One of health care’s biggest ideas is predictive analytics — looking at large amounts of data to predict future patient behavior or outcomes. Jeff Young, Associate Director in Decision Support, worked with a multi-disciplinary team to put predictive analytics into action. Here, he shares why innovation is nothing without the team.

1. OPERATIONAL PREDICTIVE
ANALYTICS:
YEAR 1 RESULTS

2. MEASUREABLE RESULTS:
Pharmacy Readmissions

3. PHARMACY READMISSION AVOIDANCE
Pharmacy group uses BOE report to
organize patients by risk
Pharmacist calls patients starting at
highest risk of readmission
Pharmacist consults medications with
patient, answers questions
Contact is recorded in Epic

4. READMISSION EFFECT OF STRATIFYING
PATIENTSPredicted Risk
Total # of
Discharges
Readmission
Rate
Number of Patients Needed to Call to
Contact One Readmission
Without Risk Score 8,270 10% 11
Low Risk 723 1% 91
Medium Risk 3,321 5% 19
High Risk 4,949 13% 8
• Predicted risk score creates a 27% decrease in needless calls by stratifying patients

5. 6.0%
7.0%
8.0%
9.0%
10.0%
11.0%
12.0%
2015 2016 2017
Overall Population
Readmission Rate
Contacted Patient
Readmission Rate
18.8% reduction in contacted
patient’s readmission rate
12.2% reduction in
population readmission rate
*Population consists of discharges where the patient has U of U provider listed as their current PCP
PHARMACY READMISSION IMPACT

6. PHARMACY READMISSION IMPACT
9.6%
7.8%
-1.10%
-0.68%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
2015 Rate Pharmacy Call
Effect
Predictive Score
Effect
2017 Rate
Attribution of Drop in Readmission Rate
• Mean cost of a readmission visit
$xx,xxx with a mean length of stay of
6.1 days
• An estimated 113 avoided
readmissions since May 2015 due to
Pharmacy Transition of Care
Program, saving $x.x million and
690 patient days
• Of those avoided readmissions, 27
are credited to use of predictive
score, saving an estimated $xxx,xxx
and 165 patient days

7. MEASUREABLE RESULTS:
No-Shows and Cancelled Visits

8. NO-SHOW PROJECT
Project: Predict probability of no-show or cancelled visit within 24 hours for
outpatient clinic patients
Technical Development: Decision Support, Biomedical Informatics, EDW
Operational Implementation: Decision Support, Template Optimization, Quality
Key Question: How can we use the probability that each patient won’t show up for
their appointment to maximize the efficiency of outpatient clinics without sacrificing
exceptional patient experience or creating new problems for front end staff?
Our Approach: Pilot use of the data with various groups to measure the impact
of various interventions, then disseminate finding to other groups

9. Improve the patient visit by
increasing efficiency around
patient flow, helping manage
anticipated volume
Improve patient access by
maximizing potential patients
seen each day
Optimize clinic flow by
providing data and feedback
around probability of no-
shows, allowing for intelligent
planning around anticipated
volumes throughout the day
PROJECT GOALS

10. REDWOOD PILOT WORKFLOW

11. REDWOOD CLINIC IMPACT
• 60% drop in likelihood of no show when contact made with patient
• No measureable impact shown from message being left
28% 28%
11%
0%
5%
10%
15%
20%
25%
30%
No Contact / No Message Message Left Contact Made With Patient
No Show Rates

12. FARMINGTON CLINIC IMPACT
• No show rate for non-contacted patients more than 3x that of patients
who were contacted
• Messages left show significant impact on likelihood of patient no show
35%
16%
11%
0%
5%
10%
15%
20%
25%
30%
35%
40%
No Contact / No Message Message Left Contact Made With Patient
No Show Rates

13. NO-SHOW EFFECT OF STRATIFYING
PATIENTS
Predicted Risk
Total # of
Visits
Redwood
No-Show
Rate
Number of Patients Needed to Call to
Contact One No-Show
Without Risk Score 1,500 16% 7
Low: 0-11% 392 8% 13
Medium Low: 12-24% 362 13% 8
Moderate: 25-49% 381 15% 7
High: >50% 365 27% 4
• Predicted risk score creates a 43% decrease in needless calls by stratifying patients