Fusiak Jakub, Mansmann Ulrich, Hoffmann Verena S
Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Faculty of Medicine, LMU Munich, Munich, Germany.
JBI Evid Synth. 2024 Dec 1;22(12):2593-2600. doi: 10.11124/JBIES-23-00498.
The objective of this scoping review is to identify and map methods used to incorporate patient preferences into medical algorithms and models as well as to report on their quantification, balancing, and evaluation in the literature. The review will focus on computational methods for incorporating patient preferences into algorithms and models at an individual level as well as the types of medical algorithms and models in which these methods have been applied.
Medical algorithms and models are increasingly being used to support clinical and shared decision-making; however, their effectiveness, accuracy, acceptance, and comprehension may be limited if patients' preferences are not considered. To address this issue, it is important to explore methods integrating patient preferences.
This review will investigate patient preferences and their integration into medical algorithms and models for individual-level clinical decision-making. The scoping review will include diverse sources, such as peer-reviewed articles, clinical practice guidelines, gray literature, government reports, guidelines, and expert opinions for a comprehensive investigation of the subject.
This scoping review will follow JBI methodology. A comprehensive search will be conducted in PubMed, Web of Science, ACM Digital Library, IEEE Xplore, the Cochrane Library, OpenGrey, the National Technical Reports Library, and the first 20 pages of Google Scholar. The search strategy will include keywords related to patient preferences, medical algorithms and models, decision-making, and software tools and frameworks. Data extraction and analysis will be guided by the JBI framework, which includes an explorative and qualitative analysis.
Open Science Framework https://osf.io/qg3b5.
本综述的目的是识别和梳理将患者偏好纳入医学算法和模型的方法,以及报告这些方法在文献中的量化、权衡和评估情况。该综述将聚焦于在个体层面将患者偏好纳入算法和模型的计算方法,以及应用这些方法的医学算法和模型类型。
医学算法和模型越来越多地用于支持临床决策和共同决策;然而,如果不考虑患者偏好,它们的有效性、准确性、可接受性和可理解性可能会受到限制。为解决这一问题,探索整合患者偏好的方法很重要。
本综述将研究患者偏好及其在个体层面临床决策的医学算法和模型中的整合情况。范围综述将包括多种来源,如同行评审文章、临床实践指南、灰色文献、政府报告、指南和专家意见,以便对该主题进行全面调查。
本范围综述将遵循JBI方法。将在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以及谷歌学术的前20页进行全面搜索。搜索策略将包括与患者偏好、医学算法和模型、决策以及软件工具和框架相关的关键词。数据提取和分析将以JBI框架为指导,该框架包括探索性和定性分析。
开放科学框架https://osf.io/qg3b5 。