By Patti Brennan
Data-powered health heralds a revolution in medical research and health care.
Data-powered health relies upon knowing more—more input in the moment, more details across systems, more people (and their data) contributing to the overall picture.
Data-powered health ushers in a new biomedical research paradigm in which patient-generated data complements clinical, observational, and experimental data to create a boundless pool we can explore. New tools based in text mining, deep learning, and artificial intelligence will allow researchers to probe that vast data pool to isolate patterns, determine trends, and predict outcomes, all while preserving patient privacy.
As a result, data-powered health promises personalized health care at a level never before seen. It signals a time when tracking one’s own health data becomes the foundation of personal health management, with sensors—coupled with something like a smartphone—delivering tailored, up-to-the-moment health coaching.
The National Library of Medicine will play an important role in the future of data-powered health. Each of our divisions has something to contribute. NCBI’s identity and access management systems will ensure a solid core for the NIH gateway to data sharing. Researchers in the Lister Hill Center can apply machine learning, computational linguistics, and natural language processing to make sense out of large, diverse data sources, whether that’s the text within medical records or large numbers of X-rays. Library Operations staff will manage the extensive terminologies that support the necessary interoperability. Specialized Information Services’ experience with disaster information management will help us ensure data remains available even with limited or no internet access. And the National Network of Libraries of Medicine will continue to partner with libraries across the country to support the public as they join this strange, new world.
Together these and other areas of excellence give NLM a solid foundation, but NLM itself must grow and develop to become the NIH hub for data science. We must develop data management skills and knowledge among the Library’s workforce. We must also partner with the other NIH institutes and centers, and with scientists around the country, to complement, not duplicate, data science efforts; to build the technical infrastructure for finding and linking data sets stored in the cloud; to shape best practices for curating data; and to craft policies that support exploration and inquiry while preserving patient privacy.
The ultimate goal is for NLM to do for data what we have already done for the literature—formulating sound, systematic approaches to acquiring and curating data sets, devising the technical platforms to ensure the data’s permanence, and creating human and computer-targeted interfaces that deliver these data sets to those around the world who need them.
We continue to discuss how best to create an organizational home for data science at NLM, and I welcome your ideas. How would you establish a visible, accessible, and stable home for data science at NLM while building upon our expertise and our tradition of collaboration?