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.

Researchers

  • Suzanne B. Bakken, PhD, MS, BSN, FAAN, FACMI, FIAHSI

    • Professor of Biomedical Informatics
    • Alumni Professor of the School of Nursing

    Research Approaches: Informatics, Interdisciplinary, Precision Health
    Research Interests: Latino, Symptom Science, Self-Management, Informatics, Information Visualization, Precision Health

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  • Kenrick Dwain Cato, PhD, FAAN

    • Assistant Professor of Nursing

    Research Approaches: Clinical, Dissemination and Implementation, Informatics
    Research Interests: Sexual and Gender Minority Populations, Infection Control, Social Determinants of Health

  • Sarah Collins Rossetti, PhD, BSN, RN, FAAN, FAMIA, FACMI

    • Assistant Professor of Biomedical Informatics and Nursing at the Columbia University Medical Center
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  • Haomiao Jia, PhD, MS

    • Professor of Biostatistics (in Nursing) at CUMC

    Research Approaches: Comparative Effectiveness, Economic Analysis, Health Services and Policy
    Research Interests: Elderly, Suicide, Disability, Mental Health, Social Determinants of Health, Cost-Utility Analysis, Quality-Adjusted Survival Time, Survival Analysis

  • Rachel Yeji Lee, PhD, RN

    • Postdoctoral Research Fellow in the School of Nursing

    Research Approaches: Informatics, Precision Health

    Research Interests: Electronic Health Records, Informatics, Natural Language Processing, Machine Learning

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  • Meghan Reading Turchioe, PhD, MPH, RN

    • Assistant Professor of Nursing

    Research Approaches: Dissemination and Implementation, Informatics, Interdisciplinary

    Research Interests: Data Visualization, Natural Language Processing

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  • Jiyoun Song, PhD, RN, AGACNP-BC

    • Postdoctoral Research Scientist in the School of Nursing

    Research Approaches: Clinical, Informatics, Precision Health
    Research Interests: Home Health Care, Rehospitalization, Early Identification, Cutting-edge Technology, Natural Language Processing, Speech Recognition

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  • Maxim Topaz, PhD, MA, RN, FAAN

    • Elizabeth Standish Gill Associate Professor of Nursing

    Research Approaches: Clinical, Informatics, Precision Health
    Research Interests: Information Technology, Home Care, Patient Prioritization

  • Yihong Zhao, PhD, MPhil, BS

    • Professor of Data Sciences (in Nursing) at CUMC

    Research Approaches: Interdisciplinary

    Research Interests: Substance Misuse, Mental Health Disorders, Brain Image Data Analyses, Genetic Studies, Machine Learning Approaches

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