This document discusses how predictive analytics can be used to predict hospital readmissions. It begins with defining predictive analytics as techniques that use data to find models that can anticipate outcomes accurately. An example is given of how predictive direct marketing increased response rates. The document then defines patient readmission and some common causes. It notes the importance of reducing readmissions due to CMS penalties for hospitals. The final sections discuss how UPMC-Pinnacle used predictive analytics to successfully reduce COPD readmissions by 30% and lower costs. In summary, predictive analytics can help hospitals anticipate readmissions to improve patient outcomes and reduce penalties.
2. Predictive Analytics
● Simple Definition
○ Techniques, tools, and technologies that use data to find models—
models that can anticipated outcomes with a significant probability of
accuracy
3. Example of Predictive Analytics
Direct Marketing- 1% Response Rate
Send marketing
mail to
1,000,000
Customers at
cost of $2 per
mailing to see a
$220 service
$2 X 1,000,000 $2,000,000
1% response
rate means
10,000
customer will
buy service
$220 x 10,000 $2,200,000
Profit $200,000
Predictive Direct Marketing- 3% Response Rate
Send marketing
mail to 250,000
customers
predicted most
likely to buy at
cost of $2 per
mailing to sell a
$220 service
$2 X 250,000 $500,000
3% response rate
means 7,500
customer will
buy service
$220 x 7,500 $1,650,000
Profit $1,150,000
4. Predictive Analytics for Readmission
1. Definition of Patient Readmission-
○ Patient is admitted to the hospital for same or similar condition within a prescribed period
of time.
2. Readmissions often result from
○ Problems inadequately resolved the first time around
○ Hospital acquired infection
○ Inadequate patient management
○ Discharge instructions misunderstood
○ Lack of patient access to needed services or medications
5. Importance of knowing this?
1. The Center for Medicare and
Medicaid Services-
○ Nearly one in five fee-for-service
Medicare patients returns to the
hospital within 30 days of being
discharged.
○ High readmission rate can be an
indicator of poor quality care.
○ Readmissions estimate to cost medicare
$26 billion per-year, $17billion of
which is potentially avoidable.
○ CMS started to impose penalties.
7. Analytics improves outcomes for
UPMC-Pinnacle
● Goal- to reduce readmissions for patients with chronic health conditions by integrating
analytics into clinical workflow
● Solution- UPMC-PINNACLE and Waypoint Consulting used IBM Analytics to build a
predictive model that evaluates the risk of readmission for patients with chronic obstructive
pulmonary disease (COPD).
● Result- UPMC-Pinnacle is predicting COPD readmissions with 85% accuracy, enhancing
patient outcomes and lowering costs. Reducing readmissions by 30 percent which results in an
estimated 200$k in annual savings.
8. Analytics for patient readmission
● Efficiency is important
● CMS penalty
● “The best predictor of future
behavior is past behavior”
9. Thank you for Watching :
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● Abdelali El Bar
