Data science in healthcare is not a new concept. However, it is the most common industry to use data science and analytics. The global pandemic has significantly increased the demand for and significance of healthcare data scientists. We've seen how data science professionals have banded together over the years to work on Covid-19 Healthcare data and build AI/ML models to track the outbreak. This information was used for contact tracing, screening, and the development of vaccines.

As a result, data science can potentially transform the entire healthcare system.

Perhaps you've worked in healthcare for some time and want to change careers to put your analytical skills to the test. Perhaps you have extensive experience in data analysis and are looking for a field where you can apply your knowledge and expertise. Perhaps you are dissatisfied with your current career advancement in the healthcare industry and wish to earn a higher salary like your IT colleagues. Believe me, it is also possible, and the key is data science and artificial intelligence.

In either case, you're thinking about whether a career as a healthcare data scientist is a good fit for you.

This blog will assist you in becoming acquainted with everything you need to know.

4 Major Data Science Applications in Healthcare

• Predictive analysis: The predictive analysis model predicts a patient's condition. It examines various correlations between symptoms, behaviors, and diseases and makes pertinent predictions. It also enables healthcare to build predictive models using data science, allowing it to identify potential risks before they occur.

• Medical image analysis : It is the most common application of data science in the healthcare industry. X-ray, MRI, and CT scan techniques reveal the human body's inner workings. Deep learning has made it possible to identify flaws in the human body and assist doctors in developing effective treatment options.

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• Drug Discovery: Today, pharmaceutical companies rely heavily on DS to develop better patient drugs. They derive insights from patient information, such as mutation profiles and metadata, which aids in the development of models. On the other hand, collecting historical data can help to improve drug discovery procedures.

•  Genomic: Genomic research was a time-consuming task before the development of data analysis techniques. However, data science in healthcare has made it simple and easy. Researchers are now using DS to study DNA sequences to discover the link between the parameters contained within them and the diseases transmitted by genes.

What should healthcare data scientists expect in terms of pay?

Today, healthcare organizations are heavily investing in data science and analytics because it helps them reduce administrative costs, reduce fraudulent payments, deliver more accurate treatments and diagnostics, and make better overall decisions.

Like any other industry, a healthcare data scientist's salary is typically determined by qualification, skill set, experience, location, and organization.

Data scientists in healthcare and life science companies can expect to earn around Rs. 40 LPA on average.

Conclusion:

I hope this blog has given you a better understanding of the importance of domain expertise in healthcare. Want to become an analyst or data scientist in the Healthcare sector?

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