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Mining reflective continuing medical education data for family physician learning needs
Denice Colleen Lewis1, Pierre Pluye2, Charo Rodriguez3
1University of Ottawa, Canada. denicelewis@hotmail.com.
Journal of Innovation in Health Informatics
|June 28, 2016
Summary
Mining consumer data from continuing medical education (CME) programs can inform CME content creation and program planning. This research explored how physician feedback enhances CME development for better educational outcomes.
Area of Science:
- Medical Education Research
- Health Professions Education
- Continuing Medical Education (CME)
Background:
- Continuing Medical Education (CME) programs are crucial for healthcare professionals' lifelong learning.
- Developing effective CME content requires understanding physician needs and preferences.
- Existing methods for gathering feedback on CME effectiveness are often limited.
Purpose of the Study:
- To investigate the potential of mining consumer data from CME programs for program developers.
- To explore how quantitative physician feedback can inform qualitative insights for CME planning.
- To identify practical applications of CME consumer data in content creation and program development.
Main Methods:
- A sequential explanatory mixed methods research design was employed.
- Quantitative data were collected from family physicians assessing CME content using an information assessment method.
- Qualitative data were gathered from key informants in the CME planning community through a descriptive study.
Main Results:
- Data mining from CME consumers offers significant potential for program developers.
- Physician-generated quantitative data provided valuable insights for qualitative analysis.
- Identified applications include supporting CME content creation, program planning, and personal learning portfolios.
Conclusions:
- Mining CME consumer data is a viable strategy to improve educational offerings.
- Integrating quantitative feedback with qualitative insights enhances CME development.
- The findings support the use of data-driven approaches in continuing medical education.
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