Transcript Using Predictive Analytics in an ACO World

PREDICTIVE ANALYTICS IN AN ACO WORLD
OSF HEALTHCARE EXPERIENCE
OCTOBER 2014
Various Predictors
• Future resource utilization- DxCG, HCC
• Readmission
Resource Utilization
• DxCG, via Verisk, is used for identifying enrollees for Health
Management programs.
• Health Management is a consultative service provided by a team of
physicians, nurses, behavioral health specialist and coordinator.
Health Management Results
Interventions
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Self-management skills and motivation
Care gap closure- identified in Verisk
Provider coordination
Social and environmental assistance
Advance Care Planning
Reductions in ED visits and hospitalizations
Increase in office visits and pharmacy costs
Ambulatory Care Management
• Utilized similar risk scores
• Delivered rosters to PCPs to identify high risk and encourage
referrals
• Direct outreach to high risk score patients
• Some patients engage quickly, some are optimally treated, some do
not engage
• Risk scores are directionally sound and a good starting point
PREDICTIVE MODELING
OSF HEALTHCARE EXPERIENCE
30 Day Readmissions
OCTOBER 2014
Predictive Modeling
What does the model predict ?
How accurate it is ?
OR
And how do you know?
C-Statistics, AUC, PPV, MAPE, R2
OSF Healthcare Predictive modeling
Our Dilemma not about understanding the population risk
But about understanding WHO is at high risk
Applicable to
• Cost of care
• 30 day readmissions
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OSF Healthcare 30 Day Readmission Predictive Model
Model Description
• Built for the One OSF population
• Explored more than 140 potential
independent variables
• 70 variables included in final model
• Not reliant on vendor supplied risk
scores
• Uses data currently in the EDW
• Can be deployed via SQL
based reporting
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Project Review
10.67%
5,957 readmissions/
55,843 total discharges
2.8% Obs. Risk
696 readmissions/
24,595 total discharges
10.4% Obs. Risk
2,050 readmissions/
19,737 total discharges
23% Obs. Risk
1,383 readmissions/
6,002 total discharges
11% of Total Discharges
23% of Total Readmissions
44% of Total Discharges
12% of Total Readmissions
35% of Total Discharges
34% of Total Readmissions
18.3% of discharges account for
49.8% of Readmissions
30.9% Obs. Risk
1,029 readmissions/
3,334 total discharges
6% of Total Discharges
17% of Total Readmission
36.7% Obs. Risk
799 readmissions/
2,175 total discharges
4% of Total Discharges
13% of Total Readmissions
OSF Healthcare 30 Day Readmission Predictive Model
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The Care PROCESS
Future State Processes-High Level
1.
Report Auto distributed via email
The risk report is embedded into
current state processes
InPatient Case Manager/CTC
2. Readmission risks conversation
with “Key Learner” and physician
3. Document high level care planning
for ambulatory handoff in the
“Care Coordination Note”
4.
Complete Warm handoff
Ambulatory Care Management
5. Ambulatory Care Manager to
coordinate the mid/long term patient care
plan
Post Acute Care
6. Bridge acute & post acute care to work as
one team for patient continuum of care
PREDICTIVE MODELING
OSF HEALTHCARE EXPERIENCE
Cost of Care
OCTOBER 2014
OSF Healthcare Cost of Care Predictive Model
It is important to understand that the
underlying risk assessment is designed to
accurately explain the variation at the group
level, not at the individual level, because
risk adjustment is applied to large groups
(AAA, 2010). As the American Academy of
Actuaries notes:
“... Determining average experience for a
particular class of risk is not the same as
predicting the experience for an individual
risk in the class. It is both impossible and
unnecessary to predict expenditures for
individual risks. If the occurrence, timing,
and magnitude of an event were known in
advance, there would be no economic
uncertainty and therefore no reason for
insurance.” (AAA, 1980)
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OSF Healthcare Cost of Care Predictive Model
Model Description
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OSF Healthcare Cost of Care Predictive Model
Commercial models
Our model
Based on previously coded encounters
Built from the problem list
Typically validated on standard population Can be validated on individuals
Limited to coded data
Includes non-coded data from our EHR
No social data
Insight into social determinants
Data lag
Contemporaneous data
Limited to data from payers
Potential to risk adjust entire panel
Includes pharmacy
No insight into pharmacy costs
Includes data external to our EHR
Limited to internal EHR data
Easy
Difficult
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OSF Healthcare Cost of Care Predictive Model
Model
R^2
PPV 1%
MAPE
Medicare HCC
9.9%
11.5%
NA
Commercial #1
21.8%
23.3%
NA
Commercial #2
30.15%
NA
88.45%
OSF MODEL
36.15%
28.95%
NA
R^2 – The amount of cost variation in the population explained by the model
PPV 1% - Positive predictive value defined as the total number of those predicted to be in the top 1%
of cost actually in the observed future 1%
MAPE – Mean absolute percent error defined as the average error between expected and observed
(lower is better)
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OSF Healthcare Cost of Care Predictive Model
• Next Steps
– Validate outside Medicare and develop operational platform
No single model is perfect – need to compare and contrast the two views of the
patient and combine it with knowledge from the engaged Care team
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