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Learning how big data can inform analytical processes and clinical decision making.
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Using Big Data and Predictive Analytics in Healthcare Chris Stout, PhD Vice President Clinical of Research and Data Analytics - ATI and The College of Medicine, University of Illinois at Chicago
Who is in the audience today…? PT students…? PT faculty…? Biomedical Sciences students….? Biomedical Sciences faculty…? Anyone else….? Folks that just like to raise their hands…?
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.
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
>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…
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
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!
Surgeon’s Perspective on a Good Outcome • No anesthesia issues • No surprises during or after • No complications • Good wound healing • No post-op infection
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?
PRN Tx Guideline PRN Tx Guideline Consult ( Consult (brought to you brought to you by your EMR) ) by your EMR
I was always frustrated with the disconnect of getting evidence-based practice in real-time to the clinician while with the patient
And we may have cracked the code
Evidence-based practice is sort of like MoneyBall
No longer a problem of too little data… …but too much
OK, So, now WHAT?
It’s about tools…
Collaborative Opportunities 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 37
Registry in Differentiation and Exposure 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 38
We are in the midst of some wonderfully revolutionalry and promising changes afoot…
Please be in touch Chris.Stout@ATIPT.com or visit DrChrisStout.com for these slides