Data driven models to minimize hospital readmissions

1. 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 2 million $280 million $17.5 billion 19% 2,207 276 hospitals “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

3. Hospital Industry Subject to Hospital Readmission Penalties – Oct. 2012 “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.”

4. Why are new readmissions predictive models necessary? 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. 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 Models must be tailored

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