This document provides an overview of sources of big data in healthcare and their applications. It discusses traditional sources like medical claims, electronic health records, and medical imaging. It also examines emerging sources like internet of things sensor data, social media data, mobile network data, and satellite imagery. The document outlines how these diverse data sources can be used for applications like personalized healthcare, disease surveillance, disaster management, and climate change adaptation. It concludes that big data opens new opportunities to improve healthcare through right interventions for patients. However, issues around data representativeness and bias must be addressed.

1. Sources of Big Data in Health (a comparative description of
national and international data sources and identification of
new/emerging sources of data)
11-12 February 2019 Bangabandhu International Conference Center (BICC),
Agargaon, Dhaka, Bangladesh
Moinul Zaber, Ph.D.
zaber@du.ac.bd
Yusuf Brima
yusufbrima@cse.du.ac.bd
Data and Design Lab https://dndlab.org
Department of Computer Science and
Engineering, University of Dhaka

2. 1. Scope and Framework of Big Data
2. Sources of Big Data for Healthcare
and applications
contents
3. Key Issues
4. Conclusion
5. References

3. 01
Scope and Framework of
Big Data in healthcare

4. Motivation
The doctor of the future will give no medicine, but instead will interest his
patients in the care of human frame, in diet, and in the cause and prevention of
disease.
Thomas Edison (1847 – 1931)
Big data healthcare market is estimated to grow from $10bn in
2016 to reach $27.6bn by 2021 at a robust CAGR of 22.3%.
Source: http://fusionanalyticsworld.com/

5. Source: Cisco Global Index 2016

6. Goal of Big Data Healthcare Analytics
Source: https://www.researchgate.net/publication/309908207_Big_Data_and_Predictive_Analytics_Application_in_Public_Health_Field
Provide Personalized care through right intervention to the right patient
at the right time

7. What’s there in Healthcare Big Data
Source: https://www.slideshare.net/leobarella/big-data-applications-in-health-care

8. How is the current Healthcare Analytics
Ecosystem ?
• EMR
• Clinical notes
• Medical Images
• Clinical trials
• Genomics
• Resource allocation
• Claims and Billing
• Approvals and Denials
• Population Health and
Risk
• Population Health
• Patient Similarity
• Drug Discovery
• Vital Signs
• Activity levels
• Nutritional data
• Behavioral data
• Social
Interactions
Patient
Pharma/
Drug
Companie
s
ProvidersPayers
Use of Bigdata for
healthcare public
policy is rare

9. 02
Sources of Healthcare Big
Data and applications

10. Including private payer
and plan claims,
government health plan
claims, and pharmacy
claims. Electronic
records from healthcare
facilities
Medical insurance
claims
Insurance Provider
The IoT has already made
waves in the energy and
utilities, home monitoring, and
transportation industries, and
the number of connected
things in healthcare is
growing.
Data streams do not provide
confirmed infection status for
specific pathogens.
Provide individual-level
health data in near real time
Derive from use of Internet
search engines, social media,
or mobile phones.
Self-reporting of health
outcomes.
Provide information on health-
related behavior, including
contact and travel patterns,
vaccine status and sentiments.
Key ingredients for
understanding and modeling
disease transmission.
Hospital discharge records
Death certificates
Provide information on
disease status.
Can be monitored at the
individual level or
aggregated geographically
before reporting.
IoT-sensor Devices Non-health digital data Medical encounter data
Data
Sources of Healthcare Big Data

11. Body Sensor Network as an heterogeneous
data source
Source: https://www.semanticscholar.org/paper/Performance-Analysis-of-Multi-hop-and-Cluster-Based-Azhari-
Toumanari/6093a8ab95c1b92e5e8a21524adbf5282c24c9c2

12. Mobile network data as tool for predictive
modeling
(right) Population migration during Haiti earthquake
(left) The distribution of settlements, cell towers, and malaria risk in Kenya
Source: http://science.sciencemag.org/content/338/6104/267.full?sid=bc279ee7-05cd-41d4-a975-e01beebdcece

13. Using satellite data to develop
environmental indicators
Satellite-derived annual average surface-level PM2.5 concentrations at 50% relative humidity, 2001–06 (map generated from data available
at http://fizz.phys.dal.ca/~atmos/martin/?page_id=140)

14. Medical Imaging and diagnostics

15. Healthcare support system for the elderly
A typical situation involved
an elderly person, recovering
from a medical condition at
home, linked to a
combination of several
connected services
streaming data towards
different parties, such as
family members, telecare
givers and physicians.
Source: https://www.researchgate.net/publication/306022306_Medical_Internet_of_Things_and_Big_Data_in_Healthcare/figures?lo=2

16. Disaster Management
• Enhance Ground Level Intelligence through UAVs
• Imagery
• Cell Phone Triangulation LT5534 power signal detectors
• Re-establish Communication Networks
• Iridium 9602 M2M
• CPS Interactions
• Site Embedded
• Mobile
• Semi-Autonomous Operations - Swarm

17. Climate Change Adaptation
Interaction between Climate, Water and Society (people and
infrastructure). Red arrows indicate key fluxes where models need to
be integrated and model interfaces developed.
Major interactions between model blocks (b) Interfaces providing
aggregation or discretization for temporal scales (c) Interfaces
providing conversion between dimensions and spatial scale

18. The architecture of Healthcare Monitoring System

19. 03
Key Issues

20. Representativeness
and Biases
Key Issues

21. 04
Conclusion

22. Conclusion
✓ Big Data has opened up new possibilities to analyze traditional data sources
in conjunction with other heterogenous data sources
✓ These new data resources are getting enriched every milliseconds and
available all around the world
✓ Those in the field of disease surveillance and modeling can learn a lot from
other data-and modeling fields, such as meteorology and marketing, and can
ultimately provide useful tools to improve situational awareness and outbreak
response for a variety of old and new diseases.
✓ An effort to design policy, regulation and Big Data governance schemes may
facilitate “Personalized care through right intervention to the right patient
at the right time”

23. 05
References

24. References
1. http://www.who.int/news-room/fact-sheets/detail/maternal-mortality
2. http://www.pnas.org/content/suppl/2015/08/12/1423542112.DCSupplemental
1. https://academic.oup.com/jid/article/214/suppl_4/S375/2527914#43448010
1. https://www.fic.nih.gov/News/GlobalHealthMatters/january-february-2017/Pages/big-data-infectious-disease-
surveillance-modeling.aspx
1. https://www.youtube.com/watch?v=fEhM_dHJZCo&feature=youtu.be
1. http://wws.princeton.edu/news-and-events/news/item/cellphone-data-can-track-infectious-diseases
2. Safa, B., Abed, M., Zoghlami, N., & Tavares, J. M. (2018). BIG DATA for Healthcare: A Survey. IEEE Access.
3. Bengtsson, L., Gaudart, J., Lu, X., Moore, S., Wetter, E., Sallah, K., . . . Piarroux, R. (2015). Using Mobile
Phone Data to Predict the Spatial Spread of Cholera. Scientific Reports, Nature Publishing Group.
4. Dimitrov, D. (2016). Medical Internet of Things and Big Data in Healthcare. PubMed .
5. Wesolowski, A., Stresman, G., Eagle, N., Stevenson, J., Owaga, C., Marube, E., . . . Buckee, C. (2014).
Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile
phones. PubMed.

25. The End
Thank you