Date post: | 22-Jan-2018 |
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MEDICAL
OFFICE
Len Usvyat, PhD
Vice President, Integrated Care Analytics
Dugan Maddux, MD
Vice President of Kidney Disease Initiatives
Improving Predictive Model Adoption in a Clinical Health Care Environment
• Overview of “the Kidney”
• End Stage Renal Disease in the U.S.
• About Fresenius Medical Care
• Our Approach to Predictive Modeling
• Real-life Examples of Predictive Modeling
• Looking Beyond
Agenda
2
Overview of a Kidney
Reference: www.umich.edu
3
Stages of CKD Based on Kidney Function
Glomerular Filtration Rate = GFR (in ml/min/1.73m2)
Late Stage CKD
5
• More than 700,000* people in the U.S. have a diagnosis of End Stage Renal Disease (ESRD)
• ESRD patients typically have multiple diseases that contribute to kidney failure
• Routine treatment with dialysis therapies or kidney transplantation are the key options for ESRD patients and are required to sustain life
ESRD in the U.S.
*Per United States Renal Data Systems (USRDS) Prevalence of ESRD was 703,653 in Q2 of 2015
http://www.usrds.org/qtr/default.aspx 6
Hemodialysis is the primary modality for treatment of ESRD and includes routine treatments to filter the body’s toxins from the blood
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Overview of Hemodialysis
Reference:
http://openwetware.org/wiki/Dialysis,_by_Kyle_Reed
• Fresenius Medical Care North America (FMCNA) is the leading provider of dialysis the U.S.
• We have the largest collection of clinical data on dialysis patients, treatments, and outcomes, in the world
• We leverage our data to provide a predictive insight to our staff
Our Company
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Fresenius Medical Care North America by the Numbers
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What Do U.S. Hemodialysis Patients Look Like?
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Wealth of Dialysis Patient Data
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Predictive Analytics Process
Assessment of
modeling demand
Model prioritization through
Predictive Analytics Steering
Committee (PASC)
Creation of predictive
models
Piloting and testing of
predictive models
14
Predictive Analytics Steering Committee
Members • Frank Maddux
• Peter Kotanko
• Terry Ketchersid
• Scott Ash
• Dugan Maddux
• Yue Jiao
• Hanjie Zhang
• Len Usvyat
Members
• Medical Staff
• Statisticians
• Data Scientists
• Business leads
Frequency
• Bi-Monthly
How it works
15
Suite of Operationalized Predictive Models
Predicting
Patient
Functional
Status
Predicting
Chronic
Kidney
Disease
Progression
Predicting
Patients who
Drop
Commercial
Insurance
Predicting
Patients who
Stop Using
FMCRx
Predicting
Vascular
Access
Failure
Predicting
Hospitalizatio
n Risk
Predicting
Who and
When a
Patient Will
Miss an
Expected
Treatment
Predicting
Patients who
will Leave a
Home
Dialysis
Modality
Predicting
Comorbid
Clinical
Conditions
Predicting
FMCNA
Clinical Staff
Retention
MODEL DESCRIPTION
• Goal: To predict dialysis patients who will have >=6 (“high risk patients”), 4-5, 2-3, 0-1 hospital admissions per year
• 300+ predictors
• Gradient boosting model
• Area under the curve (AUC) 0.80-0.90
Hospitalization Risk Stratification
16
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Overview of the DHR Process
The “Library” provides common interventions and quick access to resources and tools related to these “Tags”.
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Top 10 “Tags”: Areas of Focus for the Interdisciplinary Team Interventions
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Predicting High Risk Patients: Most Common Tags
December 2015 data
Malnutrition Fluid
Overload
Non-
Adherence
Psychosocial
Anemia Vascular
Access
BP
Instability Glycemic
Control
Active
Ulcer
Blood
Stream
Infection
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DHR Phase Outcomes (n= 299 patients)
7.3
45.3
7.6
3.56
29
5
29.8
7.2
3.6
25
0
5
10
15
20
25
30
35
40
45
50
Hospital admits Hospital days missed txts albumin catheters
before enrollment After enrollment
6%
14%
31%
34%
1%
December 2015 data
Annualized 3 mos before & 3 mos after
MODEL DESCRIPTION
Goal: To predict which patients will have an unexcused absence from dialysis treatment in the following week
60 predictors
Area under the curve (AUC) ~0.87
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Predicting Unexcused No Show
Comorbidities
Dialysis Shift & Changes
Weather in prior week
Method of transportation
Time to drive to clinic
Changes in clinical
parameters
Clinic retention
and clinic size
Intradialytic events in
prior week
Events:
birthday, holiday, sports
Lifestyle
Season
History of no show
Demographics
• Care Navigation Unit (CNU) currently uses the Unexcused No Show Prediction Dashboard to manage patients who are not likely to show up for a treatment in the following week
• The team captures intervention data on the dashboard
Predicting Unexcused No Show
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Two new columns in the CNU Worklist:
No Show Risk
Hospitalization Risk
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CNU Worklist
Predictive model automation
Hospitalization risk model
API ability to use predictive modeling results
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No Show Dashboard
Broad Pervasive Data Sources Automating Model Updates
and Performance Analysis
Delivering Person-
Specific Predictions
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Automating FMCNA Predictive Analytics
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• Fresenius has a wealth of data related to CKD and ESRD patients
• Multiple efforts are under way to identify patients who need extra attention
• We focus on making these efforts provide useful and insightful information for our clinicians
• This cannot be done in a vacuum (support needed from clinical, business and IT teams)
• Success is iterative: we learn and improve analytics over time
27
Conclusion