Universidad de Sevilla, ETSII, Avda. Reina Mercedes S/N., 41012, Seville, Spain; CAPHRI Care and Public Health Research Institute, Health Promotion, Maastricht University, CAPHRI, Department of Health Promotion, Faculty of Health, Medicine and Life Sciences, Peter Debyeplein 1, 6229 HA Maastricht, P.O. Box 616 6200, MD, Maastricht, Netherlands.
Universidad de Sevilla, ETSII, Avda. Reina Mercedes S/N., 41012, Seville, Spain.
Int J Med Inform. 2018 Jun;114:143-155. doi: 10.1016/j.ijmedinf.2017.12.018. Epub 2017 Dec 28.
Recommender systems are information retrieval systems that provide users with relevant items (e.g., through messages). Despite their extensive use in the e-commerce and leisure domains, their application in healthcare is still in its infancy. These systems may be used to create tailored health interventions, thus reducing the cost of healthcare and fostering a healthier lifestyle in the population.
This paper identifies, categorizes, and analyzes the existing knowledge in terms of the literature published over the past 10 years on the use of health recommender systems for patient interventions. The aim of this study is to understand the scientific evidence generated about health recommender systems, to identify any gaps in this field to achieve the United Nations Sustainable Development Goal 3 (SDG3) (namely, "Ensure healthy lives and promote well-being for all at all ages"), and to suggest possible reasons for these gaps as well as to propose some solutions.
We conducted a scoping review, which consisted of a keyword search of the literature related to health recommender systems for patients in the following databases: ScienceDirect, PsycInfo, Association for Computing Machinery, IEEExplore, and Pubmed. Further, we limited our search to consider only English-language journal articles published in the last 10 years. The reviewing process comprised three researchers who filtered the results simultaneously. The quantitative synthesis was conducted in parallel by two researchers, who classified each paper in terms of four aspects-the domain, the methodological and procedural aspects, the health promotion theoretical factors and behavior change theories, and the technical aspects-using a new multidisciplinary taxonomy.
Nineteen papers met the inclusion criteria and were included in the data analysis, for which thirty-three features were assessed. The nine features associated with the health promotion theoretical factors and behavior change theories were not observed in any of the selected studies, did not use principles of tailoring, and did not assess (cost)-effectiveness.
Health recommender systems may be further improved by using relevant behavior change strategies and by implementing essential characteristics of tailored interventions. In addition, many of the features required to assess each of the domain aspects, the methodological and procedural aspects, and technical aspects were not reported in the studies.
The studies analyzed presented few evidence in support of the positive effects of using health recommender systems in terms of cost-effectiveness and patient health outcomes. This is why future studies should ensure that all the proposed features are covered in our multidisciplinary taxonomy, including integration with electronic health records and the incorporation of health promotion theoretical factors and behavior change theories. This will render those studies more useful for policymakers since they will cover all aspects needed to determine their impact toward meeting SDG3.
推荐系统是一种信息检索系统，它为用户提供相关项目（例如通过消息）。尽管它们在电子商务和休闲领域得到了广泛应用，但在医疗保健领域的应用仍处于起步阶段。这些系统可用于创建定制的健康干预措施，从而降低医疗保健成本，并促进人口的更健康生活方式。
本文旨在识别、分类和分析过去 10 年中关于使用健康推荐系统进行患者干预的文献中已有的知识。本研究的目的是了解关于健康推荐系统产生的科学证据，确定该领域的空白，以实现联合国可持续发展目标 3（SDG3）（即“确保所有人在所有年龄段的健康生活和福祉”），并提出造成这些空白的可能原因，并提出一些解决方案。
我们进行了范围界定审查，该审查包括在以下数据库中对与患者健康推荐系统相关的文献进行关键字搜索：ScienceDirect、PsycInfo、Association for Computing Machinery、IEEExplore 和 Pubmed。此外，我们将搜索范围仅限于过去 10 年内仅发表英文期刊文章的研究。审查过程由同时筛选结果的三位研究人员进行。两位研究人员同时进行定量综合，他们根据四个方面对每篇论文进行分类-领域、方法学和程序方面、健康促进理论因素和行为改变理论以及技术方面-使用新的多学科分类法。
符合纳入标准并纳入数据分析的论文有 19 篇，评估了 33 个特征。在选定的研究中，没有观察到与健康促进理论因素和行为改变理论相关的九个特征，没有使用定制原则，也没有评估（成本）-效果。
通过使用相关的行为改变策略并实施定制干预措施的基本特征，可以进一步改进健康推荐系统。此外，许多评估每个领域方面、方法学和程序方面以及技术方面所需的特征在研究中都没有报告。
分析的研究在成本效益和患者健康结果方面支持使用健康推荐系统的积极影响方面提供的证据很少。因此，未来的研究应确保在我们的多学科分类法中涵盖所有提议的特征，包括与电子健康记录的集成以及健康促进理论因素和行为改变理论的纳入。这将使这些研究对政策制定者更有用，因为它们将涵盖确定其对实现 SDG3 影响所需的所有方面。
