chief analytics officer fall usa 2017 - dugan maddux & len usyvat

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

Reference:

www.doccartoon.blogspot.com

4

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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9

10

Fresenius Medical Care North America by the Numbers

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What Do U.S. Hemodialysis Patients Look Like?

12

Wealth of Dialysis Patient Data

13

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

17

Overview of the DHR Process

The “Library” provides common interventions and quick access to resources and tools related to these “Tags”.

18

Top 10 “Tags”: Areas of Focus for the Interdisciplinary Team Interventions

19

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

20

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

21

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

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Conclusion