well as nascent applications of machine learning will also be covered. An example of one approach of incorporating a model of assessment, evidence-based practice, treatment augmentation, and resultant outcome evaluation will be provided as well. Please be in touch http://DrChrisStout.com

1. Chris Stout, PhD
VP, Department of Research and Data Analytics, ATI
College of Medicine, University of Illinois, Chicago
Chris Stout, PhD
VP, Department of Research and Data Analytics ATI
Holdings, and
The College of Medicine,
University of Illinois at Chicago
Harnessing Big Data
and
Analytics in Healthcare

2. Who is in the audience today…?
CIO…?
CTO…?
CISO….?
Founder…?
Investor/PE/VC….?
Anyone else….?
Folks that just like to raise their hands…?

3. Machine Learning and the Profession of Medicine
Feb 2016

4. Machine Learning… not so fast…

5. Precision Medicine Initiative®

6. There’s an app for that… and how!
Roughly 200 new apps added
every day.
Those apps are increasingly being
used in clinical trials.
Right now, there are 860 trials
underway across the globe testing
health apps for clinical use.
If the evidence supporting some of
those apps pans out — e.g., if
they’re able to reduce ER visits or
improve medication adherence —
they could help cut health care
costs in the future.

7. There’s an app for that… and how!

8. There’s an app for that… and how!

9. There’s an app for that… and how!

10. Apple HealthKit
In 14 of 23 major hospitals are trialing
Google, Phillips, Samsung, IBM, and
others are getting deeper into health-
based technology applications
Healthcare + fitness apps =
comprehensive picture
Send to MD or case manager

11. Harnessing diverse data, and a lot of it

14. Predictive Analytics

15. Predictive Analytics

16. >15,000 prior-managed bills were loaded and rerun
against the ODG Treatment UR Advisor for each ICD9-
CPT combination on frequency, number of visits,
recommendations from ODG Treatment, and the "Bill
Review Payment (or ODG Approval) Flags" divided
into Green, Yellow, Red…

17. Green, OK to auto-pay up to ODG Codes for
Automated Approval max number of visits;
Yellow, OK to auto-pay up to 25th %tile
number of visits
Red, need to review

20. It’s nice to work with workers’ comp
outcomes because…
Outcomes are VERY Quantified
– RTW at the same job description and PDL
or not?
– How many days passed before RTW?
– Nice, clean, and tidy!

21. Surgeon’s Perspective on a
Good Outcome
• No anesthesia issues
• No surprises during or after
• No complications
• Good wound healing
• No post-op infection

22. But how does the story end?
Is the patient back at work?
Quickly?
At the same PDL as prior to injury?
With the same job classification?

24. PRN Tx Guideline
Consult (brought to you
by your EMR)

25. I was always frustrated with the
disconnect of getting evidence-based
practice in real-time to the clinician
while with the patient

26. So I did some experimenting….

27. And we may have
cracked the code

28. Combing Evidence with Practical Application, in Real-time

29. Evidence-based practice is sort of
like MoneyBall

30. Evidence-based practice is not…

31. Evidence-based practice is not…

32. No longer a problem of too
little data…
…but too much

33. OK,
So,
now
WHAT?

34. It’s about
tools…

35. Registries to the Rescue!

36. ATI Outcomes Registry
36

37. Collaborative Opportunities
37
Example Projects
Analyzing individual patient demographic,
baseline and outcomes characteristics to
predict risk for developing chronic
musculoskeletal pain
Analyzing clinic and clinician, patient
demographics, baseline and outcomes
characteristics to predict likelihoods for
appointment cancellation

38. Registry in Differentiation and Exposure
38
ATI possesses > 2 million unique cases of clinical outcomes
Solid profile describing clinical-operational results
Building towards differentiation
Creating National Benchmarks for comparison purposes
Developing models to predict and profile best care behaviors
Using data to target improvements to differentiation
Partnering with world class research institutes to innovate, improve patient
management and clinical outcomes

40. We are in the midst of some
wonderfully revolutionalry and
promising changes afoot…

43. Please be in touch
Chris.Stout@ATIPT.com
or visit DrChrisStout.com
for these slides