College of Medicine and Public Health, Flinders University, Clovelly Park, Australia.
School of Information Science and Engineering, Shandong Normal University, Jinan, China.
JMIR Hum Factors. 2024 May 29;11:e52027. doi: 10.2196/52027.
In the digital age, search engines and social media platforms are primary sources for health information, yet their commercial interests-focused algorithms often prioritize irrelevant content. Web-based health applications by reputable sources offer a solution to circumvent these biased algorithms. Despite this advantage, there remains a significant gap in research on the effective integration of content-ranking algorithms within these specialized health applications to ensure the delivery of personalized and relevant health information.
This study introduces a generic methodology designed to facilitate the development and implementation of health information recommendation features within web-based health applications.
We detail our proposed methodology, covering conceptual foundation and practical considerations through the stages of design, development, operation, review, and optimization in the software development life cycle. Using a case study, we demonstrate the practical application of the proposed methodology through the implementation of recommendation functionalities in the EndoZone platform, a platform dedicated to providing targeted health information on endometriosis.
Application of the proposed methodology in the EndoZone platform led to the creation of a tailored health information recommendation system known as EndoZone Informatics. Feedback from EndoZone stakeholders as well as insights from the implementation process validate the methodology's utility in enabling advanced recommendation features in health information applications. Preliminary assessments indicate that the system successfully delivers personalized content, adeptly incorporates user feedback, and exhibits considerable flexibility in adjusting its recommendation logic. While certain project-specific design flaws were not caught in the initial stages, these issues were subsequently identified and rectified in the review and optimization stages.
We propose a generic methodology to guide the design and implementation of health information recommendation functionality within web-based health information applications. By harnessing user characteristics and feedback for content ranking, this methodology enables the creation of personalized recommendations that align with individual user needs within trusted health applications. The successful application of our methodology in the development of EndoZone Informatics marks a significant progress toward personalized health information delivery at scale, tailored to the specific needs of users.
在数字时代，搜索引擎和社交媒体平台是获取健康信息的主要来源，但它们以商业利益为导向的算法往往优先推送不相关的内容。信誉良好的来源的基于网络的健康应用程序提供了一种解决方案，可以规避这些有偏见的算法。尽管有这个优势，但在这些专门的健康应用程序中有效整合内容排名算法以确保提供个性化和相关的健康信息方面，仍存在显著的研究差距。
本研究介绍了一种通用方法，旨在促进基于网络的健康应用程序中健康信息推荐功能的开发和实施。
我们详细介绍了我们提出的方法，涵盖了概念基础和通过软件开发生命周期的设计、开发、运营、审查和优化阶段的实际考虑。通过一个案例研究，我们通过在 EndoZone 平台（一个专门提供子宫内膜异位症靶向健康信息的平台）中实现推荐功能，展示了所提出方法的实际应用。
在所提出的方法在 EndoZone 平台中的应用导致创建了一个名为 EndoZone Informatics 的定制健康信息推荐系统。EndoZone 利益相关者的反馈以及实施过程中的见解验证了该方法在使健康信息应用程序中的高级推荐功能成为可能方面的实用性。初步评估表明，该系统成功地提供了个性化的内容，巧妙地整合了用户反馈，并在调整其推荐逻辑方面表现出相当大的灵活性。虽然在初始阶段没有发现某些特定于项目的设计缺陷，但在审查和优化阶段发现并纠正了这些问题。
我们提出了一种通用方法来指导基于网络的健康信息应用程序中健康信息推荐功能的设计和实现。通过利用用户特征和反馈进行内容排名，该方法能够创建与个别用户需求相匹配的个性化推荐，在可信的健康应用程序中实现个性化的健康信息推送。我们的方法在开发 EndoZone Informatics 中的成功应用标志着朝着大规模个性化健康信息推送迈出了重要一步，针对用户的具体需求进行了定制。
