Another impact of big data in healthcare is contributing to drug production. Drug discovery, creation, and acceptance is a tedious process regulated by strict protocols. It involves multiple rounds of testing, and the time to market can be rather lengthy. With big data and machine learning, scientists apply data models to predict potential drug effects instead of carrying out actual lab experiments, which are time-consuming.
Moreover, conventional pre-clinical drug testing is performed on animals and is not fully representative of human outcomes. Artificial intelligence and big data in healthcare allow simulating drug effects on virtual humans. When the time comes, big data repositories help researchers locate and recruit suitable patients for advanced stages of drug testing.
Identifying drug molecules
Another use case of big data in medical research is finding the right drug molecules. After scientists identify a biological target, they start researching molecules that can interact with it and produce the desirable effect. A health tech startup, Atomwise, employs deep learning neural networks for medicine discovery. With their software, the company managed to go through 8.2 million compounds
in a few days to find a potential cure for multiple sclerosis.
Organizing medical knowledge
Big data in healthcare analytics also contributes to organizing the vast medical knowledge base. For instance, New Jersey-based Innoplexus developed a discovery tool that arranges medical dissertations, articles, clinical trial documentations, etc. and makes these materials searchable for pharmaceutical companies developing new drugs.