Automatic Profiling of Open-Ended Survey Data on Medical Workplace Teaching

Abstract

On-the-job medical training is known to be challenging due to the fast-paced environment and strong vocational profile. It relies on on-site supervisors, mainly doctors and nurses with long practical experience, who coach and teach their less experienced colleagues, such as residents and healthcare students. These supervisors receive pedagogical training to ensure that their guidance and teaching skills are constantly improved. The aim of such training is to develop participants’ patient, collegiate and student guidance skills in a multiprofessional environment, and to expand their understanding of guidance as part of their work as supervisors of healthcare professionals. In this paper, we investigate open-ended answers on guidance experience of 281 healthcare supervisors that participated in these training courses. To automate the analysis of the contents of the answers, we apply clustering to the natural language processed textual data. The results summarize the most common guidance experiences and allow an automatic grouping of the healthcare supervisors’ reflection. Such an evidence-based knowledge can be used to further improve the organization of the training courses.