Data Science in Healthcare has been driving massive changes and big news.

The fancy wearable that you purchased yesterday or the online health advice you received from the doctor in Kerala, presents the changing face of healthcare. 

Every time you booked an appointment with your physician through an app or downloaded your clinical reports, a huge amount of data was generated.

With data comes the need to analyze it and put it to the best use of the masses so that they get improved care and attention and access to the modern facilities.

This is exactly what Data Science is!

With numbers increasing rapidly, Data Science in Healthcare offers tools and techniques that usher in a revolution in the sector.

Does Healthcare Need Data Science?

News pieces paint a grim picture of healthcare conditions. With doctors making a diagnosis, chances of human error increases. 

It is not always that a doctor is wrong but chances of interpreting, say usual stomach pain to be that of appendicitis, can’t be completely ruled out.

Now picture this- According to a report, 72% of people use the web to seek health information. This might be to search for a disease, seek consultation, order medications, etc.

The two above scenarios call for a technological intervention that will improve the healthcare scenario and impact many more lives. Data Science comes to the rescue.

Why Data Science in Healthcare?

To keep up with the pace of technological advancements in the healthcare sector; new diseases, health symptoms and conditions coming up and the increasing complexities in a diagnosis and thereafter, it is imperative for healthcare and Data Science to work in unison.

Much work has already been done with Data Science in Healthcare and constant improvements are always being worked on.

Let’s go through some of the applications of Data Science in Healthcare that show how the former has transformed and help refine the sector.

1. Drug Development

Development of new and effective drugs is not just a critical task but also involves a lot of research and expenditure. When the stakes are so high, the reliance of the pharma industry on Data Science is quite understandable.

Don’t expect a new drug to pop up at any time. A lot of time goes into developing a new drug.

– What if it doesn’t produce the desired results?
– Will it be suitable for all age groups or a specific one?

All these and many more crucial points are taken into consideration and then the process of drug development is undertaken.

Because the world of drug development rests on predictions, machine learning algorithms and data science are transforming this and ensuring an improved success rate.

The researchers use the information from patient metadata and mutation profiles to build models and establish relationships between the variables. Even the deep learning algorithms have a part to play here. They predict whether a disease will develop in a human system.

2. Analyzing Medical Images

Medical imaging is one of the most important uses of Data Science in Healthcare. The X-ray that your doctor took when you were having a bad cough; CT scan done to detect the brain’s condition after a terrible road accident or MRI done to diagnose spinal cord abnormalities are all imaging techniques.

You must have seen a doctor examining the X-ray or MRI and then suggesting the medication.

– What if a microscopic deformity went unnoticed?
– Can you even imagine how life-threatening this might be?

Thanks to the Deep Learning technologies, any deformity in the scanned images cannot go unnoticed.

Image Segmentation is one such technique that will look for defects in the scanned images. Image enhancement and reconstruction, image recognition with SVM and edge detection are some of the other image recognition techniques.

3. Predictive Analysis in Disease Prevention

Early detection of chronic diseases is now possible with the use of tools from predictive analytics.

This becomes a crucial application of Data Science in Healthcare as diseases are at times not detected at a preliminary stage. This negligence might prove to be fatal for the patients and also come heavy on the monetary part.

The delayed the curing process, the more are the curing expenses.

This means that by reducing the time of diagnosis and treatment, Data Science also helps make Healthcare less expensive.

4. Virtual Assistant for Patient Support

What is the need of the changed healthcare system if a patient has to visit the doctor every time he/she needs consultation?

The enhanced healthcare lets patients use an app for all their medical needs- booking an appointment/follow-up, consultation, etc.

The doctor is now nearer to the patient. AI-powered apps reach out to the patients for simple diagnosis’ support as chatbots.

Just detail your symptoms or put forth your queries and you will get key information about your medical condition. This is done through the vast sea of information that links symptoms to causes.

Apps are also helpful in reminding you to take your medicines on time and book an appointment with a doctor if required. This is a step that aims at promoting a healthy lifestyle and discouraging sedentary one. Machine learning algorithms are the savior for a patient and offer a personalized experience, based on his/her condition. 

5. Genomics

The Human Genome Project has reduced the expenditure and time spent on genes’ sequence analysis. Modern Data Science tools have now made it possible to analyze the human genes and draw insights in an effective way.

A research scientist analyzes genomic strands and inspects it for any deformity in it. What follows is establishing a relationship between a person’s genetics and his health.

Along with detecting the disease, genomics related research also means analyzing the drug and its effect on a specific genetic tissue.

Did you hear about Bioinformatics?

It is a discipline that combines genetics and Data Science. SQL and MapReduce are few such data science tools that make the genetic sequence research process less time-consuming.

Genomics is a vast field and the deeper a researcher goes, the more unfathomable it gets, leaving more scope for research and thus benefiting the patients.

6. Predictive Analytics and Healthcare

Predictive analytics is optimally applied in healthcare.

A model based on predictive analytics searches for different combinations, linkage of symptoms, diseases and then predicts useful findings.

Improving the efficiency of pharmaceutical logistics and supply chains, playing a crucial part in bettering patient care and effective management of chronic diseases are the core focus areas of predictive analytics in healthcare.

Do you know what is the core health topic to rise in popularity, in predictive analytics?

Population health management, a data-driven approach that aims at preventing common diseases is a famous topic.

What’s there for hospitals? They can predict that the health of a particular patient will worsen. Once this is known, preventive measures can be taken and the patient’s health can improve after administering early treatment.

7. Supervising Patient’s Health

All the wearable devices to count steps, monitor heartbeat, track blood sugar, measure blood pressure, etc. are IoT devices.

These are built with an aim to supervise patient’s health. The data collected from these devices is analyzed and the results are used to predict symptoms, advise health and medical procedures and track patient’s progress.

This comes in handy for patients suffering from chronic diseases like diabetes and heart ailments. The specifically crafted devices make use of real-time analytics and thus monitor and supervise patient’s health.

Healthcare is one industry that has managed to make the most of the developments in the data science industry. After all, the question is the health of the masses.