Data driven models to minimize hospital readmissions
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Transcript of Data driven models to minimize hospital readmissions
© 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
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