Schäfer Hendrik, Lajmi Nesrine, Valente Paolo, Pedrioli Alessandro, Cigoianu Daniel, Hoehne Bernhard, Schenk Michaela, Guo Chaohui, Singhrao Ruby, Gmuer Deniz, Ahmed Rezwan, Silchmüller Maximilian, Ekinci Okan
Clinical Development & Medical Affairs, Roche Diagnostics International Ltd., Forrenstrasse 2, 6343 Rotkreuz, Switzerland.
Medical Faculty, Friedrich Schiller University Jena, 07737 Jena, Germany.
Diagnostics (Basel). 2025 Mar 6;15(5):648. doi: 10.3390/diagnostics15050648.
In a rapidly changing technology landscape, "Clinical Decision Support" (CDS) has become an important tool to improve patient management. CDS systems offer medical professionals new insights to improve diagnostic accuracy, therapy planning, and personalized treatment. In addition, CDS systems provide cost-effective options to augment conventional screening for secondary prevention. This review aims to (i) describe the purpose and mechanisms of CDS systems, (ii) discuss different entities of algorithms, (iii) highlight quality features, and (iv) discuss challenges and limitations of CDS in clinical practice. Furthermore, we (v) describe contemporary algorithms in oncology, acute care, cardiology, and nephrology. In particular, we consolidate research on algorithms across diseases that imply a significant disease and economic burden, such as lung cancer, colorectal cancer, hepatocellular cancer, coronary artery disease, traumatic brain injury, sepsis, and chronic kidney disease.
在快速变化的技术环境中，“临床决策支持”（CDS）已成为改善患者管理的重要工具。CDS系统为医学专业人员提供了新的见解，以提高诊断准确性、治疗规划和个性化治疗。此外，CDS系统提供了具有成本效益的选择，以加强传统的二级预防筛查。本综述旨在：（i）描述CDS系统的目的和机制；（ii）讨论算法的不同实体；（iii）强调质量特征；（iv）讨论CDS在临床实践中的挑战和局限性。此外，我们（v）描述肿瘤学、急性护理、心脏病学和肾脏病学中的当代算法。特别是，我们整合了对涉及重大疾病和经济负担的疾病（如肺癌、结直肠癌、肝细胞癌、冠状动脉疾病、创伤性脑损伤、脓毒症和慢性肾脏病）的算法研究。
