RESUMO
BACKGROUND & AIMS: Quality esophageal high-resolution manometry (HRM) studies require competent interpretation of data. However, there is little understanding of learning curves, training requirements, or measures of competency for HRM. We aimed to develop and use a competency assessment system to examine learning curves for interpretation of HRM data. METHODS: We conducted a prospective multicenter study of 20 gastroenterology trainees with no experience in HRM, from 8 centers, over an 8-month period (May through December 2015). We designed a web-based HRM training and competency assessment system. After reviewing the training module, participants interpreted 50 HRM studies and received answer keys at the fifth and then at every second interpretation. A cumulative sum procedure produced individual learning curves with preset acceptable failure rates of 10%; we classified competency status as competency not achieved, competency achieved, or competency likely achieved. RESULTS: Five (25%) participants achieved competence, 4 (20%) likely achieved competence, and 11 (55%) failed to achieve competence. A minimum case volume to achieve competency was not identified. There was no significant agreement between diagnostic accuracy and accuracy for individual HRM skills. CONCLUSIONS: We developed a competency assessment system for HRM interpretation; using this system, we found significant variation in learning curves for HRM diagnosis and individual skills. Our system effectively distinguished trainee competency levels for HRM interpretation and contrary to current recommendations, found that competency for HRM is not case-volume specific.
Assuntos
Competência Clínica , Gastroenterologia/educação , Refluxo Gastroesofágico/diagnóstico , Pessoal de Saúde , Curva de Aprendizado , Manometria/métodos , Adulto , Feminino , Humanos , Masculino , Estudos ProspectivosRESUMO
The COVID-19 pandemic is an unprecedented time in global history and has many emerging challenges and consequences. While much of the world was focused on the physiological effects and medical interventions or preventions, this article highlights the effects on pediatric mental health. While research is still ongoing, preliminary data suggest a significant impact on the psychosocial wellbeing of the pediatric population. This article hopes to highlight the underlying etiology for this effect and possible mitigations including emphasis on mHealth as well as the future of telemedicine.