Towards precision well-being in medical education

Abstract

Medical trainee well-being is often met with generalized solutions that overlook substantial individual variations in mental health predisposition and stress reactivity. Precision medicine leverages individual environmental, genetic, and lifestyle factors to tailor preventive and therapeutic interventions. In addition, an exclusive focus on clinical mental illness tends to disregard the importance of supporting the positive aspects of medical trainee well-being. We introduce a novel precision well-being framework for medical education that is built on a comprehensive and individualized view of mental health, combining measures from mental health and positive psychology in a unified, data-driven framework. Unsupervised machine learning techniques commonly used in precision medicine were applied to uncover patterns within multidimensional mental health data of medical students. Using data from 3,632 US medical students, clusters were formulated based on recognized metrics for depression, anxiety, and flourishing. The analysis identified three distinct clusters. Membership in the ‘Healthy Flourishers’ well-being phenotype was associated with no signs of anxiety or depression while simultaneously reporting high levels of flourishing. Students in the ‘Getting By’ cluster reported mild anxiety and depression and diminished flourishing. Membership in the ‘At-Risk’ cluster was associated with high anxiety and depression, languishing, and increased suicidality. Nearly half (49%) of the medical students surveyed were classified as 'Healthy Flourishers’, whereas 36% were grouped into the 'Getting-By’ cluster and 15% were identified as 'At-Risk’. Findings show that a substantial portion of medical students report diminished well-being during their studies, with a significant number struggling with mental health challenges. This novel precision well-being framework represents an integrated empirical model that classifies individual medical students into distinct and meaningful well-being phenotypes based on their holistic mental health. This approach has direct applicability to student support and can be used to evaluate the effectiveness of personalized intervention strategies stratified by cluster membership.