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

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

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

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

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

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

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”

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)

Model Performance Version 1

7 South 11 South using 7s weights 11 South re-weighted

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