Towards precision well-being in medical education

Abstract

AbstractProblemThe escalating mental health crisis among medical students is often met with generalized solutions that overlook substantial individual variations. Furthermore, an exclusive focus on mental illness tends to overshadow the necessity of fostering the positive aspects of medical trainee well-being. This Innovation Report introduces a novel, data-driven precision well-being approach for medical education that is built on a more comprehensive and individualized view of mental health.ApproachOur approach to precision well-being centers on categorizing medical students into distinct and meaningful groups based on their holistic mental health, enabling the future development of tailored wellness support and interventions. We applied k-means clustering, an unsupervised machine learning technique commonly used in precision medicine, to uncover patterns within multidimensional mental health data of medical students. Using data from 3,632 medical students, we formulated our clusters based on recognized metrics for depression, anxiety, and flourishing.OutcomesOur analysis identified three distinct clusters, each demonstrating unique patterns along the mental health spectrum. Students in the “Healthy Flourishers” cluster expressed no signs of anxiety or depression and simultaneously reported high levels of flourishing, while students in the “Getting By” cluster reported mild anxiety and depression and diminished flourishing. Students in the “At-Risk” cluster expressed high anxiety and depression, minimal flourishing, and increased suicidality. These results represent an integrated, comprehensive empirical model that classifies individual medical students into distinct well-being categories, creating a way for more personalized mental health support strategies.Next StepsThe three-cluster model’s generalizability needs to be improved by incorporating longitudinal data from diverse medical student populations. Integrating physiological markers from wearable devices may improve individualized insights. The model can be used to monitor students’ transitions between clusters, determine influencing factors, form individual risk profiles, and evaluate the effectiveness of personalized intervention strategies stratified by cluster membership.