The Rise of Big Data in Medicine



In the healthcare industry, there are always large volumes of data coming in. Health organizations and hospitals use Electronic health records but electronic health records (EHR) alone collect huge amounts of data. But neither the volume nor the velocity of data in healthcare is truly high enough to require big data today. Modern work with health systems shows that only a small fraction of the data tables in an electronic medical record database is relevant to the current practice of medicine and its corresponding analytics use cases. So, the vast majority of the data collection in healthcare today could be considered recreational. Although that data may have value down the road as the number of use cases expands, there aren’t any real use cases for much of that data today. One of the most exciting implications for big data in healthcare is that providers will be able to deliver much more precise and personalized care. With a more complete, detailed picture of patients and populations, they’ll be able to determine how a particular patient will respond to a specific treatment, or even identify patients at risk before a health issue arises.

One major advantage of Big Data in Medicine is put simply, the availability of more information. More information yields more granular diagnosis, which creates the opportunity for more precise treatment. There are healthcare companies and hospitals that are already taking advantage of what big data has to offer. The role of big data in medicine is one where it can help build better health profiles and better predictive models around individual patients so that doctors and healthcare workers can better diagnose and treat disease.

 

One of the main limitations of medicine today and in the pharmaceutical industry is the lack of in-depth understanding of the biology of disease. Big data comes into play around aggregating more and more information around multiple scales for what constitutes a disease—from the DNA, proteins, and metabolites to cells, tissues, organs, organisms, and ecosystems. Those are the scales of the biology that need to be modeled by the integration of big data. When health researchers do that, the models will evolve, the models will build, and they will be more predictive for given individuals.

It’s not going to be a discrete event that all of a sudden we go from not using big data in medicine to using big data in medicine. It is going to be more of a continuum, more of an evolution. As these researchers begin building these models, aggregating big data, testing and applying the models to individuals, assessing the outcomes, refining the models, and so on, Questions will become easier to answer. The modeling becomes more informed as they start pulling in all of this information. Healthcare practitioners and researchers are at the very beginning stages of this revolution, but this evolution is going to go very fast because there’s great maturity in the information sciences beyond medicine.

Health Systems Without Big Data

 

Most health systems can do plenty today without big data, including meeting most of their analytics and reporting needs. Considering the advantages that big data offers, it is obvious the healthcare industry hasn’t even come close to stretching the limits of what healthcare analytics can accomplish with traditional relational databases—and use these databases effectively is a more valuable focus than worrying about big data.

Currently, the majority of healthcare institutions are swamped with some very pedestrian problems such as regulatory reporting and operational dashboards. Most just need the most basic programming of big data in medicine right now, but once basic needs are met and some of the initial advanced applications are in place, new use cases will and on and on, the system will get upgraded bit by bit.