使用 DAPMAV 框架揭示社交媒体中医疗领域的患者报告体验
Revealing Patient-Reported Experiences in Healthcare from Social Media using the DAPMAV Framework
AI总结：
本文提出 DAPMAV 框架，用于从社交媒体文本中捕获并分析患者报告体验，并通过前列腺癌社区案例验证其在主题、叙事与情绪分析上的价值。
摘要：
在以患者为中心的护理方法中，理解医疗领域的患者体验正变得日益重要，也受到医疗专业人员的期待。社交媒体上的医疗讨论为获取患者报告体验的独特视角提供了机会，可补充传统调查数据。这些社交媒体报告通常表现为患者在医疗系统中就诊历程的第一手叙述，其细节超出了结构化调查的限制，并且规模远大于焦点小组。然而，与社交媒体上患者体验数据的广泛存在及其所提供的潜在收益相比，由于文本分析所需的技术能力，这类数据吸引的研究关注相对较少。本文提出 Design-Acquire-Process-Model-Analyse-Visualise（DAPMAV，设计—获取—处理—建模—分析—可视化）框架，以概述相关技术，并提供一种从社交媒体数据中捕获患者报告体验的方法。我们将该框架应用于来自 /r/ProstateCancer 的前列腺癌数据案例研究，展示该框架在捕获患者关切的具体方面（如性功能障碍）的价值，概述相关讨论，并呈现这些故事中的叙事和情绪演进。我们预计该框架可应用于医疗领域的多种场景，包括捕获并区分少数群体、地理边界和疾病类型之间的体验。
英文摘要：
Understanding patient experience in healthcare is increasingly important and desired by medical professionals in a patient-centered care approach. Healthcare discourse on social media presents an opportunity to gain a unique perspective on patient-reported experiences, complementing traditional survey data. These social media reports often appear as first-hand accounts of patients' journeys through the healthcare system, whose details extend beyond the confines of structured surveys and at a far larger scale than focus groups. However, in contrast with the vast presence of patient-experience data on social media and the potential benefits the data offers, it attracts comparatively little research attention due to the technical proficiency required for text analysis. In this paper, we introduce the Design-Acquire-Process-Model-Analyse-Visualise (DAPMAV) framework to provide an overview of techniques and an approach to capture patient-reported experiences from social media data. We apply this framework in a case study on prostate cancer data from /r/ProstateCancer, demonstrate the framework's value in capturing specific aspects of patient concern (such as sexual dysfunction), provide an overview of the discourse, and show narrative and emotional progression through these stories. We anticipate this framework to apply to a wide variety of areas in healthcare, including capturing and differentiating experiences across minority groups, geographic boundaries, and types of illnesses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
