Abstract
Data science, machine learning and artificial intelligence applications impact clinicians, informaticians, science journalists, and researchers. Most biomedical data science training focuses on learning a programming language in addition to higher mathematics and advanced statistics. This approach is appropriate for graduate students but greatly reduces the number of individuals in healthcare who can be involved in data science. To serve these four stakeholder audiences, we describe several curricular strategies focusing on solving real problems of interest to these audiences. Relevant competencies for these audiences include using intuitive programming tools that facilitate data exploration with minimal programming background, creating data models, evaluating results of data analyses, and assessing data science research reports, among others. Offering the curricula described here more broadly could broaden the stakeholder groups knowledgeable about and engaged in data science.
Original language | English (US) |
---|---|
Pages (from-to) | 255-263 |
Number of pages | 9 |
Journal | Health Systems |
Volume | 12 |
Issue number | 3 |
DOIs | |
State | Published - 2023 |
Keywords
- Data science
- artificial intelligence
- biomedical research
- clinicians
- health and clinical informatics
- machine learning
ASJC Scopus subject areas
- Information Systems
- Human-Computer Interaction
- Health Policy
- Health Informatics
- Management Science and Operations Research