How to get Actual Value from Health Data

The amount of data that humanity generates is growing exponentially. IDC estimates that in 2021 only, about 75 trillion gigabytes, i.e., 75 zettabytes, of data will be created. With the help of data science, financial, surveillance, and social media companies, among others, analyze this information and derive additional benefits for their businesses for many years to come. Still, not all industries are equally good at adapting to and leveraging the power of data to drive their business.
The healthcare industry generates a massive amount of data that can be used by biotech companies to deliver advanced health tools to hospitals and research labs. However, this industry is quite conservative with its unstructured records management, huge volumes of research, and unique medical cases. Sometimes, it’s hardly possible to use the same treatment methods in seemingly the same cases.

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However, scientists, researchers, and businesses have been hard at work on extracting useful information from health data and showing some positive results. Let’s zoom in on the most effective practices for today.
Medical examination
According to the study by the National Academy of Sciences, Engineering, and Medicine, 12 million adults in the United States are misdiagnosed each year. This situation is fraught with dangerous health consequences. As reported by BBC, 40,000 to 80,000 people in the US die annually from complications from diagnostic errors.
When it comes to diagnosis, data science can be a real game-changer. The market offers a whole range of instruments to quickly analyze X-rays, CT scans, mammograms, and other types of imagery. Machine learning algorithms can learn to interpret images, identify patterns, and detect cancers, bone damage, internal organ abnormalities, and more.
Data scientists have gone even further, making it possible to generate one kind of image from another one. In healthcare, this can be useful when a patient needs multiple procedures, such as computed tomography and MRI, that must be done when planning radiation therapy. To calculate the radiation dose, one has to know the permeability of all tissues through which the X-rays will pass.
To accurately assess the contours of the radiation zones, it’s best to use the information provided by an MRI scan, which is harmless to humans. However, the MRI image does not provide any data about the X-ray permeability of tissues — this info can be obtained using CT scans only. Computed tomography, though, is based on harmful X-rays. On a CT scan, the contours of various soft tissues are less visible, so patients have to do both CT and MRI and then combine the two pictures.
To reduce the level of radiation exposure, especially if the patient is a child, and cut the overall cost of surgical planning, scientists developed a special method to generate synthetic CT images from MRI data. An AI-powered program learns to generate CT scans based on existing MRI scans. As a result, the patient undergoes one procedure instead of two, which cuts down the time and costs of the examination, and, most importantly, the radiation dose.
Another case comes from Stanford University, where data scientists have developed a model to detect heart rhythm problems based on ECG results. Tests have shown that the algorithm detects abnormalities faster than a cardiologist does
Predictive analytics
Predictive analytics is a major trend in healthcare. Analyzing the health data of millions of people, one could detect correlations and patterns and figure out why some illnesses are more common in a particular location than others. Then, based on the information received, one could identify risk groups and take preventive measures before the predicted outbreak.
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Source: Unsplash
This is what we did when we started developing a solution that predicts drug or non-drug resistant epilepsy. Across the world, about 65 million people suffer from this disease. Sometimes, years pass before doctors find the right drug to help their patients. To develop a system for predicting drug resistance, we used historical data from 450,000 epilepsy patients. Using machine learning, we created an algorithm that predicts drug resistance with an accuracy of 82%. Predictive models like this one can help doctors find the correct drug faster and more accurately.
Here’s another case when predictive analytics was successfully implemented. In 2017, the Philadelphia-based Penn Medicine healthcare system began collecting data from the patient electronic medical history. Over the past three years, it has been using a machine-learning algorithm to make prognostic estimates. The resulting score, based on 30 factors, helps medical staff make a prognosis for the next six months. Ultimately, the system identifies patients with the highest risk of bad outcome upon admission to the hospital. This helps doctors recognize these patterns and actively engage with them.
So, armed with enough high-quality historical data, you can predict almost any case in medicine — from drug prescription to the outcome of a specific treatment.
Pharmacy
Pharmaceutical companies spend up to $2.6 billion to develop a new drug, and it takes 12 years to release it to the market. But now, with all kinds of healthcare data processing applications coming into play, it has become easier. Pharmacy data analytics allows scientists to process hundreds of thousands of clinical trial results in a matter of weeks, simulate the human body responds to a particular drug, and accelerate the development of medicine or vaccine by up to one year. It is data science and machine learning that act as enablers here, having revolutionized the R&D in the pharmaceutical industry.
In 2020, a British startup Exscientia and a Japanese company Sumitomo Dainippon Pharma announced that their machine learning algorithms had invented a drug molecule that would be used to treat obsessive-compulsive disorder. Researchers said that the algorithm developed the drug in just 12 months compared to 5 years it usually takes to undergo human trials.
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Source: Unsplash
Aggregation of research works
Data extraction is a crucial task of natural language processing (NLP) to discover and extract important knowledge hidden in the unstructured clinical data. Every single day, thousands of new medical articles are published on the Internet, describing the nature of illnesses and methods of their treatment. Each scientific work certainly makes a huge contribution to healthcare evolution; each new discovery brings humanity closer to overcoming another disease.
However, there are two sides to the same coin. The main obstacle to the effective use of scientific articles is that there are too many of them, and one keyword search is not enough. As a result, researchers need costly and time-consuming text review. In 2020, Google teamed up with Microsoft, the National Library of Medicine, and the Allen Institute for AI to release the Covid-19 Open Research Dataset (CORD-19). It will enable the global AI community to use text and data mining approaches, as well as NLP techniques to find solutions in response to the pandemic.