Dexcom: CGM technology, IoT connectivity, and the drive to insight delivery in diabetes care

Transcription

1 Dexcom: CGM technology, IoT connectivity, and the drive to insight delivery in diabetes care Nate Heintzman, PhD Sr. Manager, Data Partnerships

2 MILLIONS 700 Diabetes is a global crisis 1 IN 3 ADULTS IN US WILL HAVE DIABETES BY M ADULTS GLOBALLY 108M ADULTS GLOBALLY PREVALENCE OF DIABETES TYPE 2 Source: NCD Risk Factor Collaboration, Published April 2016; CDC

3 Diabetes is a global crisis United States $320 BILLION Germany $35 BILLION China $51 BILLION Japan $29 BILLION WORLDWIDE DIRECT ANNUAL COST $673B - $1.2T Source: International Diabetes Federation Diabetes Atlas 7 th Edition, 2015

4 Patients and their care teams live with uncertainty about their diabetes, every day 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? Are her lifestyle changes having an impact? Is her $400/month medication working for her? Will she need to start taking insulin injections soon?

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6 Continuous Glucose Monitoring (CGM) We now have a whole new understanding of glucose levels BEFORE CGM WITH CGM 2:15 PM 2:45 PM 3:45 PM Continuous flow of data Holistic understanding of time in range Better decisionmaking to increase time in range Patient can share data with family members and clinicians

7 Dexcom CGM Delivering 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 AUGUST 2015 Software that delivers personalized, easy to understand analysis of glucose trends APRIL 2015 First apps to enable CGM on Apple Watch AUGUST 2015 First fully mobile CGM that sends data directly to smartphone without receiver

8 The status quo was to isolate the data Family Care Team Medical Record INACCESSIBLE Payor Research DATA ISOLATED TO DEVICE DATA ISOLATED TO SPECIFIC COMPUTER

9 Dexcom has invested in a different approach DATA SCIENCE BIG DATA CLOUD COMPUTING MACHINE LEARNING PLATFORM INTERNET of THINGS MICRO ELECTRONICS

10 Dexcom platform core requirements Security Privacy Integration Adaptability Securing to industry/regulatory standards Protecting patient s right to determine how their data is used Easing integration by building with industry standards Architecting with scalability, resilience, and multi-tenancy to support extended ecosystem Regulatory / Quality Compliance

11 Adopting best-in-class big data technologies Solution Overview Qualities Hadoop Cassandra Sqoop Kafka Spark Ecosystem of tools that enable distributed processing and analytics of large disparate data sets across multiple systems Distributed data store optimized for availability and high write throughput. Commonly used to back end API and store/process time series data Enables integration between Hadoop and relational data sources Distributed, partitioned, replicated commit log service that provides queuing system functionality. Fast and general-purpose cluster computing system providing high-level APIs and also supports a rich set of higher-level tools. Highly redundant Fault tolerant Large storage capacity (handles large datasets) Scalable Highly redundant Fault tolerant Low latency Linearly Scalable Enables new technologies to leverage pre-existing investments in relational data systems Highly redundant Fault tolerant Wicked fast Linearly Scalable Can use it interactively with Scala, Python, and R Can combine the power of SQL, Machine Learning, and Streaming Python/R Languages 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

12 What can the Dexcom platform do for you? Data Platform

13 What can the Dexcom platform do for you? APIs Data Platform

14 What can the Dexcom platform do for you? 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)

15 What can the Dexcom platform do for you? 3 rd -party app 3 rd -party software 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)

16 What can the Dexcom platform do for you? 3 rd -party app 3 rd -party software 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)

17 What can the Dexcom platform do for you? 3 rd -party app 3 rd -party software 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)

18 Enabling integration, insight, and innovation Alert family of danger and locate patient Cost Data Determine impact of glycemic control on cost of care Give high-risk patients additional support Risk Data Geolocation Data Data Science Identify patterns to inform new treatments Evaluate treatment and intervention efficacy Treatment Data Device Integrations Closed-loop technology for automated insulin delivery

19 and empowering everyone involved to improve outcomes and reduce costs POPULATIONS PATIENTS Navigate daily selfmanagement decisions with feedback loops for personal understanding of glucose response CLINICIANS Identify patients who need more attention and develop individualized precision treatment plans PAYORS 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

20 Getting there is not simple Economics of sharing data Privacy & Security Industry status quo Regulation Which data returns true value? Cross pollination

21 Driving together to the future of diabetes care Payors EHR

22 Dexcom: CGM technology, IoT connectivity, and the drive to insight delivery in diabetes care Nate Heintzman, PhD Sr. Manager, Data Partnerships

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