Raszke Pascal, Giebel Godwin Denk, Abels Carina, Wasem Jürgen, Adamzik Michael, Nowak Hartmuth, Palmowski Lars, Heinz Philipp, Mreyen Silke, Timmesfeld Nina, Tokic Marianne, Brunkhorst Frank Martin, Blase Nikola
Institute for Health Care Management and Research, University of Duisburg-Essen, Essen, Germany.
Department of Anaesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Knappschaftskrankenhaus, Ruhr University Bochum, Bochum, Germany.
JMIR Res Protoc. 2025 Jan 30;14:e62704. doi: 10.2196/62704.
Artificial intelligence (AI)-based clinical decision support systems (CDSS) have been developed for several diseases. However, despite the potential to improve the quality of care and thereby positively impact patient-relevant outcomes, the majority of AI-based CDSS have not been adopted in standard care. Possible reasons for this include barriers in the implementation and a nonuser-oriented development approach, resulting in reduced user acceptance.
This research project has 2 objectives. First, problems and corresponding solutions that hinder or support the development and implementation of AI-based CDSS are identified. Second, the research project aims to increase user acceptance by creating a user-oriented requirement profile, using the example of sepsis.
The research project is based on a multimethod approach combining (1) a scoping review, (2) focus groups with physicians and professional caregivers, and (3) semistructured interviews with relevant stakeholders. The research modules mentioned provide the basis for the development of a (4) survey, including a discrete choice experiment (DCE) with physicians. A minimum of 6667 physicians with expertise in the clinical picture of sepsis are contacted for this purpose. The survey is followed by the development of a requirement profile for AI-based CDSS and the derivation of policy recommendations for action, which are evaluated in a (5) expert roundtable discussion.
The multimethod research project started in November 2022. It provides an overview of the barriers and corresponding solutions related to the development and implementation of AI-based CDSS. Using sepsis as an example, a user-oriented requirement profile for AI-based CDSS is developed. The scoping review has been concluded and the qualitative modules have been subjected to analysis. The start of the survey, including the DCE, was at the end of July 2024.
The results of the research project represent the first attempt to create a comprehensive user-oriented requirement profile for the development of sepsis-specific AI-based CDSS. In addition, general recommendations are derived, in order to reduce barriers in the development and implementation of AI-based CDSS. The findings of this research project have the potential to facilitate the integration of AI-based CDSS into standard care in the long term.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/62704.
基于人工智能（AI）的临床决策支持系统（CDSS）已针对多种疾病开发。然而，尽管有潜力提高医疗质量并从而对与患者相关的结果产生积极影响，但大多数基于AI的CDSS尚未在标准护理中得到采用。其可能的原因包括实施过程中的障碍以及非以用户为导向的开发方法，导致用户接受度降低。
本研究项目有两个目标。首先，确定阻碍或支持基于AI的CDSS开发与实施的问题及相应解决方案。其次，该研究项目旨在以脓毒症为例，通过创建以用户为导向的需求概况来提高用户接受度。
该研究项目基于一种多方法途径，结合了（1）范围综述、（2）与医生和专业护理人员的焦点小组讨论以及（3）与相关利益相关者的半结构化访谈。上述研究模块为（4）一项调查的开展提供了基础，该调查包括针对医生的离散选择实验（DCE）。为此，至少联系了6667名具有脓毒症临床表现专业知识的医生。在该调查之后，制定基于AI的CDSS的需求概况并得出行动政策建议，这些将在（5）一次专家圆桌讨论中进行评估。
该多方法研究项目于2022年11月启动。它概述了与基于AI的CDSS开发和实施相关的障碍及相应解决方案。以脓毒症为例，制定了基于AI的CDSS的以用户为导向的需求概况。范围综述已完成，定性模块已进行分析。包括DCE在内的调查于2024年7月底开始。
该研究项目的结果代表了首次尝试为特定于脓毒症的基于AI的CDSS开发创建全面的以用户为导向的需求概况。此外，还得出了一般性建议，以减少基于AI的CDSS开发和实施中的障碍。该研究项目的结果有可能长期促进基于AI的CDSS融入标准护理。
国际注册报告识别号（IRRID）：DERR1-10.2196/62704。
