How an Interagency Smart and Connected Health Program Uses Big Data to Support Personal Health

Guest post by Yanli Wang, PhD, Program Officer, Division of Extramural Programs, National Library of Medicine.

Solving complex biomedical or public health problems demands interdisciplinary collaboration with researchers who are ready to address important biomedical issues. To meet this demand, the National Science Foundation (NSF) and the National Institutes of Health (NIH) joined in 2013 to create the Smart and Connected Health (SCH) program. This NSF-NIH interagency initiative is a cross-disciplinary research program intended to combine technology-based solutions with biomedical and biobehavioral research in support of enabling discovery and optimizing health.

The purpose of SCH is to promote the development and integration of novel, transformative computational methods that effectively collect, connect, analyze, and interpret data from individuals, devices, and systems, including electronic health records. These approaches are both high risk and high reward. In supporting innovation in these areas, scientific and research communities can accelerate how they use computer and information science, engineering, mathematics, statistics, and behavioral and cognitive research to transform health and medicine.

NLM has been an active participant in the SCH program since 2018. NLM brings its steadfast support for the development of technologies, analytics, and models that utilize novel informatics and data science approaches to help individuals gather, manage, and use data and information about their personal health. The SCH program has been growing steadily and has gone through three reissuances; the most recent program, released in 2021, focuses on “Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science.” During this period, participation from NIH Institutes and Centers has also grown significantly by bringing their own specific research interests and focus areas into the fold. Applications for the SCH program are required to propose a research proposal to address a key health problem, carry out the research with an interdisciplinary team, and make fundamental contributions to two or more disciplines. These teams are expected to include students and postdocs.

In 2021, the NIH Office of Data Science Strategy (ODSS) co-funded or supplemented funding for two projects: one designed to optimize patient data collection and management from wearable devices; and the other focused on patient data privacy protection for cloud computing. In 2022, ODSS and the National Cancer Institute co-funded a project to enhance challenging behavior prediction for children with autism using biosensor data and machine learning techniques.

NLM recognizes that these projects need new biomedical informatics and data science approaches to meet the needs of consumers and patients whose health literacy, language skills, technical sophistication, education, and cultural traditions may affect how they find, understand, and use their personal health information. Any novel data science developments must help individuals at every step. NLM has also made it clear that funded research should align with its strategic planning and approaches in support of the FAIR (Findable, Accessible, Interoperable, Reusable) principles of data management.

NLM has funded nine SCH projects including the development of technology that connects audio and radio sensing systems to improve patient care at home, the development of control system engineering that counteracts notification fatigue for heath behavior change examination, and the design and development of longitudinal risk prediction methods that integrate all available data for preterm birth. NLM is looking forward to continuing its contributions to the SCH program so it may continue to bring the benefits of big data research to consumers and patients.