Translating Evidence-based Approaches into optimal Care for individuals at High-risk of ASCVD: Pilot testing of case-based e-learning modules and design of the TEACH-ASCVD study.

Journal of clinical lipidology(2023)

引用 1|浏览24
暂无评分
摘要
BACKGROUND:Atherosclerotic cardiovascular disease (ASCVD) remains the leading cause of death in the United States. Case-based learning using electronic delivery of the modules can educate clinicians and improve translation of evidence-based guidelines into practice for high-risk ASCVD patients. OBJECTIVE:To develop and optimize module design, content, and usability of e-learning modules to teach clinicians evidence-based management in accordance with multi-society guidelines for high-risk ASCVD patients that will be implemented and evaluated in U.S. health systems in the TEACH-ASCVD study. METHODS:Seven e-learning modules were created by a committee of lipid experts. Focus groups were conducted with lipid experts to elicit feedback on case content followed by interviews with a target audience of clinicians to assess usability of the online module platform. Responses from both groups were evaluated, and appropriate changes were made to improve the e-learning modules. Design of the TEACH-ASCVD study is presented. RESULTS:Feedback regarding case content by lipid experts included providing more detailed patient histories, clarifying various diagnostic criteria, and emphasizing clinical best practices based on evidence-based guidelines. The target audience clinician group reported an agreeable experience with the e-learning modules but noted a discordance between the evidence-based guidelines and clinical decision-making in their own practices. Participants felt the modules would help educate clinicians in managing high-risk ASCVD patients. CONCLUSION:Clinicians must be informed of best practices as the field of lipidology continues to evolve. E-learning modules provide a concise, valuable, and accessible mechanism for educating clinicians regarding changes in the field to deliver the best patient care.
更多
查看译文
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