1. Case Studies 1

2. 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

3. Patient volume 3 Algorithms Outpatient Clinics Emergenc y Dept. Physician Office Activity Day of the week Length of stay Nurse unit Predicted daily census by nurses station

4. Customer segmentation 4

5. Demand by customer segment Demand Landscape: The height represents potential demand; the areas represent ZIP code areas.

6. Demand by customer Segment Service 1, White, Youth 2015 High Demand Medium Demand Low Demand Facility Service 2, African American, Male, 45-65 2015 Service 3, White, Female 2015

7. 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’

8. 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)