Big Data and the Precision Medicine Revolution

• 成果类型: Article
• 署名作者: Hopp, Wallace J.; Li, Jun; Wang, Guihua
• 署名单位: University of Michigan System; University of Michigan
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.12891
• 发表日期: 2018
• 页码: 1647-1664
• 关键词: sample selection; allocation; mortality; care; Heterogeneity; surgery; DESIGN; CHOICE; IMPACT

摘要

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.