Big Data Analytics in Health Care - AIDA Home - Acadia ...aida.acadiau.ca/tl_files/sites/cs/Acadia...

Alex J Mair

Canada Health Infoway

Emerging Technology Group

March 21st, 2014 Copyright © 2014 Canada Health Infoway Acadia University

Big Data Analytics in Health Care

Copyright © 2014 Canada Health Infoway

Infoway has the exclusive right to make copies of this document. No alterations, deletions or substitutions may be made in it without the prior written consent of the owner.

No part of it may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, email or any information storage and retrieval system, without the prior written consent of the owner.

Big Data in Health Care Agenda

• Canada Health Infoway Background

• Trends

• Definition and Characteristics

• Economics

• Opportunities and Challenges

• Call to Action

• Summary

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Canada Health Infoway

• Created in 2001

• $2.1 billion in federal funding

• Independent, not-for-profit corporation

• Accountable to 14 federal/provincial/territorial governments

Mission: Fostering and accelerating the development and adoption of

electronic health record information systems with compatible standards and communications technologies on a pan-

Canadian basis with tangible benefits to Canadians.

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Enabled by EHR Solutions Blueprint

• A common technical conceptual architecture


• Point-of-service applications can be added, are extensible & scalable

• Depicts shared data locally, jurisdictionally and regionally

• Enables cost effective & re-useable data integration

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Emerging Technology Group

Mission

• Emerging and disruptive information and communication technologies.

Vision

• Appropriately and effectively used

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Nexus of Forces

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The digital world – the Internet and the cloud and supercomputing and social networking – is breaking medicine out of its cocoon. It’s a super convergence we’ve seen in other walks of life but not in the health and medical sphere.

Eric Topol, MD

quote from

Wired Magazine

Feb 2012

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The Paper Era

Clinical Applications

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Home Care

Emergency Service

Family Practice

Clinic

Pharmacy

Lab

Medical Imaging

Department

Specialist Office

Health Consumer

Other

The e-Health Era

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EHR Data and Services


The Digital Health Era EHR Data and

Services


Mobile Apps

Genomics

Analytics

Devices

Social

Clinician must stay on top of • 10,000+ diseases &

syndromes • 3,000+ Rx • 1,100+ lab tests • 80% of data is

unstructured

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Services


Mobile Apps

Genomics

Analytics

Devices

Social

PubMed has over 22.6 million records • 1 million: annual rate at which

articles are indexed • 13% of articles, published in NEJM

in 2009, were reversals of previous findings

• 50% (half) of clinical guidelines become outdated in < 6 yrs

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Services


Mobile Apps

Genomics

Analytics

Devices

Social

Each minute of the day • 2 million searches

generated on Google • 100,000 twitter messages • 700,000 pieces of content

shared on facebook

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Services


Mobile Apps

Genomics

Analytics

Devices

Social

Human genome • 3 billion base pairs, 6 billion

DNA letters • 4 million variants per patient’s

genome Gene sequencing can: • Be created in 7 days • Produce 600Gb of data per run

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Services


Mobile Apps

Genomics

Analytics

Devices

Social

Smart phones and apps • Nearly four billion

smartphones sold (4 years) • 40,000 mobile health apps

and hundreds of devices allow consumers to track indicators in real time.

• Consumers downloaded 24 million health apps in 2012

Each minute of the day • 50,000 apps downloaded

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Services


Mobile Apps

Genomics

Analytics

Devices

Social

Number of patients monitored over mobile networks will reach three million globally.

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BDA Defined

• “Big data” characteristics include: high volume, high velocity and variety of types of information that demand cost-effective and innovative forms of information processing

• “Analytics” is the process of examining large amounts of big data to deliver new insights that can enable decisions in real or near real time

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BDA in Health Care

Characteristics include four aspects • Value

• Veracity

• Visibility

• Visualization

Value

Visualization

Value

Veracity

Visualization

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Visibility


Collection Process Differences

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Big Data Traditional Data

Exabytes, real-time Terabytes (limited near real-time, traditionally retrospective)

Distributed Centralized

Unknown, unstructured and not necessarily modeled

Modeled and stable

Exploratory, dynamic and discovery based

Transactional, established and known requirements

External data In-house


BDA Functions Differently

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Big Data Analytics Traditional Analytics

Experimental type of analytics

Are based on answering known questions or hypotheses

Open ended "how and why" type questions

Are designed to query specific "what and where“

Processes unstructured data to find patterns

Processes structured and aggregated data

Automated, mines and flags data relevant for other use and analytics

End user initiated


Existing data now available for new uses • Health publication and clinical reference data • Clinical data • Business, organizational and external data

Sources of Big Data

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New data available for use by health system • Web and social networking-based data • Streamed data • Genomic data

Sources of Big Data

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New Analytics Concepts

• Make innovative uses of: • Data mining • Natural language

processing • Artificial intelligence • Predictive analytics

• Integrate vast amounts of data from different sources

• Recognize patterns, correlations and anomalies

• Analyze, contextualize and visualize data

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Trends in consumer digital health

• Internet is main channel for researching health information

• Individuals are connecting with others who may share their condition

• A new generation of health destinations, through social networking has emerged

• Smartphones with fitness, and medical devices, real-time observations deliver new possibilities for personal and patient health monitoring and analytics

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Genomics is Already Here

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• Pharmaco-genomic data for drug and dosage selection

• Availability of over 1500 genetic tests, and several targeted therapies

• Pre-symptomatic diagnosis – BRCA gene mutation testing for breast cancer

• Personalized therapy – Herceptin for breast cancer • Personalized drug dosage – reduced dosage for

treatments for colon cancer based on gene


Economics of BDA in Health Care

Note: Figure used with permission of McKinsey Global Institute

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Note: Figure used with permission of McKinsey Global Institute


Economics of BDA in Health Care

Clinical and business value

Capabili

ty

28

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Drill down, “slice and dice” of the data to understand root cause

Predictive models, learning models, data mining discovery, benchmarking

Standardized, static views into the data

Understanding implications resulting from changes to the underlying data or analytics

What are the Opportunities

Identifying opportunities to tailor and optimize care


Capabili

ty

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Types of Analytics





Capabili

ty

30

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These three dimensions of analytics provide the framework for a transition to a more mature state of analytics where as information is actioned by decision makers, value increases significantly

Insights that create value

• Big Data – more than 1,000 recordings per second of physiological measures such as body temperature, heart rate, respiratory rate and blood pressure

• Functionality or Scope – hospital-acquired infections are a serious issue, for premature babies (immune systems that are both immature and inexperienced), late onset neonatal sepsis (LONS) can be deadly unless doctors and nurses act fast

• Analytics – real-time analytics and algorithms predict when a baby is at risk of infection by detecting subtle changes in physiological measures

• Outcomes – physicians are alerted of a life-threatening infection before child shows signs of illness, by intervening hours earlier, improving outcomes dramatically, such as shorter hospital stays and reduced costs

Toronto Sick Kids – Artemis


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• Big Data - 24/7 near 'real-time', over 15,000 media sources filtered for relevancy and categorizing of information, complemented by human analysis

• Functionality or Scope – system gathers preliminary reports on relevant unverified and verified information on disease outbreaks and other public health events by monitoring global media sources (in English, French, Arabic, Spanish, Portuguese, Russian, Farsi, Traditional Chinese and Simplified Chinese)

• Analytics - tracks events such as disease outbreaks, infectious diseases, contaminated food and water, bioterrorism and exposure to chemicals, natural disasters, and issues related to the safety of products, drugs and medical devices and radioactive agents

• Outcomes – reports allow users to respond to potential health threats in a timely manner

Global Public Health Intelligence Network

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• Big Data – 80% of healthcare data is unstructured, consisting of physician notes, registration forms, discharge summaries, echocardiograms and other medical documents

• Functionality or Scope – 500,000 new cases of congestive health failure (CHF) are diagnosed every year and more than half of CHF patients need to be readmitted within six months after treatment

• Analytics – mines unstructured data using natural language processing and search technologies to predict which patients are most at risk for readmission, based on risk factors such as smoking

• Outcomes – combined with structured data, provides a more accurate picture of trends, patterns and deviations, allowing clinicians to make better treatment decisions and predict the probability of a person's readmission to the hospital

Seton Healthcare Family – Natural Language Processing

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• Big Data - weekly, millions of users search Internet for health information online

• Functionality or Scope – how can search data be leveraged for predicting disease outbreaks

• Analytics - gathers data from Google search, estimates how much flu is circulating in different countries and regions across the world, determines flu activity levels

• Outcomes - detects regional outbreaks of influenza weekly - gets smarter, producing higher-quality predictions, and faster, as it ingests more data

Google.org Flu

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• Big Data – largest set of data on human genetic variation is freely available on the Amazon Web Services (AWS) cloud and researchers only pay for the computing services that they use

• Functionality and Scope – BioMe program - 25,000 people participating in DNA sequencing and longitudinal studies linked to data embedded in their electronic medical records

• Analytics – identification and development of biomarkers which can predict individual disease risk, enable early detection of disease, and improve diagnostic classification to better inform individualized treatment

• Outcomes – clinical decision support engine delivers guidelines with genetic variants of clinical significance informed by the patients geotype data and other longitudinal clinical data sourced from their electronic health record

Mount Sinai Medical Center of NYC - Personalized Care (Genomics)

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Infoway predictions & Challenges

• Larger HDOs

• Incremental and slow sophistication

• Key application areas

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• Skilled resources – mathematics, statistics, machine learning, research, technical tools

• Privacy and security – PHI protection, use, legal, policy, legislation interpretation

• Identify champions

•Understand BDA

• Culture, processes, staffing for BDA

• Combine several emerging technologies

• Limited scope pilots

• Evaluate and measure benefits

Cautious Execution

•Don’t go it alone

•Use cases, business cases

Call to Action

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Communications Leadership Collaborate

In Summary

• Big Data can improve patient outcomes and represents significant economic value and opportunities across healthcare

• Use cases exist, with practical guidance to discover value from big data:

• Clinical research (cancer)

• Real time remote patient monitoring

• Personalized medicine

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Traditional Data Analytics

Health Analytics

Cli

nica

l Ana

lyti

cs

Traditional data = everything except volume and velocity

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Big Data vs Small Data Analytics (SDA)

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• Continue to do traditional data analytics (TDA)

Big Data vs Traditional Data Analytics (TDA)

• BDA and TDA will co-exist

• Collection and functions different

• Big data analytics = Traditional data analytics

Website: www.infoway-inforoute.ca

ETG Resources & BDA White paper: https://www.infoway-inforoute.ca/index.php/resources/technical-documents/emerging-technology

E-mail: [email protected]

Some Additional Resources


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http://www.infoway-inforoute.ca/



https://www.infoway-inforoute.ca/index.php/resources/technical-documents/emerging-technology







mailto:[email protected]



Questions?


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Thank you


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