Data Science in Healthcare – Applications, Roles and Benefits 

Read it in 0 Mins

Last updated on
13th Jun, 2022
13th Jun, 2022
Data Science in Healthcare – Applications, Roles and Benefits 

Imagine if there was a way for healthcare professionals to process, manage, analyze, and assimilate all your healthcare data to deliver enhanced and more accurate patient care. Today, considerable data related to your health is available at your fingertips. For instance, your patient records, treatment plans, and even smartwatches provide an insight into your health status.

Information from these large datasets, also called big data, drives the field of Data Science. Data science is revolutionizing every sector, including healthcare, IT, media, entertainment, etc., and hence is also looked to as a highly prospective career option. Several platforms today offer Data Science Bootcamp with job placement on completion.

Among the various industries, the use of Data Science in healthcare has become more critical with the rise of smartphone apps that can monitor personal health parameters using sensors and analyzers.

This blog will take you through the importance, vast applications, and benefits of data science in the medical field and the vital role a data scientist plays in healthcare. 

Why Do We Use Data Science in Healthcare? 

A few years ago, we were at the precipice of a world where most people would not have access to primary healthcare and become prone to chronic diseases. Wouldn't that be the end of the world that we feared? 

But fortunately, medical Data Science, with its algorithms and analytical tools, saved the day. Nearly 30% of the world's data volume is generated by the healthcare industry, including Electronic Health Records (EHR), prescriptions, clinical reports, investigations, etc. Data Scientists, backed by certified Data Science training, pool and analyze this voluminous data effectively using machine-learning algorithms. 

Let's understand how vital data science is by going back a few years when doctors and hospitals simultaneously struggled to handle multiple patients. The lack of management would often contribute to poor treatment and worsen patients' conditions. An American study in 2019 showed that 12 million adults are misdiagnosed annually, out of which 28% are life-threatening or life-altering. 

The scenario is far better today due to Data Science in health. For instance, intelligent wearable devices allow doctors to monitor patient health, like brain activity, stress level, heart rate, sugar level, and more. These devices can be connected to various diagnostic equipment in hospitals, which collect real-time patient data and present it to doctors through mobile apps.

As soon as there is an abnormal change in readings, hospitals can immediately send healthcare professionals to these patients' homes. Doctors can also accurately diagnose the patients' condition and help junior doctors or nurses treat patients at home without being there themselves.

This is only one of the applications of Data Science in healthcare. Various fields in the medical and healthcare system use Data Science. So, let's find out more about Data Science applications

Application of Data Science in Healthcare 

Following are some of the most significant applications of data science in healthcare. 

  • Medical Image Analysis 

Healthcare professionals often use various imaging techniques like X-Ray, MRI, and CT Scan to visualize your body's internal systems and organs. Deep learning and image recognition technologies in health Data Science allow detection of minute deformities in these scanned images, helping doctors plan an effective treatment strategy. Some of the commonly used machine learning algorithms include: 

  1. Image processing algorithm: For image analysis, enhancement and denoising. 
  2. Anomaly detection algorithm: For bone fracture and displacement detection. 
  3. Descriptive image recognition algorithm: Data extraction and interpretation from images and merging several images to form a bigger picture.

Furthermore, health data scientists are constantly working on developing more advanced techniques to enhance image analysis. For example, in a recent publication in Towards Data Science, the Azure Machine Learning platform can be used to train and optimize a model to detect the presence of three common brain tumors--Glioma tumors, Meningioma tumors, and Pituitary tumors.

  • Drug Discovery and Design 

Drug research has always been a long and tedious process involving precise causative agent characterization and millions of tests. In drug discovery, it all comes down to clinical trials that often fail to meet their objectives, resulting in delays and increased costs.

Unfortunately, there is always a demand for medicine or vaccines as harmful agents and humans evolve in tandem. Data science and automation help professionals screen millions of compounds to identify drug candidates for trials within months or weeks. Data Science algorithms also help simulate how drugs will act in the human body, reducing the need for long laboratory experiments.

The advancements in Data Science have changed the clinical trials landscape. A Clinical Trials Arena analysis predicts that the frequency of decentralized/virtual clinical trials (DCT) will show a 28% increase from 2021. This implies that several tested vaccines or drugs can be launched every year successfully. 

  • Predictive Analytics in Healthcare

A predictive analytical model utilizes historical data, finds patterns from the data, and generates accurate predictions. The data could entail anything from a patient's blood pressure and body temperature to sugar level.

Predictive models in Data Science correlate and associate every data point to symptoms, habits, and diseases. This enables the identification of a disease's stage, the extent of damage, and an appropriate treatment measure. Predictive analytics in healthcare also helps: 

  1. Manage chronic diseases 
  2. Monitor and analyze the demand for pharmaceutical logistics 
  3. Predict future patient crisis 
  4. Deliver faster hospital data documentation
  • Tracking Patient Health

Did you know that the human body generates 2TB of data daily? Data scientists for public health have developed wearable devices that allow doctors to collect most of this data like heart rate, sleep patterns, blood glucose, stress levels, and even brain activity. With the help of data science tools and machine learning algorithms, doctors can detect and track common conditions, like cardiac or respiratory diseases.

Data Science tech can also detect the slightest changes in the patient's health indicators and predict possible disorders. Various wearable and home devices as a part of an IoT network use real-time analytics to predict if a patient will face any problem based on their present condition.

  • Virtual Assistants

One of the best applications of Data Science and healthcare is developing a comprehensive virtual platform. These platforms act as a patient's personal assistant. All a person must do is input their symptoms, and the application will predict the disease and patient condition. These virtual assistants suggest precautions, medication, and a treatment checklist. It also prevents negligence by notifying the patients to take their prescribed medications. 

Woebot by Stanford University is a virtual assistant that helps patients with psychological disorders improve their mental health. 

Role of a Data Scientist in Healthcare 

Now that we know how Data Science can be used, let us learn more about what a data scientist does. The primary role of a healthcare data scientist is to implement all Data Science techniques like probability and statistics, data visualization, machine learning, AI, etc., in the healthcare system.

Other responsibilities of a data scientist include: 

  1. Collecting patient data 
  2. Converting data into understandable insights 
  3. Analyzing hospital needs and supporting decision-making through data 
  4. Data collection, storage, retrieval, and security 
  5. Performing data analytics 
  6. Extracting insights through algorithm implementation 
  7. Collaborating with the development team to develop predictive models

You can enroll in boot camps like KnowledgeHut's data science Bootcamp with job placement to become a data scientist.

Benefits Of Data Science in Healthcare 

Data Science drives the advancement of healthcare facilities and workflow. It has become an essential part of the healthcare system and helps boost productivity in diagnosis and treatment. The healthcare sector benefits from Data Science in the following ways: 

  • Improved diagnostic accuracy and efficiency 
  • Advancement of pharmaceutical research for rare diseases 
  • Optimized clinic performance through actionable insights 
  • Reduced risk of prescription medicine 
  • Decreased hospital readmissions and lower care costs 
  • Lower treatment failures 


The advancements in health are driven by advances in technology, the growth of digital consumerism, and the need to fight increasing costs. And Data Science addressed them all. With nearly 1.2 billion clinical documents being produced in the US alone, life scientists and doctors have a sea of data to base their research and treatment. Not to mention the volumes of data gathered from wearable and home devices. Collecting, structuring, and processing this high volume of data and further making sense of it is a task only possible through Data Science.

Data Science finds applications in various healthcare vectors and offers benefits to both patients and doctors. And at the cusp of it all are the data scientists who implement their knowledge in healthcare.

Frequently Asked Questions (FAQs) 

Q1. Do data scientists work in healthcare? 

Answer: Yes, data scientists work in various industries, including healthcare. They implement their knowledge of probability and statistics, data visualization, machine learning, and more to enhance healthcare.

Q2. How do I become a data scientist in healthcare? 

Answer: The job profile for a data scientist varies based on the industry. However, some of the basic requirements include a master's degree with skills in statistics, machine learning, programming language R, and Python. 

Q3. Can data scientists work in hospitals? 

Answer: Yes, health data scientists work closely with healthcare professionals in hospitals to collect data from patients and analyze hospital needs. 

Q4. Is healthcare data science a promising career? 

Answer: Definitely, Data Science offers many career options. Healthcare analytics is booming, with the market projected to be worth $75.1 billion in 2026. So, rest assured that as a data scientist, you will earn well. 

Q5. Do data scientists earn more than doctors? 

Answer: Yes, they do. Based on the future trends and the importance placed on the job, a data scientist earns more than many other professions like doctors.


Mounika Narang


Mounika Narang is a project manager having a specialisation in IT project management and Instructional Design. She has an experience of 10 years 
working with Fortune 500 companies to solve their most important development challenges. She lives in Bangalore with her family.