Department of Computer Science, Université de Sherbrooke, 2500 boul. de l'Université, Sherbrooke, Québec, Canada J1K 2R1.
Department of Microbiology and Infectious Diseases, Université de Sherbrooke, 3001 12e Avenue Nord, Sherbrooke, Québec, Canada J1H 5N4.
Artif Intell Med. 2016 Mar;68:29-36. doi: 10.1016/j.artmed.2016.02.001. Epub 2016 Feb 22.
Antimicrobial stewardship programs have been shown to limit the inappropriate use of antimicrobials. Hospitals are increasingly relying on clinical decision support systems to assist in the demanding prescription reviewing process. In previous work, we have reported on an emerging clinical decision support system for antimicrobial stewardship that can learn new rules supervised by user feedback. In this paper, we report on the evaluation of this system.
The evaluated system uses a knowledge base coupled with a supervised learning module that extracts classification rules for inappropriate antimicrobial prescriptions using past recommendations for dose and dosing frequency adjustments, discontinuation of therapy, early switch from intravenous to oral therapy, and redundant antimicrobial spectrum. Over five weeks, the learning module was deployed alongside the baseline system to prospectively evaluate its ability to discover rules that complement the existing knowledge base for identifying inappropriate prescriptions of piperacillin-tazobactam, a frequently used antimicrobial.
The antimicrobial stewardship pharmacists reviewed 374 prescriptions, of which 209 (56% of 374) were identified as inappropriate leading to 43 recommendations to optimize prescriptions. The baseline system combined with the learning module triggered alerts in 270 prescriptions with a positive predictive value of identifying inappropriate prescriptions of 74%. Of these, 240 reviewed prescriptions were identified by the alerts of the baseline system with a positive predictive value of 82% and 105 reviewed prescriptions were identified by the alerts of the learning module with a positive predictive value of 62%. The combined system triggered alerts for all 43 recommendations, resulting in a rate of actionable alerts of 16% (43 recommendations of 270 reviewed alerts); the baseline system triggered alerts for 38 interventions, resulting in a rate of actionable alerts of 16% (38 of 240 reviewed alerts); and the learning module triggered alerts for 17 interventions, resulting in a rate of actionable alerts of 16% (17 of 105 reviewed alerts). The learning module triggered alerts for every inappropriate prescription missed by the knowledge base of the baseline system (n=5).
The learning module was able to extract clinically relevant rules for multiple types of antimicrobial alerts. The learned rules were shown to extend the knowledge base of the baseline system by identifying pharmacist interventions that were missed by the baseline system. The learned rules identified inappropriate prescribing practices that were not supported by local experts and were missing from its knowledge base. However, combining the baseline system and the learning module increased the number of false positives.
抗菌药物管理计划已被证明可限制抗菌药物的不当使用。医院越来越依赖临床决策支持系统来协助进行要求苛刻的处方审查过程。在之前的工作中，我们报告了一种新兴的抗菌药物管理临床决策支持系统，该系统可以通过用户反馈来学习新规则。在本文中，我们报告了对该系统的评估。
评估的系统使用知识库和一个监督学习模块，该模块使用过去针对剂量和给药频率调整、停止治疗、早期从静脉内转换为口服治疗以及冗余抗菌谱的建议，为不适当的抗菌药物处方提取分类规则。在五周的时间里，学习模块与基线系统一起部署，以前瞻性地评估其发现规则的能力，这些规则补充了用于识别哌拉西林-他唑巴坦不适当处方的现有知识库，哌拉西林-他唑巴坦是一种常用的抗菌药物。
抗菌药物管理药师审查了 374 份处方，其中 209 份（374 份的 56%）被确定为不适当，导致 43 份建议优化处方。基线系统与学习模块相结合，在 270 份有阳性预测值为 74%的不适当处方中触发了警报。在这些警报中，基线系统的警报审查了 240 份处方，阳性预测值为 82%，学习模块的警报审查了 105 份处方，阳性预测值为 62%。该组合系统为所有 43 项建议触发了警报，导致可操作警报的发生率为 16%（270 份审查警报中有 43 项建议）；基线系统触发了 38 项干预措施的警报，导致可操作警报的发生率为 16%（240 份审查警报中有 38 项）；学习模块触发了 17 项干预措施的警报，导致可操作警报的发生率为 16%（105 份审查警报中有 17 项）。学习模块为基线系统知识库遗漏的每个不适当处方（n=5）都触发了警报。
学习模块能够提取多种类型抗菌药物警报的临床相关规则。所学到的规则通过识别被基线系统遗漏的药师干预措施，显示出扩展基线系统知识库的能力。学习规则确定了不符合当地专家意见且未包含在其知识库中的不适当处方做法。然而，结合基线系统和学习模块增加了假阳性的数量。
