© 2013 Data driven models to minimize hospital readmissions Miriam Paramore, EVP Strategy & Product Management, Emdeon David Talby, VP Engineering, Atigeo
Dec 26, 2015
© 2013
Data driven models to minimize hospital readmissions
Miriam Paramore, EVP Strategy & Product Management, EmdeonDavid Talby, VP Engineering, Atigeo
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
2 million
$17.5 billion 19%
2,207
$280 million
276 hospitals
“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
Model Model’s Goal Sample size ContextCharlson morbidity index (1987) 1-year mortality 607 1 hospital in NYC,
April 1984Elixhauser morbidity index (1998)
Hospital charges, length of stay & in-hospital mortality 1,779,167 438 hospitals in CA,
1992LACE index(van Walraven et al., 2010)
30-day mortality or readmission 4,812 11 hospitals in
Ontario, 2002-2006LACE 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
• 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
• 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
LACE
New Model
0.5 0.55 0.6 0.65 0.7 0.75
C-Statistic over patients discharged for AMI, HF & PN
LACE
New Model
0 20 40 60 80 100 120 140
Number of features in model
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
• 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
• Yes, this is a big data problem• More data = Fundamentally better prediction• Models must be tailored• Models must continuously evolve
Key things to remember
Readmission Analysis Shows High Heart Failure Diagnoses
Identify High Risk Patients at Registration
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
Identify Risks in Prescription History
Follow High Risk Patients Post Discharge
Thank you!