Case Studies

1

Patient volume

Purpose: Predict patient volume, understand drivers of volume

Approach: model sources of admissions (sequence and survival analysis) and discharges

Results: • Aggregate forecast was better than their baseline forecast• More insight into service line forecasts, variation over time• Patient volume was predicted to day and nurses station• Created the ability to do ‘what-if’ analysis

2

Patient volume

3

Algorithms

Outpatient Clinics

Emergency Dept.

Physician Office

Activity

Day of the

week

Length of stay

Nurse unit

Predicted daily census by nurses station

Customer segmentation

4

Demand by customer segment

Demand Landscape: The height represents potential demand; the areas represent ZIP code areas.

Demand by customer Segment

Service 1, White, Youth 2015

High Demand Medium Demand Low DemandFacility

Service 2, African American, Male, 45-65 2015

Service 3, White, Female 2015

Chart Review

Purpose: Identify a less costly, more efficient and effective way to obtain information from physician notes.

Approach: competition between text mining and two teams of professionals

Results:• Text mining was as good as or better than the professional teams for

– Assigning state of patient into taxonomy provided for the diagnosis– Assigning ‘positive’, negative’ or ‘neutral’ assessment of patient compared to previous visit

and from first encounter assessment• Text mining identified valuable information not sought after but is valuable

– documented observations of health change not associated with the diagnosis• Text mining is not successful when physician notes are lacking

– Text mining was used to predict physician assigned scales of specific observation ‘measures’

Device failure

Purpose: Anticipate and understand device failures using technician notes

Approach: Text mining, categorization, root cause analysis, early warning

Results: • More efficient and effective corrective action

– Design, engineering, vendor selection, packaging, labeling and customer education

• Early warning system, producing alerts when failure rates exceed previous (similar product) experienced component failure rates.

• Predicted future warranty work from identified rates, installed base of product, implemented corrective actions (to mitigate historical failure rates)