Exploring patient information needs in type 2 diabetes: A cross sectional study of questions

This study set out to analyze questions about type 2 diabetes mellitus (T2DM) from patients and the public. The aim was to better understand people’s information needs by starting with what they do not know, discovered through their own questions, rather than starting with what we know about T2DM and subsequently finding ways to communicate that information to people affected by or at risk of the disease. One hundred and sixty-four questions were collected from 120 patients attending outpatient diabetes clinics and 300 questions from 100 members of the public through the Amazon Mechanical Turk crowdsourcing platform. Twenty-three general and diabetes-specific topics and five phases of disease progression were identified; these were used to manually categorize the questions. Analyses were performed to determine which topics, if any, were significant predictors of a question’s being asked by a patient or the public, and similarly for questions from a woman or a man. Further analysis identified the individual topics that were assigned significantly more often to the crowdsourced or clinic questions. These were Causes (CI: [-0.07, -0.03], p本研究旨在分析患者与普通公众提出的关于2型糖尿病（type 2 diabetes mellitus, T2DM）的相关问题。本研究的核心目标是从人们通过自身提问所暴露的未知内容入手，以此更好地理解民众的信息需求，而非先基于我们对2型糖尿病的既有认知，再探索向患病或存在患病风险人群传播相关信息的路径。 本研究共收集两类问题：120名糖尿病门诊患者提出的164个问题，以及通过亚马逊机械 Turk（Amazon Mechanical Turk）众包平台征集的100名普通公众所提出的300个问题。 研究团队共识别出23个通用及糖尿病专属主题，以及疾病进展的5个阶段，并以此为依据对所有问题开展人工分类。 通过统计分析，研究明确了哪些主题可显著预测问题的提问主体为患者或普通公众，同时也明确了哪些主题可显著预测问题的提问者为女性或男性。进一步分析则识别出在众包问题或门诊患者问题中被显著更多分配的单个主题，其中包括病因（Causes，置信区间：[-0.07, -0.03]，p值未完整提供）。