Schuler Katharina, Sedlmayr Martin, Sedlmayr Brita
Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technical University Dresden, Dresden, Germany.
Stud Health Technol Inform. 2024 Aug 22;316:1754-1758. doi: 10.3233/SHTI240769.
Clinical decision support systems (CDSS) can efficiently support doctors in coping with ever-increasing amounts of data by providing evidence-based recommendations for medical decisions. To integrate the systems into the medical workflow and provide patient-specific recommendations for action in the context of personalized medicine, it is essential to tailor the systems to the context of use. This study aims to present an overview of factors influencing medical decision-making that CDSS must consider. Our approach involves the systematic identification and categorization of contextual factors relevant to medical decision-making. Through extensive literature research and a structured card-sorting workshop, we systematized 774 context factors and mapped them into a model. This model includes six primary entities: the treating physician, the patient, the patient's family, disease treatment, the physician's institution, and professional colleagues, each with their relevant context categories. The developed model could serve as a foundation for communication between developers and physicians, supporting the creation of more context-sensitive CDSS in the future. Ultimately, this could enhance the utilization of CDSS and improve patient care.
临床决策支持系统（CDSS）可以通过为医疗决策提供循证建议，有效地支持医生应对日益增长的数据量。为了将这些系统集成到医疗工作流程中，并在个性化医疗的背景下提供针对患者的行动建议，根据使用环境定制系统至关重要。本研究旨在概述CDSS必须考虑的影响医疗决策的因素。我们的方法包括对与医疗决策相关的背景因素进行系统识别和分类。通过广泛的文献研究和结构化的卡片分类研讨会，我们将774个背景因素系统化并将它们映射到一个模型中。该模型包括六个主要实体：主治医生、患者、患者家属、疾病治疗、医生所在机构和专业同事，每个实体都有其相关的背景类别。所开发的模型可为开发者和医生之间的沟通奠定基础，支持未来创建更具情境敏感性的CDSS。最终，这可以提高CDSS的利用率并改善患者护理。
