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Data Science in Healthcare: 5 Ways Data Science Reshaping the Industry

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5th Nov, 2019
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Data Science in Healthcare: 5 Ways Data Science Reshaping the Industry

How Data Science Is Changing Healthcare?

The field of medical science sees numerous innovations every year. But now this field is getting disrupted thanks to data science and its applications. There are plenty of data mining applications in healthcare which are transforming the conventional way of medicine and helping researchers, doctors, and patients in getting better results.

data science in medicine

By using artificial intelligence and machine learning, startups are improving research, customer support, and plenty of other aspects of the medical field.

In this article, we’re discussing how data science is transforming the field of healthcare:

Finding Cure for Cancer

Cancer is still among the deadliest diseases known to humanity. And even after decades of research, scientists haven’t been able to find a cure for it. The estimated number of Indians living with cancer is near to 2.25 million. The number of total deaths due to cancer was around 8 lakh in 2018. These figures are frightening, and that’s why finding a cure for this disease is crucial.

However, there are startups around the globe, focusing on advancing the research for this purpose. For example, a startup called BERG Health uses data science and machine learning algorithms for analysis. They analyzed biological samples from around 1,000 patients, and each sample had more than 14 trillion data points. They fed all this information into their AI algorithm and developed BPM 31510.

BPM 31510 detects and kills the cancer-inflicted cells naturally. It’s still under testing, but it’s a huge leap forward in the direction of finding a cure for this fatal diseases. Many startups are focused on finding cures for similarly dangerous diseases such as Ebola. Companies are also using data science for patient monitoring for preventative medicine.

Reducing the Risks of Prescription Medicine

Errors in prescription medicine are one of the leading causes of death in India. Around 50 lakh people die every year because of medical errors. Data science can help in the reduction of such errors and improve the accuracy of prescriptions as well.

MedAware, a startup aiming to solve this issue, is one such example. They provide a self-learning software solution that checks its database for similar cases and helps the doctor in writing the prescription. By using big data, the software helps the doctors in fighting doubt and write more accurate prescriptions. Such applications can save thousands or even lakhs of lives.

Apart from that, it can help in reducing re-admissions and save the time and money of both parties (doctors and patients).

Doing Better Drug Research

Drug testing and research is a costly process. It takes a lot of years and resources to create a drug as it involves a lot of trials, clinical tests, and research. Machine Learning and big data can help in reducing costs and improving the accuracy of these tests.

Big data can contribute to drug research in multiple ways. It can simplify the process and help in predicting the success rate according to the specific biological factors. One can create model simulations for biological networks and optimize the prediction process. This way, it will become easier to find out which trial would be successful.

Data science improves the accuracy of the predictions. It also helps the researchers in choosing the right experiments. Researchers use analogous techniques to predict the possible side effects of the drugs they are testing as well.

Increasing the Accuracy of Diagnosis

Misdiagnosis is a significant issue in the medical field. It leads to millions of deaths, and like the other significant problems in healthcare, it also doesn’t have a simple solution. Many times, doctors make a wrong diagnosis because of inexperience, doubt, or wrong understanding of the case. Data science solutions can help in solving these problems, as well.


One of the most common mistakes leading to misdiagnosis is a wrong interpretation of imaging data. One study published in BioMed Research International, different techniques are removing the difference in the dimension, resolution, and modality of medical images. These applications are mainly helping in improving images obtained through X-ray, mammography, magnetic resonance imaging (MRI), and others.

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Deep-learning algorithms are increasing the accuracy of the interpretation of image data. And the techniques we mentioned before are also helping in improving the quality of these images for further enhancement. iDASH (aka integrating data for analysis, anonymization, and sharing) is a prominent analytical framework, and it’s used for biomedical computing. Hadoop is another framework used in this industry.

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Providing Virtual Assistance to Patients

The number of people visiting hospitals can be drastically reduced by using data science. That’s because many people who visit the hospital or clinic, don’t necessarily need to see a doctor. Their problem can be solved with a simple consultation.

Startups are using data science applications to bring the doctors to the patients virtually. They use mobile apps that ask for the patients’ symptoms and compare the same to its extensive database. After the comparison, the AI-powered app can link the signs to the causes and inform the patient. These apps can also help with simple tasks such as reminding the patient to take a medicine or setting up an appointment as required.

The benefit of such applications is that the patients get help quickly, and the doctors get to focus on more severe cases. Companies also aim to provide better customer support to medical patients through apps. These apps use machine learning algorithms and create a detailed map of the patient’s condition. By using that map, the application can give the customer a personalized experience.

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Concluding Thoughts

As you would’ve noticed, the use of data science in healthcare has led to numerous benefits. From facilitating research to saving costs, it has touched every aspect of this vast sector. This is a major reason why the demand for data scientists is constantly increasing. Medical startups need data scientists to conduct faster research or develop advanced solutions.

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If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s PG Diploma in Data Science and get job on top firms.



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We are an online education platform providing industry-relevant programs for professionals, designed and delivered in collaboration with world-class faculty and businesses. Merging the latest technology, pedagogy and services, we deliver an immersive learning experience for the digital world – anytime, anywhere.

Frequently Asked Questions (FAQs)

1Is there a good demand for health data scientists?

Absolutely, Health data scientists are in demand. We know how the field of medicine demands growth from time to time. Healthcare data scientists create forecasting and modelling software for analyzing medical records and other types of healthcare data. Thus, as a healthcare data scientist, you can contribute towards the growth of the field through your skills and knowledge.

2What are the required educational qualifications to become a healthcare data analyst?

You must have at least a bachelor's degree to work as a healthcare data analyst. A degree in statistics, data science, information technology, or health information management is preferable. Additionally, top-tier firms want people with a master's degree in business administration (MBA). To work as a data analyst, the applicant must have specific licenses and certifications. Different countries accept different certificates and licenses.

3How did data science and analysis prove to be useful in the pandemic?

Various surveys were conducted in different countries. The survey data was then used to create estimations of how individuals of all ages and from various areas of the globe interact in public spaces, schools, businesses, and homes. Their findings gave crucial information to policymakers on how to reduce both virus spread and damage to the economy. Different statistical models were also created in order to better understand how the virus may affect people in the future and, as a result, what sort of preparation is needed.

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