Introduction
Did you know, there are over 3 Billion Internet users in the world and we humans produce 2.5 billion bytes of data every day? That is a lot of data. So, all of this data contributes to a vast pool that consists of information about each one of us. From our streaming patterns to our medical history and what we buy last on Amazon, every little information like this is recorded.
This information is frequently processed through algorithms to simulate and predict the occurrence of any event virtually. This is when Data Science in healthcare comes into the picture. Data science provides the necessary environment for the processing of an enormous amount of data.
What Exactly Is Data Science?
Data science isn’t some rocket science to explain. To be precise, data science is a technology that predicts an event with the help of processing data. Data such as what websites you visit, to categorize ads for you, and what shows you frequently watch, to prepare an OTT watchlist, and so on.
Data science doesn’t process a couple of kilobytes but deals in hundreds and thousands of them to be as precise as it can. Data science is rapidly expanding and growing into something more than user behavioral prediction.
Data science for healthcare: A big why?
- Time-effective approach
- Better accuracy
- Resource-friendly
Healthcare is at the top of the list of industries that might benefit from potential data science applications. Amidst the worldwide crisis of the pandemic, the need to rely on data science to find effective treatments is more crucial than ever.
With an insufficient number of health workers, lately procured medical history, and untimely diagnosis of diseases, there have been several blunders that could have been avoided. Data science for healthcare might have a solution for all of this and can be the next big thing in the healthcare industry. So, technology in the healthcare industry is changing faster than ever before.
How Is a Revolution In Healthcare Taking Place?
- The Coronavirus pandemic has made us revamp healthcare
- The healthcare industry is overburdened now more than ever
- Need faster results, better treatments
Epidemiologists predict this may not be the last pandemic humanity has to face, and there are undoubtedly more pandemics to come. These unprecedented times demand better preparation, and the whole world is going through a historical healthcare revolution.
From virtual doctor’s appointments to getting medicines delivered online, the Internet is an essential part of the healthcare system. Now is the time to introduce technologies such as Data Science, Artificial Intelligence, and Machine Learning to the traditional medicinal industry and be better prepared for the challenges to come.
So, can it be the carrier of the revolution? And more importantly, how can data science be used in healthcare?
Applications of Data Science for Healthcare
Medical Imaging
Around 90% of the aggregate medical data is provided with the help of image diagnosis. X-rays, MRIs & CT scans are some of the most important medical imaging techniques. With the help of data science for healthcare, we can attain better and faster results from data-driven image analysis to diagnose a severe disease. This will save time, which is a crucial factor when it comes to saving lives.
- Image processing algorithms can be used to enhance, declutter, and segment images.
- Deep-learning-based algorithms, backed with Hadoop, can be used to derive insights from a series of medical images.
Drug Discovery
Discovering medicines, drugs, and any other non-invasive medical substance is a long process. Introducing data science for healthcare can significantly reduce the time involved and make the process resource-friendly and economical.
- Use of computational biological models to predict the effects of various drugs
- Data-driven analysis to identify exact drug compositions
Genomics
Genomics, or the simple study of genome sequences, helps understand the DNA of a disease. Scientists can then figure out a cure/vaccine to fight off. Data science can help in getting better and personalized results from a similar disease DNA to different individuals. This will lay down a path for personalized healthcare.
- MapReduce can save time for the mapping of Genome Sequencing
- SQL for manipulation and computation & retrieval of data out of research database
Monitoring Patient Health
Smart wearables are seen everywhere nowadays. The human body generates over two terabytes of data per day. This data includes activities of the brain, stress level, heart rate, sugar level, and much more. Data science can process all of this data for better monitoring of a patient’s health and keep the healthcare professional aware of it.
- Big data-based continuous monitoring
- Algorithms to detect any sudden/improper change in vitals
Predicting, Tracking & Preventing Diseases
Do you ever wonder what it would be like to know in advance if we were to catch a disease? Data science for healthcare can make it happen. Data science can process your medical data and convert it into a prediction-based model to help you know what conditions you are prone to. On a global level, early Cancer Detection & Diabetic Retinopathy is saving countless lives with the combination of data science and healthcare.
- Machine learning for cancer detection & EEG analysis
- Monitoring the risk of cardiovascular diseases with AI
Bonus: The Cognizant case study
Cognizant Healthcare conducted a study in 2017 to identify how data science for healthcare can be used to improve patient care and satisfaction. This study features the application of data-driven insights to subjugate the essential probes faced by a patient. Every hospital patient in the U.S. is requested to fill out a survey — the Consumer Assessment of Healthcare Providers and Systems (CAPHS) — to describe their overall experience and rate the hospital’s efficacy in providing the best care.
In 2017, Cognizant was asked to analyze its CAHPS data using advanced data science technologies. The aim was to fully understand the patient’s needs to improve the CAHPS rating and develop a better, more personalized patient care system.
This study analyzed feedback from each of the 60,000 patients Cognizant Healthcare served, and this is what the outcome was:
- Recommendations for customized patient servicing
- Significant improvement suggestions for their CAHPS scores
- Identified the factors leading to lower patient satisfaction
- Classification and insights on patients based on clinical and demographic trait
What Is There In the Future?
With things still growing and developing, the future is quite hopeful. The field of data science is advancing, which will only lead us to better healthcare and save as many lives as possible. Here are some aspects that aren’t yet visible but soon can be a massive thing as data science for healthcare evolves:
- AI-enabled robots
- Faster clinical trials
- Better administrative task management
- Reduced treatment costs and duration
- Better handling of large population
These prospects aren’t entirely invisible right now. AI-based robots were seen taking vitals of COVID-affected patients in Japan, whereas various mobile or web applications provide virtual assistance for minor health issues. There is hope for betterment, and that is our silver lining to watch out for with data science for healthcare.
Let’s Sum up
So, after this, we can quickly conclude that data science for healthcare has endless potential and possibilities for exploring new ideas. The healthcare industry is developing, and data science and other technologies have a lot to offer.
The whole healthcare industry has come to mainstream attention after the system has collapsed worldwide against the current pandemic. We should take this as an early call to put more effort into the betterment of healthcare, be it data science or research academia, or any other important factor. Adios, until next time! Do comment if you think there are more potential future aspects that we missed!
