Tsopra R, Venot A, Duclos C
INSERM, U1142, LIMICS, F-75006, Paris, France ; Université Paris 13, Sorbonne Paris Cité, F-93000, Bobigny, France ; Sorbonne Universités, Univ Paris 06, F-75006, Paris, France.
AMIA Annu Symp Proc. 2014 Nov 14;2014:1115-24. eCollection 2014.
Clinical Decision Support Systems (CDSS) incorporating justifications, updating and adjustable recommendations can considerably improve the quality of healthcare. We propose a new approach to the design of CDSS for empiric antibiotic prescription, based on implementation of the deeper medical reasoning used by experts in the development of clinical practice guidelines (CPGs), to deduce the recommended antibiotics.
We investigated two methods ("exclusion" versus "scoring") for reproducing this reasoning based on antibiotic properties.
The "exclusion" method reproduced expert reasoning the more accurately, retrieving the full list of recommended antibiotics for almost all clinical situations.
This approach has several advantages: (i) it provides convincing explanations for physicians; (ii) updating could easily be incorporated into the CDSS; (iii) it can provide recommendations for clinical situations missing from CPGs.
包含理由说明、更新及可调整建议的临床决策支持系统(CDSS)能显著提高医疗质量。我们基于临床实践指南(CPG)制定过程中专家所运用的更深入医学推理的实施,提出了一种用于经验性抗生素处方的CDSS设计新方法,以推断推荐使用的抗生素。
我们研究了两种基于抗生素特性重现此推理的方法(“排除法 ”与 “评分法”)。
“排除法 ”更准确地重现了专家推理,几乎能检索出所有临床情况的推荐抗生素完整列表。
此方法具有几个优点:(i)为医生提供有说服力的解释;(ii)更新可轻松纳入CDSS;(iii)可为CPG中未涵盖的临床情况提供建议。