Big Data and the Precision Medicine Revolution

TLDR

The big data revolution supplies vast amounts of observational health data, offering larger sample sizes and broader patient coverage than experimental studies, yet its high dimensionality and lack of control pose methodological challenges. The study proposes that integrating observational and experimental data using production‑and‑operations‑management methods can uncover treatment heterogeneity and enable precision medicine, making POM scholars essential to this effort.

Abstract

The big data revolution is making vast amounts of information available in all sectors of the economy including health care. One important type of data that is particularly relevant to medicine is observational data from actual practice. In comparison to experimental data from clinical studies, observational data offers much larger sample sizes and much broader coverage of patient variables. Properly combining observational data with experimental data can facilitate precision medicine by enabling detection of heterogeneity in patient responses to treatments and tailoring of health care to the specific needs of individuals. However, because it is high‐dimensional and uncontrolled, observational data presents unique methodological challenges. The modeling and analysis tools of the production and operations management field are well‐suited to these challenges and hence POM scholars are critical to the realization of precision medicine with its many benefits to society.