nasscom big data and analytics summit 2015:session viii:real time analytics as a disruptor

Harnessing the Disruptive

Value of Mobile Data and Analytics in Diabetes Care

Anand K. Iyer, PhD, MBAChief Data Science Officer, WellDocPast President and COO, WellDoc

NASSCOM Big Data Analytics 2015Hyderabad, India

June 26, 2015

©2009-15 Anand K. Iyer & Associates, LLC and WellDoc, Inc.. WellDoc and the logos associated therewith and all other WellDoc marks contained herein are trademarks of WellDoc. All other marks contained herein are the property of their respective owners.

Today’s Presentation

• Context: Defining and Framing “Big Data”• Extracting Disruptive Value from Data: What’s

Achievable?• Case Study: “Mobile Prescription Therapy” for

Type 2 Diabetes• Questions and Panel Discussion


BIG Data: It’s Everywhere!

• Everything we do (as persons and “things”) is increasingly leaving a digital signal or “trace”

• These “traces” can be collected, organized, cleansed and analyzed to extract value


Sources and Attributes of Traces

• Traces with “People”– Lifestyle– Health– Psycho-social– Socio-economic– Genomic

• “Traces with IoT”– Signature– Physical – Environmental– Security– Configuration

4 V’s: Volume, Variety, Velocity, Veracity*(+ Anand’s 5th: Value!)

*Source: Bernard Marr


Putting Volume and Velocity in Context!

2005 2008 2010 2012 2015 2020

130 422 1,227 2,720

7,910

35,000 Exabytes

1 EB = 1,000,000,000,000,000,000B = 1018 bytes = 1,000,000,000 gigabytes

The amount of data is growing

40 TIMESfaster than the world population

Source: 2012 Rock Health Big Data in Digital Health Report


Right Data

Right Person/Thing

Right Place

Right Time

Extracting Value from Big Data Requires Connectivity and …


Data

Information

Knowledge

Actions

Outcomes

S-M-A-RTSpecific. Measurable. Actionable. Real-Time

… Context: … Easier Said Than Done!


What Kinds of Value can be Realized?

• Mean-logistics response time (MLRT) for readiness drivers in the US military supply chain– Pre-wireless, no ITV: 23 days in-theater distribution– Post-wireless, ITV: 3.5 days in Balad, Bagram Air Base

• In-building energy efficiency*: – 38% reduction in annual, in-building energy cost from $2.42/ft2 to $1.64/ft2

• Public safety response (US)*– ~40 min command & control setup to ~6 min– Derivative value: $128B potential savings in annual deaths, injuries,

property damage

*Sources: In-building Wireless Alliance, www.ibwa.org, US Army G4 and Army Materiel Command)


http://www.ibwa.org/

Overarching Objectives for Big Data

• Unlock insight into “discovering what we don’t know”• Precisely understand people and “HumEx”• Enable a “Hubble-like” view to optimize business process• Transform public safety and security• Pave a path for eco-sustainability• Transform health outcomes and economics

Simply put, we have an opportunity with big data to Inform, Discover, Extrapolate and Adapt


Source: www.diabetes.org, American Diabetes Association, August 1 2014

©2009-15 Anand K. Iyer & Associates, LLC and WellDoc, Inc.. WellDoc and the logos associated therewith and all other WellDoc marks contained herein are trademarks of WellDoc. All other marks contained herein are the property of their respective

owners.

http://www.diabetes.org/

http://www.cdc.gov/diabetes/prevention/?s_cid=bb-nccdphp-diabetes-002

Source: www.diabetes.org, American Diabetes Association, August 1 2014


http://www.diabetes.org/

http://www.cdc.gov/diabetes/prevention/?s_cid=bb-nccdphp-diabetes-002

India is not Immune to this Problem …

In urban areas, where there is better ability to pay for healthcare services and where higher diagnosis rates are likely, cases of type 2 diabetes are estimated to increase substantially from 30.5 million in 2010 to 49.9 million in 2020. This is due to an increase in urbanization rates as well as an increase in diabetes risk factors associated with urbanization, mainly obesity

In rural areas, however, the number of diabetics will increase at a slower rate compared to changes in urban areas due to lower risk factors and demographic transition from rural to urban populations.


India’s Diabetes Realities

• Inadequate infrastructure– 0.59 doctors per 1000 population (global norm 2.25)

• Limited reimbursement– 70% of total population incur out-of-pocket expenditures¹

• Under-investment in public healthcare – Public sector spending 0.9% of GDP

• Uneven access to care– Only 24% of rural areas have a health facility of some form²

• Low level of diabetes awareness & knowledge– 20% of adults do not know “diabetes”

• Low affordability among middle & low income population– 98% of households at <$13K annual income

Source(s): 1. Frost & Sullivan Report 2. National Commission on MacroEconomics and Health, 2014


Patient Coach• Personalized, real-time

coaching• Testing and medications

reminders• Out of bounds alerts• Metabolic target ranges

Social Engagement

• Family and community connectivity

• Caregiver alerts and support

Clinical Decision Support

• Outcomes-based support • EMR/EHR integration• Clinical analysis and trends• Patient stratification • Population managementAutomated Expert

Analytic System™• Cloud-based analytics• Evidence-based guidelines• Predictive modeling• Longitudinal tracking• Event/alert tracking


Data Capture Frequency

DataCapture Source

Patient

Non-Patient (e.g., 3rd party, system, etc.)

Low(~Static)

High(~Dynamic)

• System-generated KPI’s, alerts, trends

• System and feature-level utilization data

• Absence of data

• Physiologic data • Symptom data• Lifestyle data• Medication data

• Registration data (e.g., demographics, medication history, daily regimen, etc.)

• Psycho-social data (health literacy, cultural, social, etc.)

• Labs and screenings• Evidence-based

guidelines• Medication regimen• Metabolic targets and

ranges• Clinical rules• Physician instructions

Expanding Breadth and Depth of Data …


Discover Using known data to draw insights

• Patterns• Discriminant

functions• Behaviors

Inform Presenting primary

analyses of the data• Engagement or usage

data• Clinical value data• Population data

Extrapolate Using existing data to model patterns and predict future occurrence of events

• Probabilistic models• Machine learning

based models

Data Presence

Analysis Intent

“Don’t Know What I’m Looking

For”

“Know What I’m Looking

For”

“Have Data” “Don’t HaveData”

Adapt Using learning techniques to provide new insights and choices for optimizing outcomes

• New product features• New service features

IDEA!

… and the Value we can Extract from it


What This Means for Patients

Data Information Knowledge Action

• Blood glucose• Blood pressure• Medications• Symptoms• Lifestyle• Social

• Daily BG patterns• Weekly test

patterns• Dynamic cohorts• What works• What doesn’t

• Correlation: BG to x• Causation: Closed

form mathematical models

• Prediction: Hypo, hyper, med adherence

• Coaching, alerting• Prevention• CDSS for providers


20

Patented Clinical & Behavioral Engine

So What Did Big Data Achieve for Us?


Cartoon by Harley Schwadron via kaiserhealthnews.org

The Future? BIG Data has a BIG Role!


nasscom big data and analytics summit 2015:session viii:real time analytics as a disruptor

Presentations & Public Speaking

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trademarks of welldoc

iyer associates

respective owners

rock health big data

diabetes care anand

attributes of traces

mba chief data science