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中文摘要
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摘要 随着患者在其医疗保健中越来越多地发挥更积极的作用,互联网已成为一个突出的来源, 健康信息,以指导他们的决策和自我管理活动。尽管潜力巨大 许多在网上寻求健康信息的患者报告说, 大量未经过滤的信息和不合格的,以确定质量,准确性和相关性的 信息. 91%的在线健康信息寻求者表示他们需要或想要 导航支持定位适当的健康信息,以适应其不断变化的需求, 了解疾病的发展轨迹。然而,目前的策略,以提高患者的能力,找到可靠的 而网上的相关信息受到静态、时间和资源强度的限制,不够个性化。 推荐系统、整合用户简档和在线活动的信息过滤系统(例如,搜索 历史)可以有效地确定什么信息与个体用户最相关,但是这样的系统 还没有被用于向患者提供健康信息。该提案的总体目标是建立和 实施“具有个性化建议的健康电子图书馆员(HELPeR)”-个性化信息 访问系统,具有适应患者不同方面的混合推荐引擎。这将是 第一个以病人为中心的系统,可以作为一个虚拟的健康图书馆员。辅助 推荐引擎在其整合个体患者的三个维度(即, 信息需求基于用户的个人资料,用户的独特表达的信息兴趣,和水平 用户的疾病相关知识),将患者引导到高度个性化的信息集, 质量,值得信赖,适合每个患者的知识水平。我们选择了卵巢癌(OvCa) 作为我们的初始人群,因为它代表了一种具有多种肿瘤类型和一系列肿瘤的复杂疾病, 需要随时间演变的个性化治疗和支持性护理需求。HELPeR将被安置在 一个独立的网站链接到在线健康社区(OHC)的国家卵巢癌联盟, 国家OvCa倡导组织。为了实现我们实施HELPeR的目标, 建议是:(1)定义用户对个性化健康信息的需求、偏好和期望,(2) 开发和评估HELPeR系统,该系统能够适应三种类型的个人用户特征 在疾病发展过程中,不断变化的信息需求、个人信息偏好和渐进的 癌症相关知识,以及(3)对OvCa患者进行现场试验,以确定可接受性, 在现实世界中的帮助价值。HELPeR可以很容易地适用于过滤其他癌症的信息 和慢性疾病,使其高度转移到患者反复寻求的任何慢性疾病 在线信息,以更好地管理他们的病情。
英文摘要
Abstract As patients increasingly play more active roles in their health care, the Internet has become a prominent source of health information to guide their decision-making and self-management activities. Despite the great potential of the Internet, many patients who sought health information on the web reported feeling overwhelmed by the vast amount of unfiltered information and unqualified to determine the quality, veracity, and relevance of the information. Ninety-one percent of online health information seekers indicate they either need or want navigational support in locating appropriate health information that adapts to their changing needs and knowledge across the disease trajectory. However, current strategies to improve patients’ ability to find reliable and relevant information online are limited by static, time and resource intensity, and not personalized. Recommender systems, information filtering systems that integrate user profiles and online activity (e.g., search history), can efficiently determine what information is the most relevant to an individual user, but such systems have not been used to provide health information to patients. The overall goal of this proposal is to build and implement a “Health E-Librarian with Personalized Recommendations (HELPeR)” - a personalized information access system with a hybrid recommender engine that adapts to different aspects of the patient. This would be the first implementation of a patient-centered system that can serve as a virtual health librarian. The HELPeR recommender engine is innovative in its capacity to integrate three dimensions of an individual patient (i.e., information needs based on the user’s profile, the user’s unique expressed information interests, and the level of user’s disease-related knowledge) to direct patients to highly personalized sets of information, that are high quality, trustworthy, and appropriate for each patient’s knowledge level. We have selected ovarian cancer (OvCa) as our initial population as it represents a complex disease with multiple tumor types and a range of prognoses, requiring personalized treatments and supportive care needs that evolve over time. HELPeR will be housed on a standalone website linked to the online health community (OHC) of the National Ovarian Cancer Coalition, a national OvCa advocacy organization. In order to attain our goal of implementing HELPeR, the aims of this proposal are: (1) Define user needs, preferences, and expectations for personalized health information, (2) Develop and evaluate the HELPeR system that is able to adapt to three types of individual user characteristics across the disease trajectory evolving information needs, personal information preferences, and progressive cancer-related knowledge, and (3) Conduct a field trial with OvCa patients to determine the acceptability and value of HELPeR in a real-world setting. HELPeR can be easily adapted to filter information for other cancers and chronic conditions, making it highly transferable to any chronic disease where patients repeatedly seek online information to better manage their condition.
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Tailoring Responses to ADRD Caregivers' InfOrmation wants (TRACO) through human-machine collaboration
  • 批准号:
    10670479
  • 项目类别:
  • 资助金额:
    $56.14万
  • 财政年份:
    2022
  • 负责人:
    Daqing He
  • 依托单位:
Development and Implementation of a Health e-Librarian with Personalized Recommender (HELPeR)
海外基金