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Development of a national medical leadership competency framework: the Dutch approach
Wouter A Keijser1,2, Henricus J M Handgraaf3, Liz M Isfordink4
1Faculty of Behavioural, Management and Social Sciences (BMS) Change, Management and Organizational Behavior (CMOB), University Twente, Enschede, The Netherlands. wouter@keijser.com.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
Implications:
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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