Data Science and Health Informatics (DaSHI) Methods Cluster

What is Data Science and Health Informatics? 

Data Science is an umbrella term for techniques used when trying to extract insights and information from data. It is the intersection of: statistics, mathematics, computer design & programming, and involves problem-solving, capturing data in ingenious ways, cleansing, preparing, and aligning the data. Data science methods can relate both to structured and unstructured data.

Health Informatics is the use of information and communication technologies in health care (also known as: eHealth, digital health & biomedical informatics). It Is the intersection of computer science, library science, cognitive science, organizational science and health science (e.g. medicine, nursing, pharmacy etc.).


DasSHI Group
Method Cluster Visioning Event, October, 2017

What are potential areas for methods development within data science and health informatics?

Data Science methods include:      Health Informatics methods include:
  • Machine learning
  • Mathematical modeling
  • New statistical methods
  • Temporal modelling
  • Data linkage
  • Information visualization and visual
  • Information architecture
  • Natural language processing
  • Large database extraction (Big Data)
  • Social network analysis
  • Mining social media data
  • System design to ensure data reuse and information exchange
  • Personal health records, electronic health records, computerized clinical decision support, standardized languages
  • Methods to ensure privacy and security
    of health data
  • Data mining for public health surveillance
  • System design to ensure access hard-to-reach citizens (Digital Divide)
  • User-interface design methods
  • Application of Data Science methods at the point of care




Stakeholder Consultation Report

Themes for Projects

For more information, please see our Themes page.


    • Methods Cluster Update: November 24th, 2017:

    • Methods Cluster Update: June 1, 2018:


    Lead: Dr. Kim McGrail

    Kimberlyn McGrail is Data Director for the BC Academic Health Sciences Network. She is also a Professor at UBC in the School of Population and Public Health and the Centre for Health Services and Policy Research, Scientific Director of Population Data BC, and the PI for the SPOR Canadian Data Platform. Her research interests are quantitative policy evaluation, aging and the use and cost of health care services, learning health systems and all aspects of population data science. She conducts research in partnership with clinicians, policy-makers and the public. Kim is a founding member of the International Population Data Linkage Network and founding Deputy Editor of the International Journal of Population Data Science. She was the 2009-10 Commonwealth Fund Harkness Associate in Health Care Policy and Practice, a 2016 recipient of the Cortlandt JG Mackenzie Prize for Excellence in Teaching, and 2017 recipient of a UBC award for Excellence in Clinical or Applied Research.

    For more information about Kim McGrail, visit our webpage