How is Data Science Changing the Healthcare Industry?

The healthcare industry is one of the most important industries. After all, health is vital to our quality of life and even our survival! And that is why the integration of data science and artificial intelligence into the healthcare industry is so important. This combination can contribute a lot to humanity ranging from creating new drugs to even finding cures to many diseases. Data science is changing the healthcare industry in many ways by improving medical image analysis, providing predictive medication, creating a global database of medical records, etc.  

And that’s just the beginning!  Who knows where this integration of data science and healthcare may lead in the future. So let’s see how all these methods are implemented in healthcare using Data Science as they undoubtedly required highly skilled scientists that have detailed knowledge of both biology and technology.

1. Medical Image Analysis

Image analysis is a very important part of healthcare. This may include images takes using an MRI, X-ray, mammography, CT scans, etc. Usually, there are trained healthcare professionals that read these images and find out if there is something out of the ordinary using the differences in resolution, modality, tone, dimensions, etc. of these images. However, deep learning-based algorithms can be used to analyze data consisting of millions of these images and finding better ways to diagnose them. These may reduce human error in detecting tumors, organ delineation, artery stenosis, etc. and also provides results much faster which may be the difference between life and death. For example, an Amsterdam-based company called Aidence has developed an AI system that can help radiologists detect, quantify, and report the lung lesions from CT scans so that effective treatment can be performed as soon as possible.

2. Genetics and Genomics

Understanding our genes better might be a pathway to understanding diseases better and also how to diagnose and cure them in individuals. So combining data science with DNA research allows scientists to better understand how individual health and wellness are tied to our genes and what is the relation between the DNA, diseases, and the chemicals to treat them. Data science allows scientists to collect and analyze data of genetic issues that occur in individuals in response to diseases and drugs and how better to manage them. Deep Genomics is a company that uses artificial intelligence, data science, and biology to evaluate the effects of various drugs on the disease biomarkers in cell biology to the level of DNA and RNA so that they can obtain drugs with the maximum benefit.

3. Creation of drugs

The process of creating new drugs takes a lot of time in trial and error until those drugs are considered suitable enough for public release. In the USA, it can take around 12 years to get a drug approved by the FDA. However, data science and artificial intelligence can significantly shorten this process so that the best drugs are freely available in the market as soon as possible. The idea is that ML algorithms can predict how the drugs will behave in the body using mathematical modeling and simulations with a high enough accuracy that there is no need to perform all the experiments in an actual lab. Artificial intelligence algorithms can also synthetically test chemical compounds against the data of all possible cell types, genetic mutations, etc. which would provide a clearer picture than ever possible using real worth experimentations.

4. Virtual Assistance for Patients

There are a lot of patients in the healthcare industry and not enough healthcare professionals. Moreover, many patients do have very serious ailments and just need some support. This can be done using data science and artificial intelligence-powered mobile apps that usually use chatbots to provide one on one personal service to patients. These chatbots can provide a basic diagnosis for common symptoms using a data network linking symptoms to causes, answer common questions, create an appointment with a doctor for more serious cases, and also remind patients to take their medication on time. This will reduce the pressure on doctors by solving common problems of patients and also allow them to concentrate on cases that are truly critical and in urgent need of support. For example, Your.MD is a popular medical app that provides wellness tips, details, and symptoms on many existing conditions and also the option of eventually seeing a doctor. It uses technologies like Natural Language Processing and Speech Recognition to interact with patients on a personal level and provide better interactivity.

5. Predictive Medication for Patients

Data science and artificial intelligence can be used to create predictive medication or treatment for patients. This means that algorithms can use various forms of data such as clinical notes, patient data, types of symptoms, habits, diseases, common antecedents, etc. and try to find what the problem is according to the symptoms or what the accurate response should be. The Predicting Individual Outcomes for Rapid Intervention or PRIORI app is a prime example of this. It can predict the mood swings in Bipolar disorder patients so they can be managed accordingly. This is possible because one of the signs of future mood swings is a change in patient speech patterns. So PRIORI can recognize the changes in the speech patterns as they occur and warn the Bipolar disorder patients and their families that a mood swing is about to happen. This could be done using a voice alert on the app that says “Maybe you should talk to your doctor soon”.

6. Managing Patient Data

Modern data science techniques can create comprehensive records of patient data that can be accessed by medical professionals to understand the whole medical history of a patient. Then various machine learning algorithms will be able to use this huge store of patient data to aid in diagnosing diseases by comparing with patients that show similar symptoms. A technology that already makes use of this is CancerLinQ. It uses big data analytics to gather anonymous patient data from all over America so that uniform best practices can be learned from everyone. This allows different cancer clinics to compare their treatments with everyone else and make improvements where necessary. CancerLinQ also supplies this data to researchers so that they can work on eradicating cancer permanently.

It is very important to state that these are not the only ways in which data science is changing the healthcare industry. Data Science and Machine Learning are used in Oncology to train algorithms that can identify cancerous tissue at the microscopic level at the same accuracy as trained physicians. Also, these technologies can be used in Pathology to diagnose various diseases by analyzing bodily fluids such as blood and urine. Various rare diseases may manifest in physical characteristics and can be identified in their premature stages by using Facial Analysis on the patient photos. So the full-scale implementation of data science and its related technologies in the healthcare industry can only enhance the diagnostic abilities of medical experts and ultimately lead to the overall improvement in the quality of medical care all over the world.