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Chief Analytics Officer Fall USA 2017 - Jonathan Bickel
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Transcript of Chief Analytics Officer Fall USA 2017 - Jonathan Bickel
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A Predictive Modeling
Approach to ICU Admissions
At Boston Children’s Hospital
Jonathan Bickel M.D., M.S., FAAP,
Senior Director of Business Intelligence,
Boston Children’s Hospital
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Boston Children's Hospital
• 404-bed comprehensive center for
pediatric health care
• #1 ranked children’s hospital by U.S. News &
World Report last 4 year in a row!
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Boston Children’s Hospital
• 25,000 inpatient admissions each year
• 557,000 outpatient visits scheduled
annually
• 200+ specialized clinical programs
• 26,500 surgical procedures performed in
2016
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Boston Children’s Hospital
Intensive Care Units
• Medical ICU
• Medical/Surgical ICU
• Neonatal ICU
• Cardiac ICU
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Boston Children’s Hospital
Intensive Care Units
• Typical Patient Ailments
– Respiratory Issues
– Neurological Issues
– Anatomical anomaly surgical repair
– Trauma recovery
– Severe medical illness
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Boston Children’s Hospital
Intensive Care Units
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Boston Children’s Hospital
Intensive Care Units
• Admit sources
– Emergency Department
– Direct admits
– Surgical recovery
– Transfers from regular inpatient units
– Transfers from other facilities
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ICU
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Boston Children’s Hospital
Intensive Care Units
• Dispositions
– To non-intensive-care inpatient bed
– Home
– Transfer to rehabilitation facility
– Transfer to another hospital
– (fortunately, very rarely) Death
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ICU
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Intensive Care Unit
Capacity Issue
• Finite number of beds
• High demand for beds
• Frequent turnover of most patients
• Varying number of long-stay patients
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ICU
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Intensive Care Unit
Capacity Issue
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Intensive Care Unit
Capacity Issue
• Long-stay patients have disproportionate
effect on capacity
– Most ICU patients occupy an ICU bed for a
few days
– Some ICU patients occupy an ICU bed for a
few weeks
– Very severely ill patients sometimes occupy
an ICU bed for several months or over a year
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Intensive Care Unit
Capacity Issue
• Because there are not that many ICU
beds, relatively few long stay patients can
significantly reduce the capacity to
accommodate all other patients.
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ICU ICU
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Intensive Care Unit
Capacity Issue
• Upstream effects: If no ICU bed, backups
– Emergency Dept.
– Post-surgery
– worsening regular floor patients
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Intensive Care Unit
Capacity Issue
• Downstream effects: ICU backups due to
bottlenecks in post-ICU facilities
– Shortages of regular care beds (regular
hospital beds full)
– Shortages of skilled rehab facility beds
– Need to plan for specialized transportation
such as international medical flights well in
advance
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Capacity Control
Options and Limitations
• Elective admissions are predictable and
controllable
• ICU Transfers are controllable
• Non-elective admissions must be dealt
with as they arise
• Need enough capacity to handle the
unexpected, but can’t keep beds open
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ICU
Keep discharges flowing
Monitor for ”clogging”
Change Inputs if needed
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The solution
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Predictive Analytics to
Help Optimize Management
• Want to predict upcoming capacity
constraints based on known current
conditions.
• Predict:
– Far enough out to take effective action
– Near enough for good reliability
• The farther out the data is extrapolated, the less
reliable are the predictions based on that data
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Modeling Approach
• Outcome variable selection
– Explore 1,2,3,8, and 21 day predictions that a
current patient will still occupy an ICU bed
– Decided 8 additional days stay prediction
would be most likely to optimize adequate
lead time for action with reasonable prediction
accuracy
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Modeling Approach
• Choice of model
– Considered
• Survival analysis
• Decision trees
• Logistic regression
– Logistic regression worked best with the
candidate variables
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Model
• Daily logistic regression based predictions
of likelihood of patients still being in
Med/Surg ICU 8 days later
• Trained on all patient stays in Med/Surg
ICU in calendar year 2016
• Validated on patient stays in Med/Surg
ICU in Jan. thru present 2017
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What’s in the Model?
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“Protoplasm”
Daily
Measures
Based on todays inputs, what is the % chance a child will be in the ICU 8 days from
today?
7S 11S
• Diagnoses • Region • Age
• Medications • ECMO / Intubation • “Discharge planning”
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Modeling Tools
• Extracted data from our Enterprise Data
Warehouse using MicroStrategy
• Created models using the R open source
statistical modeling language
• Presented results in MicroStrategy using
MicroStrategy/R integration package
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V1 Model
design &
development
(7S)
V1 Model
Portability to
other ICU’s
(11S)
V1 Model
development
reweighting
for new unit
(11S)
Feedback
from V1
Model Design;
Gathering of
new variables
V2 Model
Development
(7S)
• Labs • Medications • I & O’s • Diagnoses for
specific units
V2 Model
portability to
other ICU’s
(11S)
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Model Performance Version 1
7 South 11 South using 7s weights 11 South re-weighted
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Version 2
• + meds, I/O’s, labs, & diagnosis groupings
specifically for 11S separate from 7S
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With all labs and diagnoses
except albumin removed:
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Daily Usage
• Med/Surge ICU clinician receives
automatically generated predictions via
email each morning for patients in unit as
of midnight census.
• Free to use predictions as a supplement to
clinical judgment.
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Daily Usage