Department of Software and Information Systems Engineering, Ben-Gurion University, Israel.
Department of Nursing, Faculty of Health Sciences, Ben-Gurion University, Israel.
Stud Health Technol Inform. 2024 Aug 22;316:1873-1877. doi: 10.3233/SHTI240797.
Medical errors contribute significantly to morbidity and mortality, emphasizing the critical role of Clinical Guidelines (GLs) in patient care. Automating GL application can enhance GL adherence, improve patient outcomes, and reduce costs. However, several barriers exist to GL implementation and real-time automated support. Challenges include creating a formalized, machine-comprehensible GL representation, and an episodic decision-support system for sporadic treatment advice. This system must accommodate the non-continuous nature of care delivery, including partial actions or partially met treatment goals. We describe the design and implementation of an episodic GL-based clinical decision support system and its retrospective technical evaluation using patient records from a geriatric center. Initial evaluation scores of the e-Picard system were promising, with a mean 94% correctness and 90% completeness based on 50 random pressure ulcer patients. Errors were mainly due to knowledge specification, algorithmic issues, and missing data. Post-corrections, scores improved to 100% correctness and a mean 97% completeness, with missing data still affecting completeness. The results validate the system's capability to assess guideline adherence and provide quality recommendations. Despite initial limitations, we have demonstrated the feasibility of providing, through the e-Picard episodic algorithm, realistic medical decision-making support for noncontinuous, intermittent consultations.
医疗失误对发病率和死亡率有重大影响，这强调了临床指南（GL）在患者护理中的关键作用。将 GL 应用自动化可以提高 GL 依从性、改善患者结局并降低成本。然而，GL 的实施和实时自动化支持存在一些障碍。挑战包括创建一个规范化、机器可理解的 GL 表示形式，以及一个用于零星治疗建议的偶发性决策支持系统。该系统必须适应护理提供的非连续性，包括部分行动或部分达到治疗目标。我们描述了一个基于偶发性 GL 的临床决策支持系统的设计和实现，以及使用老年医学中心的患者记录进行的回顾性技术评估。e-Picard 系统的初始评估得分很有希望，基于 50 名随机压疮患者，平均正确率为 94%，完整性为 90%。错误主要归因于知识规范、算法问题和缺失数据。经过更正后，得分提高到 100%的正确率和平均 97%的完整性，缺失数据仍然影响完整性。这些结果验证了该系统评估指南依从性和提供质量建议的能力。尽管存在初始限制，但我们已经证明通过 e-Picard 偶发性算法为非连续、间歇性咨询提供现实的医疗决策支持的可行性。
