The future of precision health is data-driven decision support

In the applied sciences, the ultimate goal is not just to acquire knowledge but to turn knowledge into action. The next wave for data disciplines may be experimental designs and analytical methods for closing the gap between the “real-world” situations faced by decision-makers and their idealized representations in optimization problems, and the health sciences are poised to be the discipline where these developments substantially improve lives. We discuss three recent trends in research—experimental designs and analytical methods for precision medicine and pragmatic trials; technological developments in sensors, wearables, and smartphones for measuring health data; and methods addressing algorithmic bias and model interpretability—and argue that these seemingly disparate trends point to a future where data-driven decision support tools are increasingly used to promote wellbeing.

Duke Scholars

Author Nikki Freeman Biostatistics & Bioinformatics, Division of Biostatistics