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.

中文翻译：

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制定国家医疗领导能力框架：荷兰方法


医疗领导力 (ML) 的概念可以提高医生参与更高质量医疗保健的努力。尽管机器学习越来越受欢迎，但只有少数国家建立了国家分类法来促进机器学习能力教育和培训。在本文中，我们讨论了荷兰机器学习能力框架的开发，其目标有两个：解释框架的制定以及补充开发此类框架的已知方法。我们设计了一种研究方法，并根据扎根理论分析了多个来源的数据。在荷兰皇家医学会的推动下，由 14 名志愿者研究人员组成的小组在 2.5 年的时间里进行了会面，以执行以下任务：1）文献综述； 2）个别访谈； 3）焦点小组； 4）在线调查； 5）国际框架比较； 6）综合数据综合。该框架的开发过程从荷兰的角度对机器学习进行了分类描述。它可以被视为由实践社区创建的规范“知识制品”，由 ML 的当代定义和 12 个领域组成，每个领域都需要四种不同的 ML 能力。本文演示了如何在医疗保健系统中创建一种新的 ML 语言。我们的方法能否成功地获取与荷兰医生领导力相关的见解、期望和要求，取决于荷兰国家医学协会和全国活跃的实践社区的密切参与；不同研究人员和医疗从业者的自愿工作以及适当的研究设计，使用多种方法和策略来避免既定观点和传统观念的影响。 这里报告的经验可能会为那些期待在其他国家开展类似工作的人提供灵感和指导，以开发定制的方法来创建机器学习框架。