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Development of a national medical leadership competency framework: the Dutch approach

Published on Nov 28, 2019in BMC Medical Education1.87
· DOI :10.1186/S12909-019-1800-Y
Wouter Alexander Keijser3
Estimated H-index: 3
,
Henricus J.M. Handgraaf1
Estimated H-index: 1
+ 5 AuthorsCeleste P.M. Wilderom28
Estimated H-index: 28
Abstract
The concept of medical leadership (ML) can enhance physicians’ inclusion in efforts for higher quality healthcare. Despite ML’s spiking popularity, only a few countries have built a national taxonomy to facilitate ML competency education and training. In this paper we discuss the development of the Dutch ML competency framework with two objectives: to account for the framework’s making and to complement to known approaches of developing such frameworks. We designed a research approach and analyzed data from multiple sources based on Grounded Theory. Facilitated by the Royal Dutch Medical Association, a group of 14 volunteer researchers met over a period of 2.5 years to perform: 1) literature review; 2) individual interviews; 3) focus groups; 4) online surveys; 5) international framework comparison; and 6) comprehensive data synthesis. The developmental processes that led to the framework provided a taxonomic depiction of ML in Dutch perspective. It can be seen as a canonical ‘knowledge artefact’ created by a community of practice and comprises of a contemporary definition of ML and 12 domains, each entailing four distinct ML competencies. This paper demonstrates how a new language for ML can be created in a healthcare system. The success of our approach to capture insights, expectations and demands relating leadership by Dutch physicians depended on close involvement of the Dutch national medical associations and a nationally active community of practice; voluntary work of diverse researchers and medical practitioners and an appropriate research design that used multiple methods and strategies to circumvent reverberation of established opinions and conventionalisms. The experiences reported here may provide inspiration and guidance for those anticipating similar work in other countries to develop a tailored approach to create a ML framework.
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