data @ dexcom: cgm technology, iot connectivity, and the...

Data @ Dexcom:

CGM technology, IoT connectivity, and

the drive to insight delivery in diabetes care

Nate Heintzman, PhD

Sr. Manager, Data Partnerships

20161130

TYPE 220251980 2014

Diabetes is a global crisis

PREVALENCE

OF

DIABETES

0

700

400

MIL

LIO

NS

108MADULTS

GLOBALLY

422MADULTS

GLOBALLY

1 IN 3

ADULTSIN US WILL HAVE

DIABETES BY

2050

Source: NCD Risk Factor Collaboration, Published April 2016; CDC

WORLDWIDE DIRECT

ANNUAL COST

Source: International Diabetes Federation Diabetes Atlas 7th Edition, 2015

Germany

$35BILLION

China

$51BILLION

United

States

$320BILLION

Japan

$29BILLION

$673B - $1.2T

Diabetes is a global crisis

Are her lifestyle

changes having an

impact?

Is her $400/month

medication working

for her?

Will she need to start

taking insulin

injections soon?

Is it safe for him

to go to soccer

practice?

Should he take a

dose of insulin now?

How much?

How will his glucose

change while he is

sleeping?

Patients and their care teams live with

uncertainty about their diabetes, every day

Continuous Glucose Monitoring (CGM)We now have a whole new understanding of glucose levels

BEFORE CGM WITH CGM

Continuous

flow of data

Holistic

understanding

of time in range

Better decision-

making to increase

time in range

Patient can share

data with family

members and

clinicians

2:15 PM 2:45 PM

3:45 PM

Dexcom CGMDelivering a key vital sign to the smartphone and beyond

OCTOBER 2014

First remote mobile

device for CGM that

can transmit via app

to five “Followers”

APRIL 2015

First apps to

enable CGM

on Apple Watch

AUGUST 2015

First fully mobile

CGM that sends data

directly to smartphone

without receiver

AUGUST 2015

Software that delivers

personalized, easy to

understand analysis of

glucose trends

The status quo was to isolate the data

Care Team

Family

Medical Record

ResearchINACCESSIBLE

DATA ISOLATED

TO DEVICE

DATA ISOLATED

TO SPECIFIC

COMPUTER

Payor

INTERNET

of

THINGS

CLOUD

COMPUTINGBIG

DATA

DATA

SCIENCE

MACHINE

LEARNING

MICRO

ELECTRONICS

PLATFORM

Dexcom has invested in a different approach

Securing to

industry/regulatory

standards

Protecting patient’s

right to determine how

their data is used

Architecting with

scalability, resilience, and

multi-tenancy to support

extended ecosystem

Security Privacy AdaptabilityEasing integration by

building with industry

standards

Integration

Regulatory / Quality Compliance

Dexcom platform core requirements

Adopting best-in-class big data technologiesSolution Overview Qualities

HadoopEcosystem of tools that enable distributed processing and analytics of large disparate data sets across multiple systems

• Highly redundant• Fault tolerant • Large storage capacity (handles large datasets)• Scalable

Cassandra Distributed data store optimized for availability and high write throughput. Commonly used to back end API and store/process time series data

• Highly redundant• Fault tolerant • Low latency • Linearly Scalable

SqoopEnables integration between Hadoop and relational data sources

• Enables new technologies to leverage pre-existing investments in relational data systems

KafkaDistributed, partitioned, replicated commit log service that provides queuing system functionality.

• Highly redundant• Fault tolerant • ‘Wicked fast’• Linearly Scalable

SparkFast and general-purpose cluster computing system providing high-level APIs and also supports a rich set of higher-level tools.

• Can use it interactively with Scala, Python, and R• Can combine the power of SQL, Machine Learning,

and Streaming

Python/RLanguages used by data scientists to build and execute models and calculations

These tools can be used in conjunction with Hadoop,Cassandra, Spark, or standalone to build data products

Data Platform

What can the Dexcom platform do for you?

APIs

Data Platform


APIs

Data Platform


Standard API endpoints:EGVs (unit, rateUnit, egvs, systemTime, displayTime, value, status, trend, trendRate)devices (model, lastUploadDate, alertSettings, alertName, value, unit, snooze, delay, enabled, systemTime, displayTime)calibrations (systemTime, displayTime, value, unit)events (systemTime, displayTime, eventType, eventSubType, value, unit)statistics (estimatedA1C, hypoglycemiaRisk, min, max, mean, median, variance, stdDev, sum, q1, q2, q3, utilizationPercentage, meanDailyCalibrations, nDays, nValues, nBelowRange, nWithinRange, nAboveRange, percentageBelowRange, percentageWithinRange, percentageAboveRange)

APIs

Data Platform


Standard API endpoints:EGVs (unit, rateUnit, egvs, systemTime, displayTime, value, status, trend, trendRate)devices (model, lastUploadDate, alertSettings, alertName, value, unit, snooze, delay, enabled, systemTime, displayTime)calibrations (systemTime, displayTime, value, unit)events (systemTime, displayTime, eventType, eventSubType, value, unit)statistics (estimatedA1C, hypoglycemiaRisk, min, max, mean, median, variance, stdDev, sum, q1, q2, q3, utilizationPercentage, meanDailyCalibrations, nDays, nValues, nBelowRange, nWithinRange, nAboveRange, percentageBelowRange, percentageWithinRange, percentageAboveRange)

3rd-party

app

3rd-party

software

Treatment

Data

Geolocation

DataRisk

Data

Cost

Data

Data

Science

Device

Integrations

Enabling integration, insight, and innovation…

Give high-risk patients

additional support

Alert family of danger

and locate patient

Evaluate treatment and

intervention efficacy

Determine impact of

glycemic control on

cost of care

Identify patterns to

inform new treatments

Closed-loop technology for

automated insulin delivery

…and empowering everyone involved to

improve outcomes and reduce costs

CLINICIANS

PATIENTS

POPULATIONS

PAYORS

Navigate daily self-

management decisions

with feedback loops for

personal understanding of

glucose response

Identify patients who need

more attention and develop

individualized precision

treatment plans

Deeply understand

efficacy of treatments to

target care more efficiently

and cost-effectively

Gain insights on which

approaches have driven

positive impacts for

different kinds of patients,

and why

Economics

of sharing

data

Regulation

Privacy &

Security

Cross

pollination

Industry

status quo

Which data

returns true

value?

Getting there is not simple

Driving together to the future of diabetes care

EHR

Payors

Data @ Dexcom:

CGM technology, IoT connectivity, and

the drive to insight delivery in diabetes care

Nate Heintzman, PhD

Sr. Manager, Data Partnerships

20161130

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