Big data in Medical Research in india

1. Big Data In Medical Research
-By (Group ID 8)
1514098 Onkar Pande
1514103 Hiral Kotak
1514104 Tahir Rizvi
1514110 Kaushal Shah

2. Introduction
The cost of healthcare in India is increasing at 20% every year, which is more than
double that of overall inflation in the world. There is a shortage of 1.5 million
doctors and 2 million Hospital beds.
In Medical field, the quantity of data which is routinely generated and collected
has increased greatly, so does the ability to analyze & interpret it.
Medical Research Focuses on :
Improvement of care efficiency & cost, Improvement of clinical trials,
Prediction of diseases, Finding cures and measures to prevent diseases &
constant study of health of population.

3. Source of Big data in Medical
Digital demographic of patients
Medical claims & Pharmacy Claims
Laboratory tests
Biometric engineering results
Health Gadgets

4. Big Data Analytics in India
The study shows that in the year 2008 there were only 5 personalised medicines
available and it has increased to 132 in the year 2016
But even today, most healthcare organizations including hospitals spend less than
1% of their budget on software technologies as they have not seen serious
business value generated from such initiatives in the past.
Ministry of Electronics and Information Technology, Government of India, runs
Aadhar-based Online Registration System, a platform to help patients to book
appointments in major government hospitals.The portal has the potential to
emerge as a source of big data in India.

5. What are the challenges & what needs to be
done?
● Electronic Record Systems:
Healthcare systems are not required to exchange data with each other. Some
regions are planning to establish regional electronic health records but most are in
preliminary stages.
To overcome these problems, the interoperability of electronic records needs to
be improved,especially for data structures,data standards,and data transfer
agreements.

6. What are the challenges...
● Lack of medical terminology system:
The lack of a widely adopted and consistently implemented medical terminology
system is another problem for using big data in medical research.
By integrating and distributing key terminology ,classification, and coding
standards in medicine, these systems promote more effective and interoperable
biomedical information systems and services, including electronic health records.

7. What are the challenges...
● Current medical practice patterns
The current system is such that there are no common standards for the same
data. So whenever patient moves from one hospital to other called “medical
migration”, although through electronic records it is not possible as there are
variety of the data.

8. What are the challenges...
● Data quality:
It is one of the most important feature of the big data. If the quality of the big data
will be higher than there will be higher accuracy and vice versa. But also it is
difficult to validate it.
● Privacy Concerns:
Although it is the main thing for big data in health and medicine but there is no law
present for it. There should be privacy concerns about the data.And it should not
affect the completeness of the data.

9. Opportunities to improve health
● The use of big data in medicine includes public health promotion (disease
monitoring and population management), healthcare management (quality
control and performance measurement), drug and medical device
surveillance, routine clinical practice (risk prediction, diagnosis accuracy, and
decision support), and research.
● New data analytics, such as machine learning, to replace much of the work of
radiologists and anatomical pathologists, can also be used and is an active
area of research in developed countries.

10. Conclusion
● The application of big data in health and medicine is likely to change medical
research, medical practice, and the development of the healthcare industry in
the near future.
● Hence,Big Data has a huge scope in future and can greatly impact the future
of mankind

11. Thank You!