How big data is disrupting healthcare and life sciences

Data is the lifeblood of any business. And while advances in machine learning have been revolutionizing almost every industry, some have lagged behind due to technical constraints. Healthcare and life sciences rely on information, but when it comes to data, many professionals in this arena are stuck with legacy technologies when conducting their research.

 As John Brownstein, Director of the Computational Epidemiology Group at the Boston Children’s Hospital put it, “We’re missing the data, we’re missing the realtime information, we’re missing the forecasting methods. And we’re missing the consumer tools that feed back to the patient to inform us about what’s happening around us.” The good news is a recent wave of data innovation has been gaining momentum in these industries, with industry veterans, and technology partners developing solutions that are disrupting every layer of the healthcare system.

Data boom for life sciences

Data certainly is not new for professionals in life sciences, since scientific data is the underpinning of everything from pharmaceutical development to medical care. What is new is the realization that old methods of research are becoming obsolete compared to tactics that advanced data analytics and artificial intelligence (AI) are able to offer.

Gaurav Tripathi, CTO at Innoplexus, a data analytics firm that caters to the life sciences explained that the industry was in need of, “a transformation in the nature of their research through a more interdisciplinary approach. Researchers in the industry are moving away from an exclusive approach to data and toward a more open and scientific approach. This innovation is fueling a growing exchange of techniques and meshing of related technologies.”

This integrated approach is being fueled by tech companies that are helping the industry make use of machine learning to make data more useful. Big data does very little for an organization if it doesn’t know how to make use of it. That’s why IT and Data executives at hospitals, pharmaceutical companies, and research organizations, have turned to data firms to help rethink the existing approach to data collection and management.

Intelligence machines

There is a great demand not only for increased access to valuable data but also for tools to automate data collection, archiving, and analysis. Tripathi shares, “Sometimes it seems like in order to access any valuable data you have to be a data scientist. There is a disconnect between the data experts and the subject matter experts that need the information.” That’s why data analytics firms are creating better query results through the use of AI and machine learning.

Put another way, these companies take complicated intelligence efforts and turn them into a systematized, self-operating machine. The result is a user-centric approach to the way data is shared, meaning more people have access to critical research in the field that was previously unattainable. Typically CIO’s and CTO’s in these industries spend much of their time managing enterprise software that manages internal data. With more intelligent machines at their disposal, they can put more of their focus on understanding how technology and data could upgrade operations.

 For professionals in the life sciences and healthcare industry, this represents a significant shift in thinking about technology. Time previously spent on mundane, information-based tasks, can now be shifted to tasks that humans do better, like patient monitoring and care. For researchers, this means less time spent looking for information, and more time to spend on developing cures.

 Searching and researching: the difference

For most people looking to gain insights from data, Google searches and perusing company information will likely help them find what they are looking for. When the impact of the data is minimal, and the need for accuracy is limited, these sorts of searches are adequate. The opposite is true of professionals in any kind of research field, however. When the impact of your data could lead to a life-altering treatment, clinical research, or a new scientific discovery, research tactics are vital, and resources like Google and company intranets fall short of researchers’ needs.

Tripathi explains, “The key to success rests in crawling, curating and indexing hundreds of terabytes of scientific data across hundreds of clinical trial databases, biological databases, major patent offices, congresses, theses, forums and regulatory bodies.” 

Such a task can’t be completed by any search engine, or any human for that matter. The sheer size of the information to sift through makes it impossible. What’s needed is sophisticated artificial intelligence that can determine which information is relevant to any given query. Tripathi explained that AI and machine learning technology are helping medical practitioners find treatments faster, and helping them upgrade their research tactics overall.

Why it matters

For professionals in the life sciences, data has always been important, but in most cases, there are very few databases that allow them to use all of the information available to support research, clinical decision making, and other critical tasks like drug development. Even governments are recognizing and supporting this data revolution. In a statement on why they made their data public the U.S. Department of Health and Human Services explained, “By opening up our data, the idea is to help catalyze the emergence of a decentralized, self-propelled “ecosystem” of innovators.”

As health tech companies are on the rise, both in consumer and medical markets, the need for effective data analysis has never been greater. For executives in the life sciences, it is worth considering what kind of databases and automated tools will serve you best. While archival systems may give you access to a wide library of information, they often fail to include updated research, and the latest in industry developments.

This is why more and more healthcare executives are turning to data analytics firms that emphasize the use of machine learning and AI over older archival research systems. Tripathi explains how these tools will power future innovation, “Data and machine learning will dramatically reduce the time to market for new therapies, reduce the time and investment required for targeting rare diseases, enable precise and more personalized medicines, and automate key processes that will improve efficiency by orders of magnitude.”

For those that effectively use these technologies, the result may be new discoveries and improvements to current operations that would have otherwise remained unrealized.