1. © 2013 Data driven models to minimize hospital readmissions Miriam Paramore, EVP Strategy & Product Management, Emdeon David Talby, VP Engineering, Atigeo

2. Hospital Industry Subject to Hospital Readmission Penalties – Oct. 2012 “Medicare Revises Readmissions Penalties – Again,” Kaiser Health News, March 14, 2013, http://www.kaiserhealthnews.org/stories/2013/march/14/revised-readmissions-statistics-hospitals-medicare.aspx http://www.kaiserhealthnews.org/stories/2013/march/14/revised-readmissions-statistics-hospitals-medicare.aspx

3. “That may not sound like a lot, but for hospitals already struggling financially— especially those serving the poor—losing 1%-3% of their Medicare reimbursements could put them out of business.” Hospital Industry Subject to Hospital Readmission Penalties – Oct. 2012

4. ModelModel’s GoalSample sizeContext Charlson morbidity index (1987) 1-year mortality607 1 hospital in NYC, April 1984 Elixhauser morbidity index (1998) Hospital charges, length of stay & in-hospital mortality 1,779,167 438 hospitals in CA, 1992 LACE index (van Walraven et al., 2010) 30-day mortality or readmission 4,812 11 hospitals in Ontario, 2002-2006 LACE index + CMGs (van Walraven et al., 2012) 30-day mortality or readmission 100,000 All hospitals in Ontario, 2003-2009 Why are new readmissions predictive models necessary? Medical claims> 4.7 Billion Pharmacy claims> 1.2 Billion Providers> 500,000 Patients> 120 million Our dataset: Hospital, outpatient & physician visits Under a single master patient index Cross-US geographic coverage

5. Infrastructure requirements – Model based on the entire dataset – Model based on continuously updating data – Experiment with & combine multiple: Modeling techniques Feature combinations Ways to combine the datasets – Data quality as an integral and critical component Missing data, errors, fraud, outliers, flurries, … Yes, this is a big data problem

6. Tens of modeling & statistical techniques apply – Without over-fitting An ensemble approach applies – Combine multiple ‘weak’ models Automated feature engineering applies – Don’t assume features, “let the data speak” More data = Fundamentally better prediction

7. Models must be tailored Do not train on one hospital / geography / specialty / patient demographic and blindly apply to others Models must be tailored for each hospital location Do not assume which variables are most important to change

8. Locality (epidemics) Seasonality Changes in the hospital or population Impact of deploying the system Combination of all of the above Automated feedback loop & retrain pipeline is a must Models must continuously evolve

9. Yes, this is a big data problem More data = Fundamentally better prediction Models must be tailored Models must continuously evolve Key things to remember

10. Readmission Analysis Shows High Heart Failure Diagnoses

11. Identify High Risk Patients at Registration

12. Identify High Risk Patients at Registration: Case 1 24 Months 192 treatments at 12 different locations 8 outpatient visits in 2 separate facilities 130 outpatient diagnostic or clinic visits in 14 different facilities Most clinical care is rendered by a PCP internal medicine practice over 92 visits

13. Identify Risks in Prescription History

14. Follow High Risk Patients Post Discharge

15. Thank you!