Navigating Pathways to Diagnosis in Idiopathic Subglottic Stenosis: A Qualitative Study

LARYNGOSCOPE(2024)

引用 0|浏览7
暂无评分
摘要
ObjectiveIdiopathic subglottic stenosis is a rare disease, and time to diagnosis is often prolonged. In the United States, some estimate it takes an average of 9 years for patients with similar rare disease to be diagnosed. Patient experience during this period is termed the diagnostic odyssey. The aim of this study is to use qualitative methods grounded in behavioral-ecological conceptual frameworks to identify drivers of diagnostic odyssey length that can help inform efforts to improve health care for iSGS patients. MethodsQualitative study using semi-structured interviews. Setting consisted of participants who were recruited from those enrolled in a large, prospective multicenter trial. We use directed content analysis to analyze qualitative semi-structured interviews with iSGS patients focusing on their pathways to diagnosis. ResultsOverall, 30 patients with iSGS underwent semi-structured interviews. The patient-reported median time to diagnosis was 21 months. On average, the participants visited four different health care providers. Specialists were most likely to make an appropriate referral to otolaryngology that ended in diagnosis. However, when primary care providers referred to otolaryngology, patients experienced a shorter diagnostic odyssey. The most important behavioral-ecological factors in accelerating diagnosis were strong social support for the patient and providers' willingness to refer. ConclusionSeveral factors affected time to diagnosis for iSGS patients. Patient social capital was a catalyst in decreasing time to diagnosis. Patient-reported medical paternalism and gatekeeping limited specialty care referrals extended diagnostic odysseys. Additional research is needed to understand the effect of patient-provider and provider-provider relationships on time to diagnosis for patients with iSGS.
更多
查看译文
关键词
idiopathic subglottic stenosis,diagnostic pathway,lived experience,referral practices,health behavior
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