What’s Next For Data Science In Healthcare?

Every industry has been striving towards automation and applying innovative strategies to their business to gain maximum profitability and success. Data science is being used by varied industrial domains, but its usage in the healthcare sector has drastically transformed the overall healthcare ecosystem.

Traditionally, we only relied on the doctor’s manual prescriptions which were suggested by questioning or assuming our symptoms. This led to a wrong diagnosis and thus wrong prescriptions leading to complications. The introduction of data science has changed the way we perceive this case, now the advanced tools and techniques are used to derive an accurate diagnosis with the least human errors. With the advancement of data science in healthcare, medical professionals need to familiarize and upgrade their skills concerning data science, machine learning, artificial intelligence, business intelligence, virtualization, big data, and more.

Fields using data science in healthcare

Data science is been used in various fields in the healthcare industry. We will focus on diagnosis, disease prevention, patient monitoring, drug discovery, and virtual assistance.

Disease Diagnosis

Data science has played a significant role in the field of medical diagnosis which has helped to accurately interpret images of X-rays, CT scans, MRI, and mammography. This helps to detect the microscopic details that could have missed from the manual examination by any medical professional. This helps to reduce the misdiagnosis leading to wrongly prescribed medicines. According to BBC, there are 40,000 to 80,000 cases of deaths in the US reported annually due to the diagnostic errors. (Dusenbery). Data science plays a major role in detecting tumours, irregular heart rhythms, organ anomalies, and cancers through accurate data-driven models. (Pranav Rajpurkar). Data science and Big Data have been used to research in genomics which means to derive results about how a certain drug is used and reacts in treating a genetic disorder.

Disease Prevention

Data science plays an important role to detect and alerts well in advance about a potential disease. It makes use of predictive analysis to detect and prevent various chronic and auto-immune disease at an early stage. The predictive analysis uses the patient’s history to derives meaningful predictions by correlating their symptoms, habits, and diseases. The predictive analysis model also helps to optimize costs in healthcare. Since the disease is diagnosed at an early stage, it requires less cost for treating it as compared to advanced stages. Companies like IQuity are using data science and machine learning techniques for treating autoimmune diseases.

Monitoring Patient Health

Monitoring a patient’s health is one of the important aspects of every healthcare system. This monitoring can be for regular health check-ups or critical post-surgery recovery monitoring of a patient. IoT(Internet of Things) integrated with data science has given rise to wearable health monitoring devices. These monitoring devices were developed for empowering patients and raising awareness about their health status. The wearable device helps the patients to be connected with the medical professionals even outside their clinics and in the comfort of their homes. These devices can be worn on the body like accessories and help to track a patient’s biometric details. (Lockie). These devices track patients’ cardia details, blood pressure, calorie intake, physical movements or steps are taken, etc. These present analysis of a patient, helps their doctors with real-time data of the patient thus helping them predict and take the necessary action if in case of major health issues.

Drug Discovery

Drug discovery is a complex process in the healthcare industry. This process requires high financial expenditure and is a very time-consuming process as it needs extensive testing before it can be introduced for public use. The pharmaceutical industry depends on advanced machine learning algorithms and data science techniques to deliver the best medicines. They use data science to research and develop models based on the patient’s metadata. Deep learning algorithms are used to predict the probability of disease in the human body. Thus data science helps researchers to conduct the right tests and come with the most accurate drug discovery results.

Providing Virtual Assistance

Data science helps to provide a virtual platform to help patients get insights about their disease. The patients can use virtual devices and input their diagnoses and disease symptoms to get the desired help. This help can be a virtual chatbot suggesting to them what immediate steps they can take to avoid any emergencies. They can also be connected virtually with their doctors who can help them via video calls to take the necessary precautionary steps. At Stanford University, a therapy chatbot named Woebot helps to provide therapy treatments to depressed patients. (BRODWIN)

So to conclude data science is a boon for the healthcare sector and can help save lives if used in the right way. It can be used to predict and detect various diseases and also provide support to remote areas in curing fatal with the help of advanced AI-enabled technologies.


  1. BRODWIN, E., 2018, January, 25. A Stanford researcher is pioneering a dramatic shift in how we treat depression – and you can now try her new app. [Online]Available at https://www.businessinsider.in/science/a-stanford-researcher-is-pioneering-a-dramatic-shift-in-how-we-treat-depression-and-you-can-now-try-her-new-app/articleshow/62654550.cms[Accessed 13 October 2020].
  2. Dusenbery, M., 2018, May, 29. ‘Everybody was telling me there was nothing wrong’. [Online]Available at https://www.bbc.com/future/article/20180523-how-gender-bias-affects-your-healthcare[Accessed 13 October 2020].
  3. Lockie, M., 2002. Biometric. [Online] Available at: The aim was to raise people to interest in their health status, improving the quality of care, and making use of the new technology capabilities[Accessed 13 October 2020].
  4. Pranav Rajpurkar, A. Y. H. M. H. C. B. A. Y. N., 2017. Cardiologist-Level Arrhythmia Detection With Convolutional Neural Networks. [Online]Available at https://stanfordmlgroup.github.io/projects/ecg/ [Accessed 13 October 2020].
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