, 刘冠君, Liu Guanjun
【目的】梳理国内外在线医疗健康领域的主题演化、研究热点和研究方法, 为今后该领域的相关探索和研究提供借鉴。【文献范围】以Web of Science核心数据集为来源, 以在线医疗、在线健康作为关键词进行主题检索, 得到1 899篇相关英文文献。【方法】通过文献计量、聚类分析、纵向映射分析等方法, 对在线医疗健康的国家合作、研究热点、主题演化和研究方法进行深入分析。【结果】在线医疗健康的热点研究集中于互联网医疗健康信息、社交媒体、在线医疗健康社区、电子健康记录等内容, 其主题演化趋势与互联网信息载体的多样化、沟通的便捷化等密切相关。【局限】文献数据仅来自Web of Science核心数据集; 仅以关键词进行主题挖掘和主题演化分析, 未能进行更加深入的探索。【结论】在线医疗健康仍然具有较大的发展空间, 未来可以结合图像识别、深度学习和神经网络等技术进行更加深入的挖掘和研究。
[Objective] This paper reviews the literature of the online health research, and discusses their methods, hot topics, theme evolution, and trends, aiming to provide reference for exploration in the future. [Coverage] The data are selected from Web of Science core collection by using the keywords of “Online medical” or “Online health”, finally 1,899 English articles were retrieved. [Methods] This paper mainly relies on the methods of bibliometrics, cluster analysis and vertical mapping analysis. [Results] Internet medical and health information, social media, online health community, electronic health records are the hot topics which are influenced by the diversification of Internet information and the convenience of online communication. [Limitations] This paper does not retrieve data from other databases and analyze the full text. The results might be biased. [Conclusions] There are still much to be explored in the field of online health. Combined with image recognition, deep learning and neural networks, more researches can be conducted in the future.
医疗卫生行业中, 看病难、看病贵、医疗资源分布不均等问题广泛存在, 但用户对医疗健康服务的需求不断增长。“互联网+”医疗、“健康中国2030”等国家战略的先后出台, 为在线医疗健康行业的发展创造了条件。互联网技术的发展和医疗改革的不断深入, 逐渐改变了传统的医疗服务模式。在线预约、电话咨询、图文问诊等形式极大地提高了患者的就医体验[1]。对于许多慢性病患者和重疾患者而言, 通过网络获取与自身疾病相关的医学知识已成为常态。有调查表明, 在美国使用互联网的成年人中, 有80%的网民通过互联网获取健康信息, 34%的用户会阅读他人发布在网站或博客上的治疗经历, 18%的网民会在网上寻找与自己有相似病症的人, 16%的用户会上网查找其他人对医生的评论或评分[2]。而中国网络信息中心的报告显示, 截至2015年12月, 中国互联网医疗用户规模为1.52亿, 其中诊前环节的互联网使用率最高, 在线健康信息查询、在线预约挂号和在线咨询的总使用率为18.4%[3]。
由于人们不良的生活习惯以及社会老龄化等原因, 慢性疾病和癌症等重大疾病越来越常见, 患者在长期的治疗中需要进行科学的自我健康管理, 而社交媒体和在线医疗健康社区极大地方便了患者获取医疗信息和社会支持。用户可以在互联网中寻求和分享个人的治疗经验和健康知识[4,5], 对各类话题提出自己的观点[6], 同时参与社区互动、进行情感交流以及寻求各种支持等[7]。此外, 各类在线医疗健康服务也缓解了医疗资源匮乏、地区分布不均衡的问题[8]。在线医疗健康的相关研究已经成为众多学者关注的热点。本文从文献的角度出发, 通过聚类分析、主题演化分析等方法, 梳理并总结在线医疗健康领域的发展历程、主题演化、研究热点和研究方法等内容, 为今后的相关探索和研究提供借鉴。
本文选取Web of Science为数据源, 以“Online Medical”和“Online Health”为主题词进行检索, 最终得到1 899条文献记录, 时间范围为1996年-2018年, 共被引频次21 901次, 去除自引的被引频次为19 078次。
文献数量随时间演化是评价某研究领域发展的重要指标之一。对1996年-2018年各年度内的发文量进行统计, 得到在线医疗健康研究的时间分布如图1所示。可以看出, 有关在线医疗健康研究的文献数量呈上升趋势。20世纪90年代, 万维网的发展赋予医疗机构建设和管理网站的能力, 越来越多的医生和患者可以通过电子邮件等形式进行在线交流[11], 这为在线医疗健康研究奠定了基础。2000年-2010年期间, 文献数量稳步上升, 该阶段的研究集中于互联网健康信息使用和用户行为研究, 通常采用问卷调研和访谈的方法开展研究。2010年之后, 随着社会化媒体、在线医疗健康社区的发展和新技术、新方法的成熟, 在线医疗健康研究的文献数量迅速攀升, 成为信息系统领域的一大研究热点, 研究方法以机器学习、文本挖掘等为主。总体来看, 在线医疗健康研究保持稳步的上升态势, 但是论文发表数量趋势逐渐趋于平缓。
在文献数量随时间分布的基础上, 进一步统计在线医疗健康领域的国家和地区分布如表1所示, 以明确该领域的主要研究力量分布。排名前10的国家(地区)的发文数量占比超过91%, 说明该领域研究集中于互联网信息技术水平较为发达的地域。在68个发文国家(地区)中, 美国的发文量为879篇, 占比超46%, 位居第一。紧随其后的有英国、澳大利亚和中国, 占比在7%-9%之间。从被引频次来看, 在发文数量相同的情况下, 中国大陆的文献被引频次要远小于澳大利亚, 仅持平于发文量排名第7的德国。
对在线医疗健康领域的国家(地区)合作关系进行可视化分析, 有助于发现该领域的核心国家及科研合作现状。以节点表示国家(地区), 连边表示国家(地区)间的合作关系, 利用VOSviewer绘制了不同国家(地区)间的学者合作关系网络, 如图2所示。
图2中共包含60个节点, 247条连边, 节点大小取决于节点的度和连边权重, 节点颜色表示合作关系的平均年份。从网络结构可以看出, 美国和英国均处于核心位置, 美国与34个国家(地区)有过合作, 其中与中国、韩国、加拿大和英国的合作关系最为密切; 与英国进行合作研究的国家(地区)有34个, 美国、中国、加拿大是其主要合作国家, 说明英国的合作关系也非常广泛。从合作时间来看, 美国、中国和澳大利亚着色最深, 表示三个国家最早开始在线医疗健康的合作研究, 俄罗斯、南非、巴西、巴基斯坦等国家着色最浅, 说明其合作研究的时间明显晚于其他国家(地区)。
关键词是文献的核心和精髓, 是对文献内容的高度精炼和总结。通过关键词的词共现分析可以有效获取文献的主题分布和演化[12], 从而为在线医疗健康领域的相关研究提供辅助支持。本文选取词频大于5的187个关键词, 基于力导向布局对关键词的词共现关系进行模块化聚类[13], 结果如图3所示。
节点标签为文献关键词, 节点大小与节点的度、连线强度和被引频次有关, 连边表示关键词出现在同一篇文献中, 节点颜色代表其所属的类别, 不同颜色表示节点属于不同的类别。由关键词聚类分布可知, 自1996年以来, 在线医疗健康研究的主题分布广泛且关系密切, 互联网作为实施在线医疗健康的载体, 连接起不同的研究主题。
(2) 聚类2: 社交媒体健康研究, 主要对社交媒体中的医疗健康信息进行挖掘[17], 通过机器学习、文本挖掘等方法对内容和用户评论进行识别和分类等, 以此探究用户情感[18]、满意度[19]、观点和关系网络[20]。
(3) 聚类3: 在线医疗社区研究, 糖尿病等慢性疾病社区是目前主要的在线医疗社区, 这些社区不仅为患者提供社会支持[7], 也赋予他们改善自身健康水平、提高生活质量的机会[21]。此外, 社区网络结构及演化[22]也是学者关注的内容。
在线医疗健康是一个跨学科的研究领域, 其文献分布在Web of Science的120多个学科和领域中。使用SciMAT进行主题演化分析, 可以有效获取“在线医疗健康”领域在不同时期内的热点主题及主题间关系, 从而为该领域的相关探索提供参考。考虑到时间的连续性, 将文献所在年份平均切分为5个连续阶段, 以作者关键词作为分析单位, 以共现作为构建网络的方式, 将等价指数作为规范化网络的相似性度量, 并使用SciMAT提供的简单中心算法作为聚类算法[28], 通过强链接(内部链接)和弱链接(外部链接)构建在线医疗健康不同阶段的主题演化视图, 如图4所示。
在线医疗健康早期关注健康行为、电子医疗记录、疾病诊断和在线社区研究。随着时间推移, 青少年医疗健康成为学者关注的重点, 问卷调查和系统综述的研究方法开始应用。2010年之后, 越来越多的“患者”利用互联网获取信息和社会支持, 从而成为在线医疗健康领域的主要研究对象。社交媒体和在线社区的发展丰富了医疗健康信息的来源, 信息搜索和医患关系的研究也更加广泛。2016年-2018年期间, 健康信息搜索行为、信息沟通、电子健康记录、在线医疗健康服务等内容开始受到关注, 问卷仍然是该阶段最常用的研究方法。从不同时期的对比中发现, 在线医疗健康研究主题分布广泛, 各主题间的交织不断深化, 在线社区研究连续出现在4个时期中, 并在最近三年成为研究热点。此外, 还涉及论坛、应用等不同类型的在线医疗健康社区研究。
从关键词聚类和主题演化中可以看出, 在线医疗健康研究涉及患者、媒体、社区、内容等多个主体, 研究范畴不断扩大, 机器学习、文本挖掘等技术与方法也得到广泛利用。基于关键词聚类分析和不同时期的主题演化分析, 在线医疗健康领域的研究主题可以归纳为5大类: 互联网健康信息行为研究、社交媒体健康研究、在线医疗社区研究、电子健康记录研究、健康隐私信任研究。其中, 基于互联网在线社区的医疗健康研究是近两年的研究热点。
互联网健康信息行为研究围绕信息获取[14]、信息搜索行为[15,29]、信息质量评估[30,31]、信息素养与信息利用[16]等内容。其中, 互联网健康信息的搜索行为研究最为广泛。用户之所以进行搜索行为是因为能够从中获取自己需要的信息或寻找精神安慰。大多数用户因为自身或认识的人被诊断出患有某种疾病而寻找特定类别的信息[29]。文化差异会显著影响用户对于在线健康信息的偏好[25]。
由于互联网健康信息的质量参差不齐, 用户在得到检索结果后通常会对获得的信息进行质量评估。用户会避开具有明显商业性质的网站, 网站设计的专业性与易用性会影响用户对于信息质量的评估[31]。同时信息传播渠道也会影响信息的感知可靠性, 公众对于健康信息传播渠道的偏好依次为网站、博客、个人主页。信息原始来源(医疗专业人员或普通用户)、信息类型、传播渠道三者相互作用, 会对评估特定主题的信息质量产生影响[32,33]。性别也是影响信息可信度的重要因素之一[34], 男性和女性对于信息可信度的判断标准存在差异。对于在线健康信息的使用, 用户会将在线获取的信息与朋友、家人和医生的线下建议相结合, 用以增强个人决策的正确性[35]。
Facebook、Twitter、微博等社交媒体的发展丰富了医疗健康信息的来源, 也为用户获取知识、交流情感和表达个人观点提供了平台。作为全球最大的社交网站之一, Facebook在促进健康方面有巨大潜力[17]。通过对Facebook糖尿病小组的内容进行分析, Zhang等发现用户主要围绕疾病信息、个人情感、社区建设等内容进行互动交流, 并形成一个相互陪伴和支持的社交网络[17]。用户在论坛或社区中发布的医疗健康信息主要来自于社交媒体, 一小部分活跃用户推动话题的讨论和传播[36]。不同社交媒体中信息的可访问性和可读性会影响不同用户群体的感知信任[37]。
此外, 越来越多的医疗卫生组织在社交媒体中发布健康相关的信息或视频, 而社交媒体中的评论和评分反映了受众对于这些活动的观点和情绪。例如, 美国疾病控制与预防中心(Centers for Disease Control and Prevention, CDC)发布的禁烟视频评论挖掘结果显示观众的积极情绪比消极情绪更为普遍[38]。
在线医疗健康社区(Online Health Community, OHC)指能够将病人或医生聚集在一起的互联网平台[39], 医疗专家、病人及其家属、护理者和其他支持者能在这一平台上分享信息并寻求支持[40]。在线医疗健康社区主要提供两种功能: 一是提供医疗健康类的信息, 二是提供社会支持[15]。
很多以社区为导向的健康网站可以为用户提供各类有用的健康信息[41]。在线医疗健康社区中的社会支持包括信息支持(Informational Support)、情感支持(Emotional Support)和陪伴(Companionship)[42]。例如, 信息支持指社区成员之间的信息、建议或指导的传递, 在社区中通常是与疾病和健康相关的转诊信息、建议和个人经历等信息。情感支持指有关理解、鼓励、同情、关心等的情绪表达, 能够帮助降压和减轻焦虑。陪伴指参与者之间的聊天、开玩笑、分享日常生活等行为, 传达的内容中不包含与疾病相关的信息, 它能够加强社区成员之间的联系和社区认同感。通过研究在线医疗健康社区的社会支持对癌症病人健康的影响, 发现在线医疗健康社区确实能够减少癌症病人的焦虑和抑郁情绪[43]。患者之间的互动能够令其原有的负面情绪逐渐转变为积极乐观的情绪, 体现了在线医疗健康社区在情感支持方面的重要作用[44]。对于医疗机构而言, 自建在线医疗健康社区既是服务创新的一种形式, 还可以作为机构知识管理和客户关系管理的工具[45]。
问卷调查是在线医疗健康最早使用的研究方法, 根据研究目的对用户进行问卷调查, 获取用户数据进行统计分析。如通过网上问卷调研, 研究用户的搜索行为[54]、同理心[15]、疾病感知、对医生的信任[55]、满意度[19]以及行为变化[56]等, 还可以针对特定人群进行互联网健康干预[57]。此外, 用户在问卷中披露的信息也反映了他们对在线医疗健康平台或社区的态度或感知。对于不同类型的用户而言, 使用在线医疗健康社区的动机存在差异[58], 同时在线医疗健康社区的信息可用性、用户可用性和知识可用性会影响他们是否持续使用该平台[59], 当患者感知到社区提供的信息没有附加价值时, 会降低他们的社区参与和订阅行为[60]。还有研究者通过线下问卷调研患者对于在线健康信息的感知, 如利用问卷探究门诊病人对于医疗机构提供的网络健康信息的接受度[61]。
随着在线医疗健康信息的日益增长, 传统的人工统计分析方法需要耗费大量时间和人力, 应用此类方法难以在短时间内对海量内容进行分析。随着文本挖掘技术和机器学习方法的广泛应用, 很多研究者已经开始智能化地处理和分析在线医疗健康内容, 如借助现有的自然语言工具, 可以从用户发帖中抽取出与诊疗相关的术语, 从而实现社区帖子的分类。除关键词抽取外, 对社区讨论主题进行分析时还可以采用更为复杂的文本挖掘算法。应用聚类分析方法, 可以对不同在线医疗健康社区中的帖子进行聚类, 分析各社区的热点主题[62]和主题之间的相似性[63], 以及从用户发帖中提取心理语言学特征和内容主题进行分组预测[64]等。
常用的机器学习算法一般分为有监督学习方法、无监督学习方法和强化学习方法[65]三类。在医疗健康领域, 应用最为广泛的是有监督的机器学习方法, 通过人工标注和专家判断等方式, 识别大规模非结构化文本信息。与数字化的评分相比, 用户在博客、社交网络和在线医疗健康社区中发表的自由文本包含更加丰富的内容。例如, 通过机器学习方法和情感分析技术挖掘患者的在线评论信息, 得到患者对医院各个方面表现的评估[66]; 借助于亚马逊土耳其机器人, 将编码后的社区消息用于训练机器学习模型[67]。此外, 机器学习还可以进行用户情感分析和特征识别, 如通过人工标注数据集和多分类机器学习方法自动识别不同用户的情感类别[68], 借助机器学习训练得到的情绪分类器推断在线医疗健康社区中帖子情绪随时间的变化情况[18], 基于梯度提升树(Gradient Boosting Tree)识别用户情感支持需求的重要特征[7], 使用递归式特征消除(Recursive Feature Elimination)识别最具描述性的错误信息特征等[69]。
典型的在线医疗健康领域的研究方法如表2所示。
从最初对电子公告版信息和互联网癌症支持小组的研究开始, 国内外在线医疗健康研究已经持续了20多年, 取得了丰硕的研究成果。本文以在线医疗健康领域的文献为研究对象, 利用文献计量、聚类分析、纵向映射分析等方法, 对该领域的国家合作、热点研究、主题演化及常用研究方法进行分析和总结。
从合作关系的角度, 美国、英国、澳大利亚和中国仍然是在线医疗健康研究的主要国家, 近20多年里共发表1 353篇相关论文, 与其他国家(地区)的合作关系非常密切, 巴西、巴基斯坦、南非等发展中国家也逐步开始建立合作关系。
从主题演化的角度, 在线医疗健康的主题演化与互联网发展有着密不可分的联系。早期互联网以信息发布为主, 在线医疗健康研究关注互联网健康信息行为, 随着信息载体形式的多样化和沟通的便捷化, 在线医疗健康的研究领域也得到扩展, 社交媒体健康研究、在线医疗健康社区研究、在线医疗健康服务研究、电子健康记录研究等成为学者关注的热点。
从研究热点的角度, 在线医疗健康研究中, 用户是核心对象, 既包括疾病患者, 也涵盖不同年龄层次的健康人群。社交媒体和各类在线医疗健康社区的发展为用户提供了丰富的信息, 不同用户群体的信息搜索行为存在差异, 对于健康信息的评估也受诸多因素影响; 用户在寻求、分享信息和提供社会支持等活动中, 会产生各类结构化、半结构化和非结构化数据, 可以利用这些数据探究用户的行为、特征、态度和情感以及用户之间的关系; 此外, 个人健康信息披露也会带来隐私风险, 从而影响患者的信息分享和使用意图; 电子健康记录为改善医疗健康服务提供了更多机会, 在线访问记录帮助用户更好地了解和管理病情。
从常用研究方法的角度, 尽管问卷是最为广泛的研究方法, 但机器学习、文本挖掘、社会网络分析等方法为在线医疗健康研究开辟了新道路。随着人工智能技术的不断发展, 将图像识别、深度学习和神经网络等新技术应用于在线医疗健康研究中, 可以挖掘出更加深层次的内容。
本文系统地梳理和总结了在线医疗健康领域的相关研究, 为今后该领域的实践和探索提供参考。然而, 本文也存在一定的局限性。仅对WOS核心数据集中的文献进行检索和分析, 在线医疗健康领域的研究范畴更加广泛, 因此其他数据库的文献样本也值得挖掘和分析。另外, 仅从关键词的角度进行聚类和主题演化分析, 未能利用全文进行更加深入的聚类和挖掘。在今后的研究中, 可以扩大文献范围, 结合实际应用案例对在线医疗健康领域进行探索和分析。
吴江: 提出研究选题, 起草及修改论文;
刘冠君: 设计研究方案, 获取、分析文献, 撰写论文主体部分, 论文最终版本修订;
胡仙: 修改论文。
所有作者声明不存在利益冲突关系。
支撑数据由作者自存储, E-mail: jiangw@whu.edu.cn。
[1] 吴江, 刘冠君, 胡仙. data.xlsx. 文献时间分布数据, 国家(地区)统计数据, 关键词聚类网络和主题演化分布.
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Abstract INTRODUCTION: The emergence of online health communities broadens and diversifies channels for patient-doctor interaction. Given limited medical resources, online health communities aim to provide better treatment by decreasing medical costs, making full use of available resources and providing more diverse channels for patients. OBJECTIVES: This research examines how online channel usage affects offline channels, i.e., "Online Booking, Service in Hospitals" (OBSH), and how the channel effects change with doctors' online and offline reputation. METHODS: The study uses data of 4254 doctors from a Chinese online health community. RESULTS: Our findings demonstrate a strong relationship between online health communities and offline hospital communication with an important moderating role for reputation. There are significant channel effects, wherein written consultation complements OBSH (0205=3.320, p<0.10), but telephone consultation can be a readily substitute for OBSH (0205=-9.854, p<0.001). We also find that doctors with higher online and offline reputations can attract more patients to use the OBSH (0205 online =0.433, p<0.001; 0205 offline =2.318&2.123, p<0.001). Third, channel effects fluctuate, relative to doctors' online and offline reputations: doctors with higher online reputations mitigate substitution effects between telephone consultation and OBSH (0205=0.064, p<0.01), and doctors with higher offline reputations mitigate complementary effects between written consultation and OBSH (0205=-1.586&-1.417, p<0.001). CONCLUSIONS: This study contributes to both knowledge and practice. This study shows that there is channel effect in healthcare, websites' managers can encourage physicians to provide online services, especially for these physicians who do not have enough patients. Copyright 0008 2017 Elsevier B.V. All rights reserved.
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[本文引用:1]
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| [3] |
[本文引用:1]
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This project investigates the ways in which patients respond to the shared use of what is often considered private information: personal health data. There is a growing demand for patient access to personal health records. The predominant model for this record is a repository of all clinically relevant health information kept securely and viewed privately by patients and their health care providers. While this type of record does seem to have beneficial effects for the patient–physician relationship, the complexity and novelty of these data coupled with the lack of research in this area means the utility of personal health information for the primary stakeholders—the patients—is not well documented or understood. PatientsLikeMe is an online community built to support information exchange between patients. The site provides customized disease-specific outcome and visualization tools to help patients understand and share information about their condition. We begin this paper by describing the components and design of the online community. We then identify and analyze how users of this platform reference personal health information within patient-to-patient dialogues. Patients diagnosed with amyotrophic lateral sclerosis (ALS) post data on their current treatments, symptoms, and outcomes. These data are displayed graphically within personal health profiles and are reflected in composite community-level symptom and treatment reports. Users review and discuss these data within the Forum, private messaging, and comments posted on each other's profiles. We analyzed member communications that referenced individual-level personal health data to determine how patient peers use personal health information within patient-to-patient exchanges. Qualitative analysis of a sample of 123 comments (about 2% of the total) posted within the community revealed a variety of commenting and questioning behaviors by patient members. Members referenced data to locate others with particular experiences to answer specific health-related questions, to proffer personally acquired disease-management knowledge to those most likely to benefit from it, and to foster and solidify relationships based on shared concerns. Few studies examine the use of personal health information by patients themselves. This project suggests how patients who choose to explicitly share health data within a community may benefit from the process, helping them engage in dialogues that may inform disease self-management. We recommend that future designs make each patient's health information as clear as possible, automate matching of people with similar conditions and using similar treatments, and integrate data into online platforms for health conversations.
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The effect of motivations on knowledge sharing behavior has been extensively investigated in various online communities. However, this topic is rarely examined in the context of online health communities (OHCs). Furthermore, the difference in the motivations of sharing knowledge between two types of members in OHCs health professionals and normal users, is never examined. The present study models and examines both the extrinsic (reputation and reciprocity) and intrinsic (knowledge self-efficacy, altruism, and empathy) motivations of health professionals and normal users. The hypotheses derived from the research model were empirically validated using an online survey of 443 members of three famous online health communities in China. Results show that reciprocity and altruism positively affect the knowledge sharing intention of both health professionals and normal users. Moreover, reputation and knowledge self-efficacy have a greater influence on the knowledge sharing intentions of health professionals than normal users; whereas reciprocity, altruism, and empathy have a greater influence on the knowledge sharing intentions of normal users than health professionals. These new findings expand our understanding on the motivations that may affect knowledge sharing intentions in the context of OHCs.
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Background: While online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge. Objective: The objective of this study was to discriminate important features for identifying users social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users social support needs based on raw data collected from OHSNs. Methods: We initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features. Results: The best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others. Conclusions: We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey data to evaluate the feasibility of the framework. Our study contributes to providing personalized social support in OHSNs. [J Med Internet Res 2017;19(8):e272]
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The striking growth of online communities in recent years has sparked significant interest in understanding and quantifying benefits of participation. While research has begun to document the economic outcomes associated with online communities, quantifying the social value created in these collectives has been largely overlooked. This study proposes that online health communities create social value by addressing rural–urban health disparities via improved health capabilities. Using a unique data set from a rare disease community, we provide one of the first empirical studies of social value creation. Our quantitative analysis using exponential random graph models reveals patterns of social support exchanged between users and the variations in these patterns based on users' location. We find that, overall, urban users are net suppliers of social support while rural participants are net recipients, suggesting that technology-mediated online health communities are able to alleviate rural–urban health disparities. This study advances extant understanding of value production in online collectives, and yields implications for policy.
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[本文引用:1]
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This article presents a new open-source software tool, SciMAT, which performs science mapping analysis within a longitudinal framework. It provides different modules that help the analyst to carry out all the steps of the science mapping workflow. In addition, SciMAT presents three key features that are remarkable in respect to other science mapping software tools: (a) a powerful preprocessing module to clean the raw bibliographical data, (b) the use of bibliometric measures to study the impact of each studied element, and (c) a wizard to configure the analysis.
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以ISIWebofKnowledge数据库中收录的以InformationService为标题的文献为数据源，借助信息可视化软件CiteSpace对所采集的数据进行共引分析和聚类分析，绘制出信息服务领域的国家机构图谱、文献共被引网络图谱，展示了该领域的研究力量分布，确定了该领域的代表人物、代表作品；同时利用软件提供的词频探测技术绘制出信息服务领域的研究热点知识图谱及研究前沿与发展知识图谱，揭示了该领域的研究热点、研究前沿及其发展趋势。
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[本文引用:1]
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Two natural and widely used representations for the community structure of networks are clusterings, which partition the vertex set into disjoint subsets, and layouts, which assign the vertices to positions in a metric space. This paper unifies prominent characterizations of layout quality and clustering quality, by showing that energy models of pairwise attraction and repulsion subsume Newman and Girvan's modularity measure. Layouts with optimal energy are relaxations of, and are thus consistent with, clusterings with optimal modularity, which is of practical relevance because both representations are complementary and often used together.
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Abstract Cancer patients are increasingly using the Internet to learn about their disease, connect with others undergoing similar treatments and obtain support outside of the clinical encounter. The goal of this project was to explore how patients with gynecological cancers (ovarian, cervical, and endometrial) used the Internet as an information resource and how this influenced their treatment decisions and interactions with their health care specialists. From 2013 to 2014, ovarian, endometrial, and cervical cancer patients attending a comprehensive cancer centre were invited to complete a 24-item paper questionnaire detailing their experiences in searching the Internet. Twenty-eight patients completed survey. The largest portion of participants had an ovarian cancer diagnosis (6102%), followed by endometrial (2902%) and cervical cancer (1102%). Results indicate that the majority (8502%) of patients used the Internet as a resource to learn about their gynecological cancers. Most respondents (8902%) used Google as their search engine, and some used multiple search engines. The most frequently searched topics included treatment information (8502%), management of symptoms/treatment toxicity (5902%), and alternative treatments (3702%). Many patients (7402%) felt that the Internet was a useful tool for understanding their diagnosis; however, 3302% reported that the Internet was somewhat hard to understand. Most (7802%) patients reported that Internet information increased their understanding of their diagnosis, and 5602% felt it did not affect their decision-making. This study highlights how gynecological patients are accessing cancer information online and how physicians may support this within the clinical setting.
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Abstract OBJECTIVE: Many healthcare organizations (HCOs) including Kaiser Permanente, Johns Hopkins, Cleveland Medical Center, and MD Anderson Cancer Center, provide access to online health communities as part of their overall patient support services. The key objective in establishing and running these online health communities is to offer empathic support to patients. Patients' perceived empathy is considered to be critical in patient recovery, specifically, by enhancing patient's compliance with treatment protocols and the pace of healing. Most online health communities are characterized by two main functions: informational support and social support. This study examines the relative impact of these two distinct functions-that is, as an information seeking forum and as a social support forum-on patients' perceived empathy in online health communities. DESIGN: This study tests the impact of two variables that reflect the above functions of online health communities-information seeking effectiveness and perceived social support-on perceived empathy. The model also incorporates the potential moderating effect of homophily on these relationships. MEASUREMENTS: A web-based survey was used to collect data from members of the online health communities provided by three major healthcare centers. A regression technique was used to analyze the data to test the hypotheses. RESULTS: The study finds that it is the information seeking effectiveness rather than the social support which affects patient's perceived empathy in online health communities run by HCOs. The results indicate that HCOs that provide online health communities for their patients need to focus more on developing tools that will make information seeking more effective and efficient.
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To determine whether the community's attitudes to components of a community eHealth strategy differ across three different socioeconomic groups.A survey questionnaire was designed and implemented across three different communities.Paper-based surveys were left in community organisations and local health practices in a low socioeconomic community on the outskirts of Ipswich, Queensland (n = 262), a mid-high socioeconomic community in the western suburbs of Brisbane (n = 256) and at a local university (n = 200).Ascribed importance and comfort with proposed components of a community eHealth strategy.A community-oriented health website was perceived as useful in getting access to relevant health information. Those who were most comfortable with accessing online health information were those who were: experienced, had home internet access and were frequent internet users. The most important types of health information for the website were: information about the treatment of conditions, how to manage a chronic illness, how to stay healthy and patient clinical pathways. The low socioeconomic community had different information priorities all categories were considered more important, particularly information about how the public system operates, local health support groups, and the roles of health professionals.Different communities have different information demands but there is a strong demand for information which empowers community members to take control of their own health and become active participants in their health care. Tools such as a community health portal and patient clinical pathways should become more available.
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AbstractAs one of the largest social networking sites in the world, Facebook holds a great potential for promoting health. In this exploratory study, we analyzed 1352 messages posted to an active Facebook diabetes group to identify the characteristics of the group. The results revealed that the group was international in nature. Users overcame language barriers to communicate with people with similar conditions. Users' interactions were structured around information, emotion, and community building. They exchanged medical and lifestyle information, and highly valued their peers' personal experiences, opinions, and advice. They also demonstrated a positive attitude toward the reality of living with diabetes and generously provided encouragements and affirmations to one another. Great efforts were made to maintain the proper operation of the community by the administrator and a group of core members. As a result, the group was shaped as a social network where peer users share social support, cultivate companionship, and exert social influence. Based on the results, we discussed future directions for research of health communities in a highly connected world.
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Online health communities (OHCs) constitute a useful source of information and social support for patients. American Cancer Society's Cancer Survivor Network (CSN), a 173 000-member community, is the largest online network for cancer patients, survivors, and caregivers. A discussion thread in CSN is often initiated by a cancer survivor seeking support from other members of CSN. Discussion threads are multiparty conversations that often provide a source of social support, e.g., by bringing about a change of sentiment from negative to positive on the part of the thread originator. While previous studies regarding cancer survivors have shown that the members of an OHC derive benefits from their participation in such communities, causal accounts of the factors that contribute to the observed benefits have been lacking. We introduce a novel framework to examine the temporal causality of sentiment dynamics in the CSN. We construct a probabilistic computation tree logic representation and a corresponding probabilistic Kripke structure to represent and reason about the changes in sentiments of posts in a thread over time. We use a sentiment classifier trained using machine learning on a set of posts manually tagged with sentiment labels to classify posts as expressing either positive or negative sentiment. We analyze the probabilistic Kripke structure to identify the prima facie causes of sentiment change on the part of the thread originators in the CSN forum and their significance. We find that the sentiment of replies appears to causally influence the sentiment of the thread originator. Our experiments also show that the conclusions are robust with respect to the choice of the: 1) classification threshold of the sentiment classifier and 2) choice of the specific sentiment classifier used. We also extend the basic framework for temporal causality analysis to incorporate the uncertainty in the states of the probabilistic Kripke structure resulting from the use of an imperfect state transducer (in our case, the sentiment classifier). Our analysis of temporal causality of CSN sentiment dynamics offers new insights that the designers, managers, and moderators of an online community, such as CSN, can utilize to facilitate and enhance the interactions so as to better meet the social support needs of the CSN participants. The proposed methodology for the analysis of temporal causality has broad applicability in a variety of settings where the dynamics of the underlying system can be modeled in terms of state variables that change in response to internal or external inputs.
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Participatory health approaches are increasingly drawing attention among the scientific community, and could be used for health promotion programmes on diabetes through social media. The main aim of this project is to research how to best use social media to promote healthy lifestyles with and within the Norwegian population. The design of the health promotion intervention (HPI) will be participatory, and will involve both a panel of healthcare experts and social media users following the Norwegian Diabetes Association. The panel of experts will agree on the contents by following the Delphi method, and social media users will participate in the definition of the HPI by expressing their opinions through an adhoc online questionnaire. The agreed contents between both parties to be used in the HPI will be posted on three social media channels (Facebook, Twitter and Instagram) along 2402months. The 302months before starting the HPI, and the 302months after the HPI will be used as control data. The effect of the HPI will be assessed by comparing formats, frequency, and reactions to the published HPI messages, as well as comparing potential changes in five support-intended communication behaviours expressed on social media, and variations in sentiment analysis before vs during and after the HPI. The HPI's effect on social media users' health-related lifestyles, online health behaviours, and satisfaction with the intervention will be assessed every 602months through online questionnaires. A separate questionnaire will be used to assess the panel of experts' satisfaction and perceptions of the benefits for health professionals of a HPI as this one. The time constraints of today's medical practice combined with the piling demand of chronic conditions such as diabetes make any additional request of extra time used by health care professionals a challenge. Social media channels provide efficient, ubiquitous and user-friendly platforms that can encourage participation, engagement and action necessary from both those who receive and provide care to make health promotion interventions successful.
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Social network technologies have become part of health education and wider health promotion ither by design or happenstance. Social support, peer pressure, and information sharing in online communities may affect health behaviors. If there are positive and sustained effects, then social network technologies could increase the effectiveness and efficiency of many public health campaigns. Social media alone, however, may be insufficient to promote health. Furthermore, there may be unintended and potentially harmful consequences of inaccurate or misleading health information. Given these uncertainties, there is a need to understand and synthesize the evidence base for the use of online social networking as part of health promoting interventions to inform future research and practice. Our aim was to review the research on the integration of expert-led health promotion interventions with online social networking in order to determine the extent to which the complementary benefits of each are understood and used. We asked, in particular, (1) How is effectiveness being measured and what are the specific problems in effecting health behavior change?, and (2) To what extent is the designated role of social networking grounded in theory? The narrative synthesis approach to literature review was used to analyze the existing evidence. We searched the indexed scientific literature using keywords associated with health promotion and social networking. The papers included were only those making substantial study of both social networking and health promotion ither reporting the results of the intervention or detailing evidence-based plans. General papers about social networking and health were not included. The search identified 162 potentially relevant documents after review of titles and abstracts. Of these, 42 satisfied the inclusion criteria after full-text review. Six studies described randomized controlled trials (RCTs) evaluating the effectiveness of online social networking within health promotion interventions. Most of the trials investigated the value of a ocial networking condition in general and did not identify specific features that might play a role in effectiveness. Issues about the usability and level of uptake of interventions were more common among pilot studies, while observational studies showed positive evidence about the role of social support. A total of 20 papers showed the use of theory in the design of interventions, but authors evaluated effectiveness in only 10 papers. More research is needed in this area to understand the actual effect of social network technologies on health promotion. More RCTs of greater length need to be conducted taking into account contextual factors such as patient characteristics and types of a social network technology. Also, more evidence is needed regarding the actual usability of online social networking and how different interface design elements may help or hinder behavior change and engagement. Moreover, it is crucial to investigate further the effect of theory on the effectiveness of this type of technology for health promotion. Research is needed linking theoretical grounding with observation and analysis of health promotion in online networks.
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Health-related online social networks are starting to play a role in many people's daily lives by enabling them to monitor their diet and motivating them to change their lifestyles. These social networks provide different motivation mechanisms. However, little research has been done on the effectiveness of these motivation mechanisms. This research analyzes data collected from a digital health community to examine what mechanisms can help motivate people. The results suggest that there is a high level of correlation between users' exercise activities and their participation in these digital health communities. This research benefits the digital health communities by providing insights into the design of motivation mechanisms. (C) 2013 Elsevier B.V. All rights reserved.
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| [23] |
The use of online health services (henceforth, OHS) among middle-aged to older adults can make health-related actions more accessible to this population group as well as help reduce the burden on the health system and avoid unnecessary costs. The study's objectives were to examine the responsiveness and willingness of individuals aged 45+ to use different OHS and to characterize the attitudes and main factors influencing that responsiveness. We conducted a telephone survey among a sample of 703 individuals constituting a representative sample of the Israeli population of individuals aged 45+. The research questionnaire integrates the principles of the Adopting Medical Information Technologies model and includes socio-demographic attributes. The results indicate that 7802% of internet users claimed to use at least one OHS (7902% of the Jewish sector and 6602% of the non-Jewish sector). Nevertheless, 2202% of internet users do not use OHS. Most online use is on Health Maintenance Organizations (HMO) websites to obtain administrative information. Frequency of OHS use increases as the following factors increase: perceived ease of online use; extent of encouragement for online use; perceived reliability of online health services; and extent of advertisement exposure. The study found that OHS use is much more prevalent among wealthy populations. In addition, individuals' attitudes and the extent of their exposure to advertisement influence their use and intention to use OHS. A number of recommendations emerge from the study: 1) For OHS use to increase online health websites should be made more accessible to middle aged-older adults individuals and those of different languages and cultures. 2) Programs should be developed to teach HMO staff to encourage patients to use OHS. 3) Media advertising that encourages the use of OHS should be expanded.
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| [24] |
We compared physicians' self-reported attitudes and behaviours regarding electronic health record (EHR) use before and after installation of computers in patient examination rooms and transition to full implementation of an EHR in a family medicine training practice to identify anticipated and observed effects these changes would have on physicians' practices and clinical encounters.We conducted two individual qualitative interviews with family physicians. The first interview was before and second interview was 8 months later after full implementation of an EHR and computer installation in the examination rooms. Data were analysed through project team discussions and subsequent coding with qualitative analysis software.At the first interviews, physicians frequently expressed concerns about the potential negative effect of the EHR on quality of care and physician-patient interaction, adequacy of their skills in EHR use and privacy and confidentiality concerns. Nevertheless, most physicians also anticipated multiple benefits, including improved accessibility of patient data and online health information. In the second interviews, physicians reported that their concerns did not persist. Many anticipated benefits were realized, appearing to facilitate collaborative physician-patient relationships. Physicians reported a greater teaching role with patients and sharing online medical information and treatment plan decisions.Before computer installation and full EHR implementation, physicians expressed concerns about the impact of computer use on patient care. After installation and implementation, however, many concerns were mitigated. Using computers in the examination rooms to document and access patients' records along with online medical information and decision-making tools appears to contribute to improved physician-patient communication and collaboration.
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| [25] |
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| [26] |
The Internet has increasingly become a popular source of health information by connecting individuals with health content, experts, and support. More and more, individuals turn to social media and Internet sites to share health information and experiences. Although online health information seeking occurs worldwide, limited empirical studies exist examining cross-cultural differences in perceptions about user-generated, experience-based information compared to expertise-based information sources. To investigate if cultural variations exist in patterns of online health information seeking, specifically in perceptions of online health information sources. It was hypothesized that Koreans and Hongkongers, compared to Americans, would be more likely to trust and use experience-based knowledge shared in social Internet sites, such as social media and online support groups. Conversely, Americans, compared to Koreans and Hongkongers, would value expertise-based knowledge prepared and approved by doctors or professional health providers more. Survey questionnaires were developed in English first and then translated into Korean and Chinese. The back-translation method ensured the standardization of questions. Surveys were administered using a standardized recruitment strategy and data collection methods. A total of 826 participants living in metropolitan areas from the United States (n=301), Korea (n=179), and Hong Kong (n=337) participated in the study. We found significant cultural differences in information processing preferences for online health information. A planned contrast test revealed that Koreans and Hongkongers showed more trust in experience-based health information sources (blogs:t451.50=11.21,P<.001; online support group:t455.71=9.30,P<.001; social networking sites [SNS]:t466.75=11.36,P<.001) and also reported using blogs (t515.31=6.67,P<.001) and SNS (t529.22=4.51,P<.001) more frequently than Americans. Americans showed a stronger preference for using expertise-based information sources (eg, WebMD and CDC) compared to Koreans and Hongkongers (t360.02=3.01,P=.003). Trust in expertise-based information sources was universal, demonstrating no cultural differences (Brown-ForsytheF2,654=1.82,P=.16). Culture also contributed significantly to differences in searching information on behalf of family members (t480.38=5.99,P<.001) as well as to the goals of information searching. This research found significant cultural differences in information processing preferences for online health information. Further discussion is included regarding effective communication strategies in providing quality health information.
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| [27] |
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| [28] |
This paper presents an approach to analyze the thematic evolution of a given research field. This approach combines performance analysis and science mapping for detecting and visualizing conceptual subdomains (particular themes or general thematic areas). It allows us to quantify and visualize the thematic evolution of a given research field. To do this, co-word analysis is used in a longitudinal framework in order to detect the different themes treated by the research field across the given time period. The performance analysis uses different bibliometric measures, including the h-index, with the purpose of measuring the impact of both the detected themes and thematic areas. The presented approach includes a visualization method for showing the thematic evolution of the studied field. Then, as an example, the thematic evolution of the Fuzzy Sets Theory field is analyzed using the two most important journals in the topic: Fuzzy Sets and Systems and IEEE Transactions on Fuzzy Systems.
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| [29] |
Increasing number of individuals are using the internet to meet their health information needs; however, little is known about the characteristics of online health information seekers and whether they differ from individuals who search for health information from offline sources. Researchers must examine the primary characteristics of online and offline health information seekers in order to better recognize their needs, highlight improvements that may be made in the arena of internet health information quality and availability, and understand factors that discriminate between those who seek online vs. offline health information. This study examines factors that differentiate between online and offline health information seekers in the United States. Data for this study are from a subsample ( n=385) of individuals from the 2000 General Social Survey. The subsample includes those respondents who were asked Internet and health seeking module questions. Similar to prior research, results of this study show that the majority of both online and offline health information seekers report reliance upon health care professionals as a source of health information. This study is unique in that the results illustrate that there are several key factors (age, income, and education) that discriminate between US online and offline health information seekers; this suggests that general “digital divide” characteristics influence where health information is sought. In addition to traditional digital divide factors, those who are healthier and happier are less likely to look exclusively offline for health information. Implications of these findings are discussed in terms of the digital divide and the patient–provider relationship.
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| [30] |
The Internet has become a favored source to find health information. Worldwide, about 4.5% of all Internet searches are for health-related information. However, research has found that the quality of online health information is mixed, which raises serious concerns about the impact of this information. This paper reviews relevant research to understand how health information on the Internet is retrieved, evaluated, and used. Most users of online health information are looking for information about specific health conditions because they or someone they know was diagnosed with a medical condition. They typically use general search engines to find online health information and enter short phrases, often misspelled. They seldom go beyond the first page of a search. Both their search and evaluation skills are limited although they are concerned about the quality of online health information. They avoid sites with overt commercialism, but often do not pay attention to indicators of credibility. Online health information is used to fill an information void which can enhance coping and self efficacy, affects health-related decisions and behavior of users and their friends and family, and is often discussed with health care providers. There are cross-cultural differences in the types of sites used as well as how online information is used. Based on the research reviewed in this paper, three major recommendations are suggested. Professionals should recommend sites. Professionals should promote more effective search and evaluation techniques. Professionals should be involved in developing and promoting uniform standards for health and mental health sites.
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| [31] |
Increasing numbers of people are turning to the Internet for health advice despite reports that sites vary in terms of their quality. How do they decide whether or not to trust the advice they find online? A staged model of trust development is proposed and tested here in a longitudinal study in which fifteen women faced with decisions concerning the menopause and hormone replacement therapy (HRT) were observed while searching the Internet for information and advice over four consecutive weeks and then kept diaries over a six-month follow-up period. The women were all resident in the North-East of England and were recruited through advertisements in the local media. The study provided general support for a three-stage model of trust in which participants firstly engaged in rapid heuristic processing of information, efficiently sifting and rejecting general sales sites and portals but sometimes rejecting high-quality content because of poor design. Well-designed sites were then effectively interrogated for credible and personalized content before being designated trustworthy. The women appeared to act much like cientists using web material to generate and test hypotheses and theories about HRT, although their capacity to deal with certain forms of risk information was limited. They subsequently reported integrating online advice with offline advice from friends, family and physicians in order to be fully confident in their final decisions. Women felt that the Internet influenced their decision-making and improved communications with physicians. Personalized stories from like-minded others improved trust perceptions. Despite the use of the Internet the physician was still seen as the primary source of information and advice.
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| [32] |
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| [33] |
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| [34] |
This article contributes to the growing body of research that explores the significance of context in health information behavior. Specifically, through the lens of trust judgments, it demonstrates that gender is a determinant of the information evaluation process. A questionnaire-based survey collected data from adults regarding the factors that influence their judgment of the trustworthiness of online health information. Both men and women identified credibility, recommendation, ease of use, and brand as being of importance in their trust judgments. However, women also take into account style, while men eschew this for familiarity. In addition, men appear to be more concerned with the comprehensiveness and accuracy of the information, the ease with which they can access it, and its familiarity, whereas women demonstrate greater interest in cognition, such as the ease with which they can read and understand the information. These gender differences are consistent with the demographic data, which suggest that: women consult more types of sources than men; men are more likely to be searching with respect to a long-standing health complaint; and, women are more likely than men to use tablets in their health information seeking. Recommendations for further research to better inform practice are offered.
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| [35] |
Many Web-based health care platforms allow patients to evaluate physicians by posting open-end textual reviews based on their experiences. These reviews are helpful resources for other patients to choose high-quality doctors, especially in countries like China where no doctor referral systems exist. Analyzing such a large amount of user-generated content to understand the voice of health consumers has attracted much attention from health care providers and health care researchers. The aim of this paper is to automatically extract hidden topics from Web-based physician reviews using text-mining techniques to examine what Chinese patients have said about their doctors and whether these topics differ across various specialties. This knowledge will help health care consumers, providers, and researchers better understand this information. We conducted two-fold analyses on the data collected from the “Good Doctor Online” platform, the largest online health community in China. First, we explored all reviews from 2006-2014 using descriptive statistics. Second, we applied the well-known topic extraction algorithm Latent Dirichlet Allocation to more than 500,000 textual reviews from over 75,000 Chinese doctors across four major specialty areas to understand what Chinese health consumers said online about their doctor visits. On the “Good Doctor Online” platform, 112,873 out of 314,624 doctors had been reviewed at least once by April 11, 2014. Among the 772,979 textual reviews, we chose to focus on four major specialty areas that received the most reviews: Internal Medicine, Surgery, Obstetrics/Gynecology and Pediatrics, and Chinese Traditional Medicine. Among the doctors who received reviews from those four medical specialties, two-thirds of them received more than two reviews and in a few extreme cases, some doctors received more than 500 reviews. Across the four major areas, the most popular topics reviewers found were the experience of finding doctors, doctors' technical skills and bedside manner, general appreciation from patients, and description of various symptoms. To the best of our knowledge, our work is the first study using an automated text-mining approach to analyze a large amount of unstructured textual data of Web-based physician reviews in China. Based on our analysis, we found that Chinese reviewers mainly concentrate on a few popular topics. This is consistent with the goal of Chinese online health platforms and demonstrates the health care focus in China's health care system. Our text-mining approach reveals a new research area on how to use big data to help health care providers, health care administrators, and policy makers hear patient voices, target patient concerns, and improve the quality of care in this age of patient-centered care. Also, on the health care consumer side, our text mining technique helps patients make more informed decisions about which specialists to see without reading thousands of reviews, which is simply not feasible. In addition, our comparison analysis of Web-based physician reviews in China and the United States also indicates some cultural differences.
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| [36] |
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| [37] |
(2017). The Influence of eHealth Literacy on Perceived Trust in Online Health Communication Channels and Sources. Journal of Health Communication: Vol. 22, No. 1, pp. 53-65. doi: 10.1080/10810730.2016.1250846
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| [38] |
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| [39] |
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| [40] |
Health care professionals, patients, caregivers, family, friends, and other supporters are increasingly joining online health communities to share information and find support. But social Web (Web 2.0) technology alone does not create a successful online community. Building and sustaining a successful community requires an enabler and strategic community management. Community management is more than moderation. The developmental life cycle of a community has four stages: inception, establishment, maturity, and mitosis. Each stage presents distinct characteristics and management needs. This paper describes the community management strategies, resources, and expertise needed to build and maintain a thriving online health community; introduces some of the challenges; and provides a guide for health organizations considering this undertaking. The paper draws on insights from an ongoing study and observation of online communities as well as experience managing and consulting a variety of online health communities. Discussion includes effective community building practices relevant to each stage, such as outreach and relationship building, data collection, content creation, and other proven techniques that ensure the survival and steady growth of an online health community.
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| [41] |
Most households in the United Kingdom have Internet access, and health-related Internet use is increasing. The National Health Service (NHS) Direct website is the major UK provider of online health information. Our objective was to identify the characteristics and motivations of online health information seekers accessing the NHS Direct website, and to examine the benefits and challenges of the health Internet. We undertook an online questionnaire survey, offered to users of the NHS Direct website. A subsample of survey respondents participated in in-depth, semistructured, qualitative interviews by telephone or instant messaging/email. Questionnaire results were analyzed using chi-square statistics. Thematic coding with constant comparison was used for interview transcript analysis. In total 792 respondents completed some or all of the survey: 71.2% (534/750 with data available) were aged under 45 years, 67.4% (511/758) were female, and 37.7% (286/759) had university-level qualifications. They sought information for themselves (545/781, 69.8%), someone else (172/781, 22.0%), or both (64/781, 8.2%). Women were more likely than men to seek help for someone else or both themselves and someone else (168/509 vs 61/242, 2 2 = 6.35, P = .04). Prior consultation with a health professional was reported by 44.9% (346/770), although this was less common in younger age groups (<36 years) ( 2 1 = 24.22, P < .001). Participants aged 16 to 75 years (n = 26, 20 female, 6 male) were recruited for interview by telephone (n = 23) and instant messaging/email (n = 3). Four major interview themes were identified: motivations for seeking help online; benefits of seeking help in this way and some of the challenges faced; strategies employed in navigating online health information provision and determining what information to use and to trust; and specific comments regarding the NHS Direct website service. Within the motivation category, four concepts emerged: the desire for reassurance; the desire for a second opinion to challenge other information; the desire for greater understanding to supplement other information; and perceived external barriers to accessing information through traditional sources. The benefits clustered around three theme areas: convenience, coverage, and anonymity. Various challenges were discussed but no prominent theme emerged. Navigating online health information and determining what to trust was regarded as a 渃ommon sense activity, and brand recognition was important. Specific comments about NHS Direct included the perception that the online service was integrated with traditional service provision. This study supports a model of evolutionary rather than revolutionary change in online health information use. Given increasing resource constraints, the health care community needs to seek ways of promoting efficient and appropriate health service use, and should aim to harness the potential benefits of the Internet, informed by an understanding of how and why people go online for health.
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| [42] |
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| [43] |
[本文引用:1]
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| [44] |
[本文引用:1]
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| [45] |
[本文引用:2]
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| [46] |
Within the emerging context of the digitization of health care, electronic health records (EHRs) constitute a significant technological advance in the way medical information is stored, communicated, and processed by the multiple parties involved in health care delivery. However, in spite of the anticipated value potential of this technology, there is widespread concern that consumer privacy issues may impede its diffusion. In this study, we pose the question: Can individuals be persuaded to change their attitudes and opt-in behavioral intentions toward EHRs, and allow their medical information to be digitized even in the presence of significant privacy concerns? To investigate this question, we integrate an individual's concern for information privacy (CFIP) with the elaboration likelihood model (ELM) to examine attitude change and likelihood of opting-in to an EHR system. We theorize that issue involvement and argument framing interact to influence attitude change, and that concern for information privacy further moderates the effects of these variables. We also propose that likelihood of adoption is driven by concern for information privacy and attitude. We test our predictions using an experiment with 366 subjects where we manipulate the framing of the arguments supporting EHRs. We find that an individual's CFIP interacts with argument framing and issue involvement to affect attitudes toward the use of EHRs. In addition, results suggest that attitude toward EHR use and CFIP directly influence opt-in behavioral intentions. An important finding for both theory and practice is that even when people have high concerns for privacy, their attitudes can be positively altered with appropriate message framing. These results as well as other theoretical and practical implications are discussed.
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| [47] |
The future of health care delivery is becoming more citizen centered, as today user is more active, better informed, and more demanding. Worldwide governments are promoting online health services, such as electronic health record (EHR) patient portals and, as a result, the deployment and use of these services. Overall, this makes the adoption of patient-accessible EHR portals an important field to study and understand. The aim of this study is to understand the factors that drive individuals to adopt EHR portals. We applied a new adoption model using, as a starting point, Ventkatesh's Unified Theory of Acceptance and Use of Technology in a consumer context (UTAUT2) by integrating a new construct specific to health care, a new moderator, and new relationships. To test the research model, we used the partial least squares (PLS) causal modelling approach. An online questionnaire was administrated. We collected 360 valid responses. The statistically significant drivers of behavioral intention are performance expectancy (beta=.200;t=3.619), effort expectancy (beta=.185;t=2.907), habit (beta=.388;t=7.320), and self-perception (beta=.098;t=2.285). The predictors of use behavior are habit (beta=0.206;t=2.752) and behavioral intention (beta=0.258;t=4.036). The model explained 49.7% of the variance in behavioral intention and 26.8% of the variance in use behavior. Our research helps to understand the desired technology characteristics of EHR portals. By testing an information technology acceptance model, we are able to determine what is more valued by patients when it comes to deciding whether to adopt EHR portals or not. The inclusion of specific constructs and relationships related to the health care consumer area also had a significant impact on understanding the adoption of EHR portals.
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| [48] |
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| [49] |
Abstract BACKGROUND: Routinely recommended screening for breast, cervical, and colorectal cancers can significantly reduce mortality from these types of cancer, yet screening is underutilized among Asians. Surveys rely on self-report and often are underpowered for analysis by Asian ethnicities. Electronic health records (EHR) include validated (as opposed to recall-based) rates of cancer screening. In this article, we seek to better understand cancer screening patterns in a population of insured Asian Americans. METHODS: We calculated rates of compliance with cervical, breast, and colorectal cancer screening among Asians from an EHR population and compared them with non-Hispanic whites. We performed multivariable modeling to evaluate potential predictors (at the provider- and patient-level) of screening completion among Asian patients. RESULTS: Aggregation of Asian subgroups masked heterogeneity in screening rates. Asian Indians and native Hawaiians and Pacific Islanders had the lowest rates of screening in our sample, well below that of non-Hispanic whites. In multivariable analyses, screening completion was negatively associated with patient-physician language discordance for mammography [OR, 0.81; 95% confidence interval (CI), 0.71-0.92] and colorectal cancer screening (OR, 0.79; CI, 0.72-0.87) and positively associated with patient-provider gender concordance for mammography (OR, 1.16; CI, 1.00-1.34) and cervical cancer screening (OR, 1.66; CI, 1.51-1.82). In addition, patient enrollment in online health services increased mammography (OR, 1.32; CI, 1.20-1.46) and cervical cancer screening (OR, 1.31; CI, 1.24-1.37). CONCLUSIONS: Language- and gender-concordant primary care providers and culturally tailored online health resources may help improve preventive cancer screening in Asian patient populations. IMPACT: This study demonstrates how the use of EHR data can inform investigations of primary prevention practices within the healthcare delivery setting. See all the articles in this CEBP Focus section, "Cancer in Asian and Pacific Islander Populations." Cancer Epidemiol Biomarkers Prev; 23(11); 2208-17. 2014 AACR. 2014 American Association for Cancer Research.
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| [50] |
IntroductionAustralia has a complex health system1,2 that has evolved to deal with many competing...
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| [51] |
2017 Elsevier B.V. This study explores the antecedents and consequences of health information privacy concerns in online health communities by integrating the dual calculus and protection motivation theories. On the basis of survey data from 337 users, health information privacy concerns, together with informational and emotional support, significantly influence personal health information (PHI) disclosure intention. Privacy concerns are negatively influenced by two coping appraisals (i.e., response efficacy and self-efficacy) and positively affected by two threat appraisals (i.e., perceived vulnerability and perceived severity). The perceived health status differentially moderates the effects of privacy concerns and informational support on the PHI disclosure intention.
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| [52] |
The Internet and social media offer promising ways to improve the reach, efficiency, and effectiveness of recruitment efforts at a reasonable cost, but raise unique ethical dilemmas. We describe how we used social media to recruit cancer patients and family caregivers for a research study, the ethical issues we encountered, and the strategies we developed to address them. Drawing on the principles of Privacy by Design (PbD), a globally recognized standard for privacy protection, we aimed to develop a PbD framework for online health research recruitment. We proposed a focus group study on the dietary behaviors of cancer patients and their families, and the role of Web-based dietary self-management tools. Using an established blog on our hospital website, we proposed publishing a recruitment post and sharing the link on our Twitter and Facebook pages. The Research Ethics Board (REB) raised concern about the privacy risks associated with our recruitment strategy; by clicking on a recruitment post, an individual could inadvertently disclose personal health information to third-party companies engaged in tracking online behavior. The REB asked us to revise our social media recruitment strategy with the following questions in mind: (1) How will you inform users about the potential for privacy breaches and their implications? and (2) How will you protect users from privacy breaches or inadvertently sharing potentially identifying information about themselves? Ethical guidelines recommend a proportionate approach to ethics assessment, which advocates for risk mitigation strategies that are proportional to the magnitude and probability of risks. We revised our social media recruitment strategy to inform users about privacy risks and to protect their privacy, while at the same time meeting our recruitment objectives. We provide a critical reflection of the perceived privacy risks associated with our social media recruitment strategy and the appropriateness of the risk mitigation strategies that we employed by assessing their alignment with PbD and by discussing the following: (1) What are the potential risks and who is at risk? (2) Is cancer considered ensitive personal information? (3) What is the probability of online disclosure of a cancer diagnosis in everyday life? and (4) What are the public expectations for privacy online and their views about online tracking, profiling, and targeting? We conclude with a PbD framework for online health research recruitment. Researchers, REBs, ethicists, students, and potential study participants are often unaware of the privacy risks of social media research recruitment and there is no official guidance. Our PbD framework for online health research recruitment is a resource for these wide audiences.
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| [53] |
Context Telehealth is a fast-growing sector in health care, using a variety of technologies to exchange information across locations and to improve access, quality, and outcomes across the continuum of care. Thousands of studies and hundreds of systematic reviews have been done, but their variability leaves many questions about telehealth's effectiveness, implementation priorities, and return... [Show full abstract]
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| [54] |
The use of a smartphone in the delivery of healthcare is called an mHealth application (or app). mHealth apps present the world with an opportunity to transform healthcare delivery, making it more accessible, more affordable, and available anytime and anywhere. mHealth apps create the opportunity for healthcare professionals to engage with their patients, expanding the reach of their practice... [Show full abstract]
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| [55] |
The authors examined online support group members' reliance on their Internet community and other online and offline health resources as they prepare for a scheduled medical appointment. Adult members of an online support group (N = 505) with an upcoming medical appointment completed an online questionnaire that included measures of illness perceptions, control preference, trust in the physician, and eHealth literacy; a checklist of actions one could take to acquire health information; and demographic questions. A factor analysis identified 4 types of information seeking: reliance on the online support group, use of other online health resources, use of offline health resources, and personal network contacts. Previsit information seeking on the Internet was extensive and typically augmented with offline information. Use of online health resources was highest among those who believed they had control over their illness, who attributed many symptoms and negative emotions to it, and who were more eHealth literate. Reliance on the online support group was highest among those who believed they had personal control over their illness, expected their condition to persist, and attributed negative emotions to it. Trust in the physician and preferences for involvement in decision making were unrelated to online information seeking. Most respondents intended to ask their physician questions and request clinical resources based on online information.
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| [56] |
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| [57] |
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| [58] |
The use of the Internet for seekers of health-related information provides convenience and accessibility to diverse sources (of variable quality) for many medical conditions. There is a suggestion that patients may find empowerment by engaging with Internet health care strategies and communities. The profile of consumers of online health information on knee pain has not been explored. Our objective was to identify the characteristics and motivations of online health information-seekers accessing the online health community, KNEEguru (KG). The study was designed to obtain the respondents sociodemographic profile, together with their main reasons and motivations for joining such a community, their health information-seeking behavior, the extent of their knee problems, and their general Internet usage. We undertook an online questionnaire survey, offered to users of the KG website from June to July 2012. A mix of open and closed questions was used to facilitate inductive enquiry. Quantitative responses were analyzed using univariate analysis; qualitative thematic analysis of the open responses was completed and a conceptual model was developed. One-hundred and fifty-two respondents took part (11.56% response rate, 152/1315), with a mean age of 40.1 years. Of this cohort, 61.2% were female, 68.4% were in domestic partnerships, 57.2% were employed, 75.0% had higher education qualifications, and 80.3% were of white/Caucasian ethnicity. Females were associated with joining KG in order to get emotional support from other users (OR 2.11, 95% CI 1.04 - 4.27,P=.04). Respondents self-perception of health was associated with reported quality of life (OR 10.86, 95% CI 3.85 - 30.43,P<.001). Facebook users were associated with joining KG to share experiences (OR 2.34, 95% CI 1.04 - 5.56,P=.03). Post-surgery respondents were associated with joining KG to compare symptoms with other users (OR 7.31, 95% CI 2.06 - 39.82,P<.001). Three key themes were induced: condition, emotion and support. Respondents expressed distress and frustration at uncertainty of prognosis around various knee conditions, with some users preferring to initially observe rather than engage. Conversely, a strong desire to inform and support other community members was stated with reciprocation of ideas and experiences. KG was conceptualized as a filter that takes an individual condition and emotional response to that condition as basis for support; this filter facilitated validation as the outcome of engagement. This study, in line with wider literature, suggests that users of an online knee-specific community are typically female, middle-aged, white/Caucasian, married, employed, and have attained a level of higher education. These users demonstrate a pragmatic approach to health care information with altruistic motivations and a desire to share experiences as a means of validation. This finding emphasizes a means of promoting efficient and appropriate online health care, and demonstrates the benefits of the Internet as a viable complement to clinical engagement.
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| [59] |
Online health communities (OHCs) should utilize health-care knowledge for enhancing online patient support. To examine the use of existing OHCs to identify the challenges and strategies of enhancing online patients' decision-making support, we conducted a descriptive study to evaluate the information availability, user availability and knowledge usability in 100 carefully-selected health-related websites. On the basis of criteria for effective OHCs, we used three evaluation instruments for health-care professionals to review and score the websites. Questionnaire results were examined from the perspective of information, user and knowledge support. Results corroborate that over 80% of the websites facilitate effective social functions, whereas only 33% provide health-care decision-making services to online patients. Approximately 46% of them satisfy four or five effective OHCs' criteria. Three of them only offer the functions of patients' charts and journals to support health data management. Although the existing OHCs are facilitated with good social interaction and support, only a few can assist patients in making effective health-care decisions, not to mention properly using health-care knowledge support.
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| [60] |
Online health communities are becoming more popular in health care. Patients and professionals can communicate with one another online, patients can find peer support, and professionals can use it as an additional information channel to their patients. However, the implementation of online health communities into daily practice is challenging. These challenges relate to the fact that patients need to be activated to (1) become a member (ie, subscription) and (2) participate actively within the community before any effect can be expected. Therefore, we aimed at answering 2 research questions: (1) what factors are associated with subscription to an online health community, and (2) which are associated with becoming an active participant within an online health community. To identify barriers and facilitators as perceived by patients for the implementation of an online health community. We performed a cross-sectional study. Three Dutch fertility clinics (2 IVF-licensed) offered their patients a secure online clinical health community through which clinicians can provide online information and patients can ask questions to the medical team or share experiences and find support from peers. We randomly selected and invited 278 men and women suffering from infertility and attending 1 of the participating clinics. Participants filled out a questionnaire about their background characteristics and current use of the online community. Possible barriers and facilitators were divided into 2 parts: (1) those for subscription to the community, and (2) those for active participation in the community. We performed 2 multivariate logistic regression analyses to calculate determinants for both subscription and active participation. Subscription appeared to be associated with patients background characteristics (eg, gender, treatment phase), intervention-related facilitators (odds ratio [OR] 2.45, 95% CI 1.14-5.27), and patient-related barriers (OR 0.20, 95% CI 0.08-0.54), such as not feeling the need for such an online health community. After subscription, determinants for participation consisted of aspects related to participant age (OR 0.86, 95% CI 0.76-0.97), length of infertility (OR 1.48, 05% CI 1.09-2.02), and to intervention-related facilitators (OR 5.79, 95% CI 2.40-13.98), such as its reliable character and possibility to interact with the medical team and peers. Implementing an online health community in addition to usual fertility care should be performed stepwise. At least 2 strategies are needed to increase the proportion of patient subscribers and consequently make them active participants. First, the marketing strategy should contain information tailored to different subgroups of the patient population. Second, for a living online health community, incorporation of interactive elements, as well as frequent news and updates are needed. These results imply that involving patients and their needs into the promotion strategy, community design, and implementation are crucial.
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| [61] |
The number of multiple sclerosis (MS) information websites, online communities, and Web-based health education programs has been increasing. However, MS patients willingness to use new ways of communication, such as websites, mobile phone application, short message service, or email with their physician, remains unknown. We designed a questionnaire to evaluate the a priori use of electronic communication methods by MS patients and to assess their acceptance of such tools for communication with their health care providers. We received complete data from 586 MS patients aged between 17 and 73 years. Respondents were surveyed in outpatient clinics across Germany using a novel paper-and-pencil questionnaire. In addition to demographics, the survey items queried frequency of use of, familiarity with, and comfort with using computers, websites, email, and mobile phones. About 90% of all MS patients used a personal computer (534/586) and the Internet (527/586) at least once a week, 87.0% (510/586) communicated by email, and 85.6% (488/570) communicated by mobile phone. When asked about their comfort with using electronic communication methods for communication with health care providers, 20.5% (120/586) accepted communication by mobile Internet application or short message service via mobile phone, 41.0% (240/586) by websites, 54.3% (318/586) by email service, and 67.8% (397/586) by at least one type of electronic communication. The level of a priori use was the best predictor for the acceptance of electronic communication with health care providers. Patients who reported already searching online for health information (odds ratio 2.4,P< .001) and who had already communicated with a physician through a website (odds ratio 3.3,P= .03) reported higher acceptance for Web-based communication. Patients who already scheduled appointments with their mobile phones (odds ratio 2.1,P= .002) were more likely to accept the use of mobile phone applications or short message service for communicating with their physician. The majority of MS patients seen at specialist centers already use modern communication technology regularly. New forms of electronic communication appear to have high levels of acceptance for exchanging information about MS between patients and health care providers. Such methods should be integrated into eHealth services such as electronic health records and patient relationship management systems.
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| [62] |
This study sought to characterize and compare online discussion forums for three conditions: breast cancer, type 1 diabetes and fibromyalgia. Though there has been considerable work examining online support groups, few studies have considered differences in discussion content between health conditions. In addition, in contrast to the extant literature, this study sought to employ a semi-automated approach to examine health-related online communities. Online discussion content for the three conditions was compiled, pre-processed, and clustered at the thread level using the bisecting k-means algorithm. Though the clusters for each condition differed, the clusters fell into a set of common categories: Generic, Support, Patient-Centered, Experiential Knowledge, Treatments/Procedures, Medications, and Condition Management. The cluster analyses facilitate an increased understanding of various aspects of patient experience, including significant emotional and temporal aspects of the illness experience. The clusters highlighted the changing nature of patients information needs. Information provided to patients should be tailored to address their needs at various points during their illness. In addition, cluster analysis may be integrated into online support groups or other types of online interventions to assist patients in finding information.
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| [63] |
Abstract Objectives: Social media, including online health communities, have become popular platforms for individuals to discuss health challenges and exchange social support with others. These platforms can provide support for individuals who are concerned about social stigma and discrimination associated with their illness. Although mental health conditions can share similar symptoms and even co-occur, the extent to which discussion topics in online mental health communities are similar, different, or overlapping is unknown. Discovering the topical similarities and differences could potentially inform the design of related mental health communities and patient education programs. This study employs text mining, qualitative analysis, and visualization techniques to compare discussion topics in publicly accessible online mental health communities for three conditions: Anxiety, Depression and Post-Traumatic Stress Disorder. Methods: First, online discussion content for the three conditions was collected from three Reddit communities (r/Anxiety, r/Depression, and r/PTSD). Second, content was pre-processed, and then clustered using the k -means algorithm to identify themes that were commonly discussed by members. Third, we qualitatively examined the common themes to better understand them, as well as their similarities and differences. Fourth, we employed multiple visualization techniques to form a deeper understanding of the relationships among the identified themes for the three mental health conditions. Results: The three mental health communities shared four themes: sharing of positive emotion, gratitude for receiving emotional support, and sleep- and work-related issues. Depression clusters tended to focus on self-expressed contextual aspects of depression, whereas the Anxiety Disorders and Post-Traumatic Stress Disorder clusters addressed more treatment- and medication-related issues. Visualizations showed that discussion topics from the Anxiety Disorders and Post-Traumatic Stress Disorder subreddits shared more similarities to one another than to the depression subreddit. Conclusions: We observed that the members of the three communities shared several overlapping concerns (i.e., sleep- and work-related problems) and discussion patterns (i.e., sharing of positive emotion and showing gratitude for receiving emotional support). We also highlighted that the discussions from the r/Anxiety and r/PTSD communities were more similar to one another than to discussions from the r/Depression community. The r/Anxiety and r/PTSD subreddit members are more likely to be individuals whose experiences with a condition are long-term, and who are interested in treatments and medications. The r/Depression subreddit members may be a comparatively diffuse group, many of whom are dealing with transient issues that cause depressed mood. The findings from this study could be used to inform the design of online mental health communities and patient education programs for these conditions. Moreover, we suggest that researchers employ multiple methods to fully understand the subtle differences when comparing similar discussions from online health communities.
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| [64] |
Depression is a highly prevalent mental health problem and is a co-morbidity of other mental, physical, and behavioural disorders. The internet allows individuals who are depressed or caring for those who are depressed, to connect with others via online communities; however, the characteristics of these discussions have not yet been fully explored. This work aims to explore the textual cues of online communities interested in depression. A total of 5,000 posts were randomly selected from 24 online communities. Five subgroups of online communities were identified: Depression, Bipolar Disorder, Self-Harm, Grief/Bereavement, and Suicide. Psycholinguistic features and content topics were extracted from the posts and analysed. Machine learning techniques were used to discriminate the online conversations in the depression communities from the other subgroups. Topics and psycholinguistic features were found to be highly valid predictors of community subgroup. Clear discrimination between linguistic features and topics, alongside good predictive power is an important step in understanding social media and its use in mental health.
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| [65] |
Abstract Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright 2015, American Association for the Advancement of Science.
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| [66] |
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| [67] |
Although many people with serious diseases participate in online support communities, little research has investigated how participants elicit and provide social support on these sites. The first goal was to propose and test a model of the dynamic process through which participants in online support communities elicit and provide emotional and informational support. The second was to demonstrate the value of computer coding of conversational data using machine learning techniques (1) by replicating results derived from human-coded data about how people elicit support and (2) by answering questions that are intractable with small samples of human-coded data, namely how exposure to different types of social support predicts continued participation in online support communities. The third was to provide a detailed description of these machine learning techniques to enable other researchers to perform large-scale data analysis in these communities. Communication among approximately 90,000 registered users of an online cancer support community was analyzed. The corpus comprised 1,562,459 messages organized into 68,158 discussion threads. Amazon Mechanical Turk workers coded (1) 1000 thread-starting messages on 5 attributes (positive and negative emotional self-disclosure, positive and negative informational self-disclosure, questions) and (2) 1000 replies on emotional and informational support. Their judgments were used to train machine learning models that automatically estimated the amount of these 7 attributes in the messages. Across attributes, the average Pearson correlation between human-based judgments and computer-based judgments was .65. Part 1 used human-coded data to investigate relationships between (1) 4 kinds of self-disclosure and question asking in thread-starting posts and (2) the amount of emotional and informational support in the first reply. Self-disclosure about negative emotions (beta=.24, P<.001), negative events (beta=.25, P<.001), and positive events (beta=.10, P=.02) increased emotional support. However, asking questions depressed emotional support (beta= .21, P<.001). In contrast, asking questions increased informational support (beta=.38, P<.001), whereas positive informational self-disclosure depressed it (beta= .09, P=.003). Self-disclosure led to the perception of emotional needs, which elicited emotional support, whereas asking questions led to the perception of informational needs, which elicited informational support. Part 2 used machine-coded data to replicate these results. Part 3 analyzed the machine-coded data and showed that exposure to more emotional support predicted staying in the group longer 33% (hazard ratio=0.67, P<.001), whereas exposure to more informational support predicted leaving the group sooner (hazard ratio=1.05, P<.001). Self-disclosure is effective in eliciting emotional support, whereas question asking is effective in eliciting informational support. Moreover, perceptions that people desire particular kinds of support influence the support they receive. Finally, the type of support people receive affects the likelihood of their staying in or leaving the group. These results demonstrate the utility of machine learning methods for investigating the dynamics of social support exchange in online support communities.
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| [68] |
It has been shown that online health-related discussions significantly influence the attitudes and behavioral intentions of the discussion participants. Although empirical evidence strongly supports the importance of emotions in health-related online discussions, there are few studies of the relationship between a subjective language and online discussions of personal health. In this work, we study sentiments expressed on online medical forums. Individual posts are classified into one of five categories. We identified three categories as sentimental (encouragement, gratitude, confusion) and two categories as neutral (facts, endorsement). A total of 1438 messages were annotated manually by two annotators with a strong inter-annotator agreement (Fleiss kappa = 0.737 when the posts were annotated in the context of discussion and Fleiss kappa = 0.763 when the posts were annotated as individual entities). Using machine learning multi-class classification approach, we assess the feasibility of automated recognition of the five sentiment categories. As well as considering the predominant sentiments expressed in individual posts, we analyze transitions between sentiments in online discussions.
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| [69] |
[本文引用:1]
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| [70] |
Purpose - The purpose of this paper is to examine how participation in an online health community provides for direct benefits in the form of information utility and social support and an indirect influence on perceptions of patient empowerment.Design/methodology/approach - A multi-method approach was conducted involving interviews with moderators of 18 online health communities and a field survey of 153 online health community participants.Findings - Online health community participation leads to direct benefits in the form of information utility and social support and that information utility also helps to shape perceptions of patient empowerment among community participants.Research limitations/implications - This research calls into question the role of online health communities as a support mechanism to empower patients to take ownership over their healthcare treatment. Online health communities support the development of patient empowerment by creating and disseminating information that can be used to gain an understanding of a patient's health condition.Practical implications -Purveyors of online health communities must be able to ensure a high level of engagement among community participants that allows for each member to elicit outcomes such as information utility, while simultaneously guarding against undesirable circumstances that may prohibit a positive experience.Social implications - Medical professionals can utilize the results of this study to develop strategies for incorporating online health communities into patient care. Specifically, medical professionals can use these results to identify relevant communities and engage in information sharing to ensure relevant and accurate information is disseminated to patients as they seek out information concerning their health conditions.Originality/value - As an ever growing segment of the population looks to online health communities for health information seeking and emotional support, we still know very little as to the type of support that is provided by these forums and how benefits obtained from participation help to shape patient empowerment outcomes. This study determined that information utility and social support are two benefits obtained by online health community participants and that information utility also helps to shape perceptions of patient empowerment among community participants.
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| [71] |
[本文引用:1]
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