Data Science

Nursing researchers in the School generate and use large and complex data sets to evaluate, predict, and protect patient and population health across the lifespan. Collecting and analyzing large data sets offers a unique opportunity for researchers to understand the behavioral, biological, and omic underpinnings of health with the goal of preventing and managing illness. Our faculty have expertise in and are conducting numerous research projects using major national data sets such as the Outcome and Assessment Information Set (OASIS) and other big data from the Centers for Medicare and Medicaid Services (CMS), as well as the National Health and Nutrition Examination Survey (NHANES) and the Minimum Data Set for long term care (MDS).

In addition, funded projects have led to the development of large, rich databases such as those from four NewYork-Presbyterian affiliated Hospitals containing information on hundreds of thousands of patient discharges and the Washington Heights/Inwood Informatics Infrastructure for Community-Centered Comparative Effectiveness Research (WICER) data which continue to be used in ongoing research and secondary data analyses. 

Some examples of areas of data-based research in the School of Nursing include natural language processing technologies to better recognize when nurses first begin noticing subtle indicators of patient decline and big data analytic research which integrates patient-level data with person-level self-reported information, providing a link through which to prospectively conduct comparative effectiveness research studies.