Data Science is a field that is widely applied in most other domains on a regular basis. The huge amount of data generated regularly calls for sophisticated methods of analysis so that the best interpretatiosn can be drawn from them. Healthcare is one such field in which data science is being used extensively.

1. Application of Data Science
in Healthcare
Presented By:
Shreya Ramesh Pai (1837053)
1-MA APPLIED ECONOMICS

2. Introduction
 Medicine and healthcare is a revolutionary and promising industry for
implementing the data science solutions.
 Data Analytics is used in almost all aspects, from computerizing medical records to
drug discovery and genetic disease exploration.
 Healthcare and data science are often linked through finances as the industry
attempts to reduce its expenses with the help of large amounts of data.
 Being able to collect, structure and process a high volume of data and further make
sense of it, to gain a deeper understanding of the human body is the key objective
for thousands of data scientists and machine learning experts all over the world.
 we see 5 significant ways data science is advancing the medical industry.

3. Using data to monitor and prevent health
problems
 Human body generates two terabytes of data regularly.
 Information about heart rate, sleep patterns, blood glucose, stress levels and even
brain activity can be collected.
 Machine learning algorithms can be used to detect and track more common
conditions, like heart or respiratory diseases by analysing this data.
 Collecting and analyzing heart rate and breathing patterns, technology can detect
the slightest changes in the patient’s health indicators and predict possible
disorders.

4. Improving diagnostic accuracy and efficiency
 According to the recent research by the National Academies of Sciences, Engineering,
and Medicine, about 5 percent of adult patients are misdiagnosed each year in the US.
 Targeting this problem, a deep learning start-up, “Enlitic”, employs data science to
increase the accuracy and efficiency of diagnostics.
 The company claims to deliver up to 70 percent more accurate results, 50,000 times
faster.
 Another example is the Dutch start-up, called “Bruxlab”, which applies similar data
science and machine learning algorithms for diagnostic purposes.
 Coupled with sound recognition technologies, they help diagnose and measure Bruxism
symptoms, which often goes unnoticed due to the symptoms being highly concealed.

5. Turning patient care into precision medicine
 Similar to the way scientists collect and analyze health data in order to find
symptoms and identify diseases, doctors can track the clinical course of the
patients with confirmed diagnosis.
 Personalized treatment and informed care, enabled by technology, can significantly
reduce the death rate and lead to predictable medical outcomes.
 Physicians now have enough information at hand to identify consistent patterns in
symptoms and create accurate patient profiles.
 This enables the doctors to prescribe precision medicine that will open up the
opportunities for personalized, thus more effective treatment.

6. Optimizing clinic performance through
actionable insights
 Data science and predictive analytics are are a valuable tool which can help
healthcare providers optimize the way hospital operations are managed.
 Data science and machine learning can be used to –
1. optimize the clinic staff
2. scheduling and reduce the wait times
3. manage supplies and accounting
4. build efficient action programs for epidemics

7. Reducing hospital readmissions to cut
healthcare costs
 Analytics-based preventative medicine contributes to an overall reduction in
healthcare costs.
 A smart algorithm, used by the companies, identifies the most at-risk patients and
helps coordinate the necessary care.
 For example, Clover Health, a data-driven analytics health insurance start-up,
reports up to 50 percent fewer hospital admissions and 34 percent fewer hospital
readmissions.
 The use of data processing and analysis tools allows physicians to make informed
decisions, which results in significant savings.
 As an example, data analytics, applied to optimization of the knee replacement
process helped the healthcare provider save over $1.2 million within a year.

8. Conclusion
 The adoption of data science strategy can bring many benefits to an organization.
 However, a search for professional data scientists may become one of the main
challenges for its management.
 The lack of people or skills becomes the major obstacles to the adoption of
predictive analytics.
 The vast amount of unstructured healthcare data also complicates decision-
making.

9. THANK YOU