Introduction to the role, impact and concerns about big data in healthcare. Presentation made for the SciCloud 2015 conference.

1. The role, impact and
concerns of Big Data in
HealthCare
SciCloud, April 2015
Xavier Rafael-Palou
(Barcelona Digital Centre Tecnològic)

2. 1. Motivation
2. Introduction
3. Big Data in Healthcare
4. Technical perspective
5. Use Cases
6. Risks & Awareness
7. Conclusions
Contents
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3. ❏ Nowadays, experiments, observations, and simulations in many
areas of science and business are currently generating terabytes of
data and beyond
❏ Traditional computing environments can not manage such volumes
❏ Traditional analysis methods can not be applied
- Sampling
- Do not collect all the data possible
Motivation
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4. Big Data may sound a vague concept or a trend
❏ Marketing and promotional mischaracterizations
Sometimes meaninglessness even in technical situations
❏ “Big data” once meant petabyte scale, or unstructured chunks of
data or data generated from the Internet,...
Introduction: Vague perception
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5. Introduction: Description
Big Data means a paradigm change:
❏ Massive data can be stored, processed and exploited effectively even in
situations when having little too time
Potentialities:
❏ Science is extending its reach with new discoverments
❏ Services are becoming more adaptive, personalized, and focused
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6. Introduction: Big Data Investments
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7. Introduction: Big Data Profits
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8. Big Data enables to test exhaustively these claims:
❏ Faster identification of high-risk patients
❏ More effective interventions
❏ Better decision making
❏ Closer monitoring
❏ Correlate “prior” clinical knowledge vs Big Data crunching
Aspirin used by those at risk for coronary heart disease, combined with early cholesterol
screening and smoking cessation, could reduce the total cost of their care by more than $30
billion
Big Data in Healthcare: Why?
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9. ❏ Massive storage
Increasingly detailed data for each individual—including genomic,
cellular, environmental data, historical patient records, clinical trials
❏ Massive computation
Distributed and parallel computation on comodity hardware
❏ Powerful analytics
Allowing to process large amounts of data in batch or in streaming
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Big Data in Healthcare: How?

10. Big Data in Healthcare: Benefits
So, BigData in Healthcare
should it be an Option or
an Obligation?
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11. Big Data in Healthcare: Economic benefits
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12. Big Data in Healthcare: Research
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13. Big Data in Healthcare: Research
(*) Wang W, Krishnan E. Big Data and Clinicians: A Review on the State of the Science. JMIR Med Inform 2014;2(1):e1
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14. Recent research studies:
- Patients given a one-year regimen of Tamoxifen (Breast cancer treatment) were 56 percent less likely
to see a recurrence of breast cancer compared to those patients on the current recommendation of
just five years. (Gregory Veltri et al 2012)
- Examined 1.3 million US adults for patient-level discordance of non-high-density lipoprotein
cholesterol and low-density lipoprotein cholesterol. (Elshazly et al, 2013)
- Performed a population-based study on patients after cardiac surgery in all 16 cardiothoracic
surgery centers in the Netherlands. (A and B. Siregar et al, 2013)
- Found the trend of higher prevalence of Amelia among younger mothers among 23 million live
births, stillbirths, and fetal anomalies from 23 countries and 4 continents (Bermejo-Sanchez et al
2011)
- ...
Big Data in Healthcare: Research Outcomes
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15. Recent Healthcare Applications :
- Asthmapolis. A GPS-enabled tracker that records inhaler usage by asthmatics. Data is merged with
Centers for Disease Control and Prevention to identify individual, group, and population-based trends.
- Ginger.io. The app records data about calls, texts, geographic location, and even physical
movements of patient. The app integrates data with public research on behavioral health for detection
of lack of activity or irregular sleep patterns in patients to avoid feeling physically unwell or anxiety
symptoms.
- mHealthCoach. Supports patients on chronic care medication, providing education and promoting
treatment adherence through an interactive system. Also used by providers and payors to identify
higher-risk patients and deliver targeted messages and reminders to them.
- Many others. Catlight Health, MyDrugCosts, Symcat, iBlueButton
Big Data in Healthcare: Industry
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16. Big Data: Technical perspective
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17. We speak about big data thanks to recent technological advances and
the role of open source community which has allowed them to speed
up and spread
Where are the main technological contributions?
❏ Infrastructure: Distributed file systems
- Fault tolerant
- Runs on comodity hardware
- Handle large datasets
❏ Analytics: Map-reduce
- Computational model for computing clusters
Big Data: Technical perspective
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18. Big Data: Infrastucture
Option 1: Rent cloud Infrastructure as a Service
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19. Option 2: Build yourn own cluster, but ...
● Consistency: all nodes see the same data at the same time.
● Availability: a guarantee that every request receives a response about whether it
succeeded or failed.
● Partition tolerance: the system continues to operate despite arbitrary message loss or
failure of part of the system.
Big Data: Infrastucture
(batch) (stream)
(Brewer’s
Theorem)
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20. Lots of techniques for mining data:
- Pattern discovery, correlations, clustering, classification,
recommendations,...
For dealing with Big Data, the algorithms have to be re-organized
(Map&Reduce) in a way that takes advantage of cluster computing
Big Data: Analytics
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21. Cardiovascular disseases are one of the main human
mortality causes. Cardiac arrhythmia is one of main symthoms
Recent introduction of Holters has allowed improve arrhythmia detection
by 24h monitoring cardiac frequency while doing normal life.
Goal: support physicians in the study of this large amount of data
building automatic arrhythmia analyzer
Use case: Automatic Holter Analyzer
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Holter
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22. Data analysis: Store / load, cleaning, feature extraction,
beat detection, beat recognition and finding patterns
- 1 analysis → 3 signals of 10K rows
- 1 user up to 5 -20 analysis/year
- Computational cost: aprox 10 hours for building
models with 100 signals/1M rows (Local centralised server)
Challenge: Data mining for batch analysis of large amount of data
from thousands monitored holter signals
Use case: Automatic Holter Analyzer
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Holter
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23. Use case: Automatic Holter Analyzer (Proposed solution)
Massivedatasets
Clinical Decision Support System
New signal
Holter
Clinical experts
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24. People aged from 65-80 will raise by nearly 30% in 2020
How to improve elderly quality of life,autonomy and independence given heath system
limitations
Required improve teleassistance services to prevent emergency situations, early detection and
prevention of aging Illnesses (activity decline, alzheimer, dementia)
Variety of data:
- Home automation sensor data (e.g. door switches,
motion sensors, environmental sensors, gas,
smoke detectors)
Real time Data Analysis Challenges:
- Activity/Inactivity
- Provide summary of action events (sleep hours,...)
- Fit models with variety of data
Use case: Advanced teleassistance system
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25. Use case: Advanced teleassistance system
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26. Use case: Advanced teleassistance system
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27. Use case: Advanced teleassistance system
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28. Use case: Advanced teleassistance system
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29. Challenge: Scale up
❏ Integration of home automation with new data services:
- Health services (e.g. Bluetooth glucometer, weight scales, blood pressure)
- Social services (e.g. facebook notifications/images,Gmail messages, Online games)
- Clinical shared records
❏ Real time Data Analysis & visualisation:
- Intrusion detection, fall detection
- Personalised health and, social recommendations
- New data summarisation for carers
❏ Thousands of users being monitored on real time
Solution Approach: AWS (Amazon Web Services), Kinesis, S3, EC2
Use case: Advanced teleassistance system
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30. - Security & privacy. Concentrate and analyse so much data gives
too much power and responsability to the institutions,
organisations, private entities in terms of Information that can be
derived or leaked
- Data analysis challenge. Data is the new oil, but massive data
sometimes difficult and messy to extract
- Cost. One of the biggest stumbling blocks for Big Data in
healthcare right now, even though applications like Hadoop are
open-source
- Recent technologies. Continuous tools in market, new tools
require consolidation, few experts and difficult to find
Risks and Concerns
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31. - Big data provides infrastructure and analytics for massive data
- Big Data can improve healthcare system (5 R’s)
- Research can boost its productivity
- Key technical aspects: DFS and Map & Reduce
- Big Data raises security and privacy concerns
- Required right policies to make the change possible in healthcare.
E.g. Obama-backed initiative that sets aside $14.6 billion to
encourage electronic medical record adoption
Conclusions
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32. ❏ Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman. Mining of Massive Datasets. 2014
❏ Jared Dean. Big Data, data mining and Machine Learning. Wiley, 2014
❏ Vignesh Prajapati. Big Data Analytics with R and Hadoop. Packt Publishing, 2013
❏ Peter Groves, Basel Kayyali, David Knott, Steve Van Kuiken. The ‘big data’ revolution in
healthcare. Accelerating value and innovation. Center for US Health System Reform.
Business Technology Office. 2013
❏ Frontiers in Massive Data Analysis. THE NATIONAL ACADEMIES PRESS, 2013
❏ Edwin Morley-Fletcher. An overview of the challenges in data intensive healthcare
❏ Wang W, Krishnan E. Big Data and Clinicians: A Review on the State of the Science. JMIR
Med Inform 2014;2(1):e1
References
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33. Thanks for your attention…
xrafael@bdigital.org
April, 2015
SciCloud
Acknowledgements
Questions
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