Recommendations for patient engagement in patient-oriented emergency medicine research.

CANADIAN JOURNAL OF EMERGENCY MEDICINE(2018)

引用 16|浏览20
暂无评分
摘要
Objective: To make pragmatic recommendations on best practices for the engagement of patients in emergency medicine (EM) research. Methods: We created a panel of expert Canadian EM researchers, physicians, and a patient partner to develop our recommendations. We used mixed methods consisting of 1) a literature review; 2) a survey of Canadian EM researchers; 3) qualitative interviews with key informants; and 4) feedback during the 2017 Canadian Association of Emergency Physicians (CAEP) Academic Symposium. Results: We synthesized our literature review into categories including identification and engagement, patients' roles, perceived benefits, harms, and barriers to patient engagement; 40/75 (53% response rate) invited researchers completed our survey. Among respondents, 58% had engaged patients in research, and 83% intended to engage patients in future research. However, 95% stated that they need further guidance to engage patients. Our qualitative interviews revealed barriers to patient engagement, including the need for training and patient partner recruitment. Our panel recommends 1) an overarching positive recommendation to support patient engagement in EM research; 2) seven policy-level recommendations for CAEP to support the creation of a national patient council, to develop, adopt and adapt training material, guidelines, and tools for patient engagement, and to support increased patient engagement in EM research; and 3) nine pragmatic recommendations about engaging patients in the preparatory, execution, and translational phases of EM research. Conclusion: Patient engagement can improve EM research by helping researchers select meaningful outcomes, increase social acceptability of studies, and design knowledge transla- tion strategies that target patients' needs.
更多
查看译文
关键词
patient engagement,emergency medicine,knowledge translation,patient-oriented research
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