Catch My Drift: Building Scalable and Sustainable Models for Promoting Clinician Adherence to Evidence-Based Treatments for Eating Disorders - Summary - MDSpire
To propose strategies for enhancing the quality and accessibility of eating disorders training in community settings, particularly focusing on scalable consultation and supervision frameworks.
Approach:
Virtual Training Effectiveness: The article discusses the effectiveness of virtual asynchronous training complemented by expert clinical case-based consultation for training clinicians in Family-Based Treatment (FBT) for anorexia nervosa, as evidenced by high fidelity achieved by participants.
Scalable Consultation Frameworks: It proposes the development and assessment of scalable consultation and supervision frameworks to maintain treatment fidelity over time, addressing the challenges of clinician drift.
AI Integration: The article suggests utilizing artificial intelligence to enhance and assess long-term fidelity in clinician training, with ongoing developments in AI platforms aimed at providing feedback on clinician performance.
Key Findings:
Clinicians achieved high fidelity in FBT through virtual training, with self and expert evaluations.
Modest improvements in fidelity were observed among participants who engaged in case consultation, indicating the potential benefits of ongoing support.
AI platforms are being developed to provide feedback on clinician training and improve fidelity, showing promise for future applications.
Interpretation:
The findings indicate that virtual training can effectively cultivate high fidelity in FBT, but the durability of these results beyond active consultation phases remains unassessed.
Limitations:
The study focused exclusively on clinicians in private practice, which may limit the generalizability of the findings to other settings.
Long-term fidelity assessment beyond the consultation phase was not conducted, leaving a gap in understanding the sustainability of training outcomes.
Conclusion:
The article emphasizes the need for innovative, scalable supervision models and highlights the potential role of AI in enhancing clinician training and treatment fidelity.
