Expanding Latent Tuberculosis Infection Testing and Treatment in Massachusetts Primary Care Clinics via the ECHO Model

JOURNAL OF PRIMARY CARE AND COMMUNITY HEALTH(2022)

引用 2|浏览8
暂无评分
摘要
Introduction/Objectives: In the US, reactivation of latent tuberculosis infection (LTBI) accounts for 80% of new cases. In 2016, the US Preventive Services Task Force provided a new recommendation that primary care providers (PCPs) should conduct LTBI screening, whereas in the past, LTBI cases were evaluated and treated by specialty providers. This shift in care revealed knowledge gaps surrounding LTBI treatment among PCPs. This study assessed changes in PCPs' confidence for performing key aspects of LTBI care before and after participation in an LTBI Extension for Community Healthcare Outcomes (ECHO) course. Methods: The ECHO Model TM is an evidence-based telementoring intervention. Participants were primary care team members from clinics throughout Massachusetts who voluntarily enrolled in the ECHO course. In this mixed-methods evaluation, primary outcomes were PCP self-reported confidence changes by pre-and post-course surveys and post-course semi-structured interviews. Results: Twenty PCPs (43% of registered PCPs) attended at least 3 of the 6 sessions and 24 PCPs (31% of registered PCPs) completed at least one survey. Confidence increased in selecting a test (P= .004), interpreting tuberculosis infection test results (P=.03), and selecting a treatment regimen (P=.004). Qualitative interviews with 3 PCPs revealed practice changes including switching to interferon gamma release assays for testing and using rifampin for treatment. Conclusions: Use of the ECHO model to train PCPs in LTBI management is feasible and efficacious. For continuing medical education, ECHO courses can be lever-aged to reduce health disparities in settings where PCPs' lack of familiarity about a treatment topic contributes to poor health outcomes.
更多
查看译文
关键词
latent tuberculosis infection, Project ECHO, continuing medical education, primary care, program evaluation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要