Fusiak Jakub, Sarpari Kousha, Ma Inger, Mansmann Ulrich, Hoffmann Verena S
The Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-Universität, Marchioninistr. 15, 81377, Munich, Bavaria, Germany.
BMC Med Inform Decis Mak. 2025 Mar 3;25(1):109. doi: 10.1186/s12911-025-02945-5.
Algorithms and models increasingly support clinical and shared decision-making. However, they may be limited in effectiveness, accuracy, acceptance, and comprehensibility if they fail to consider patient preferences. Addressing this gap requires exploring methods to integrate patient preferences into model-based clinical decision-making.
This scoping review aimed to identify and map applications of computational methods for incorporating patient preferences into individualized medical decision models and to report on the types of models where these methods are applied.
This review includes articles without restriction on publication date or language, focusing on practical applications. It examines the integration of patient preferences in models for individualized clinical decision-making, drawing on diverse sources, including both white and gray literature, for comprehensive insights.
Following the Joanna Briggs Institute (JBI) methodology, a comprehensive search was conducted across databases such as PubMed, Web of Science, ACM Digital Library, IEEE Xplore, Cochrane Library, OpenGrey, National Technical Reports Library, and the first 20 pages of Google Scholar. Keywords related to patient preferences, medical models, decision-making, and software tools guided the search strategy. Data extraction and analysis followed the JBI framework, with an explorative analysis.
From 7074 identified and 7023 screened articles, 45 publications on specific applications were reviewed, revealing significant heterogeneity in incorporating patient preferences into decision-making tools. Clinical applications primarily target neoplasms and circulatory diseases, using methods like Multi-Criteria Decision Analysis (MCDA) and statistical models, often combining approaches. Studies show that incorporating patient preferences can significantly impact treatment decisions, underscoring the need for shared and personalized decision-making.
This scoping review highlights a wide range of approaches for integrating patient preferences into medical decision models, underscoring a critical gap in the use of cohesive frameworks that could enhance consistency and clinician acceptance. While the flexibility of current methods supports tailored applications, the limited use of existing frameworks constrains their potential. This gap, coupled with minimal focus on clinician and patient engagement, hinders the real-world utility of these tools. Future research should prioritize co-design with clinicians, real-world testing, and impact evaluation to close this gap and improve patient-centered care.
算法和模型越来越多地支持临床决策和共同决策。然而，如果它们未能考虑患者偏好，其有效性、准确性、可接受性和可理解性可能会受到限制。解决这一差距需要探索将患者偏好纳入基于模型的临床决策的方法。
本范围综述旨在识别和梳理将患者偏好纳入个体化医疗决策模型的计算方法的应用，并报告应用这些方法的模型类型。
本综述纳入对发表日期或语言无限制的文章，重点关注实际应用。它考察了患者偏好在个体化临床决策模型中的整合情况，借鉴了包括白色文献和灰色文献在内的多种来源，以获得全面的见解。
按照乔安娜·布里格斯研究所（JBI）的方法，在PubMed、科学网、ACM数字图书馆、IEEE Xplore、考科蓝图书馆、OpenGrey、国家技术报告图书馆等数据库以及谷歌学术搜索的前20页进行了全面检索。与患者偏好、医学模型、决策制定和软件工具相关的关键词指导了检索策略。数据提取和分析遵循JBI框架，并进行了探索性分析。
从7074篇已识别和7023篇筛选的文章中，对45篇关于特定应用的出版物进行了综述，结果显示在将患者偏好纳入决策工具方面存在显著异质性。临床应用主要针对肿瘤和循环系统疾病，使用多标准决策分析（MCDA）和统计模型等方法，且常常将多种方法结合使用。研究表明，纳入患者偏好会显著影响治疗决策，这凸显了共同决策和个性化决策的必要性。
本范围综述强调了将患者偏好纳入医学决策模型的多种方法，突出了在使用能够增强一致性和临床医生接受度的连贯框架方面存在的关键差距。虽然当前方法的灵活性支持量身定制的应用，但现有框架的使用有限限制了其潜力。这一差距，再加上对临床医生和患者参与的关注不足，阻碍了这些工具在现实世界中的效用。未来的研究应优先与临床医生进行共同设计、进行现实世界测试和影响评估，以弥合这一差距并改善以患者为中心的护理。
