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Design and Implementation of a Pediatric ICU Acuity Scoring Tool as Clinical Decision Support

机译:儿科ICU视力评分工具作为临床决策支持的设计与实现

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摘要

>Background and Objective  Pediatric in-hospital cardiac arrest most commonly occurs in the pediatric intensive care unit (PICU) and is frequently preceded by early warning signs of clinical deterioration. In this study, we describe the implementation and evaluation of criteria to identify high-risk patients from a paper-based checklist into a clinical decision support (CDS) tool in the electronic health record (EHR). >Materials and Methods  The validated paper-based tool was first adapted by PICU clinicians and clinical informaticians and then integrated into clinical workflow following best practices for CDS design. A vendor-based rule engine was utilized. Littenberg's assessment framework helped guide the overall evaluation. Preliminary testing took place in EHR development environments with more rigorous evaluation, testing, and feedback completed in the live production environment. To verify data quality of the CDS rule engine, a retrospective Structured Query Language (SQL) data query was also created. As a process metric, preparedness was measured in pre- and postimplementation surveys. >Results  The system was deployed, evaluating approximately 340 unique patients monthly across 4 clinical teams. The verification against retrospective SQL of 15-minute intervals over a 30-day period revealed no missing triggered intervals and demonstrated 99.3% concordance of positive triggers. Preparedness showed improvements across multiple domains to our a priori goal of 90%. >Conclusion  We describe the successful adaptation and implementation of a real-time CDS tool to identify PICU patients at risk of deterioration. Prospective multicenter evaluation of the tool's effectiveness on clinical outcomes is necessary before broader implementation can be recommended.
机译:>背景和目的小儿住院心脏骤停最常见于小儿重症监护室(PICU),并经常出现临床恶化的早期预警迹象。在这项研究中,我们描述了标准的实施和评估,以从纸质清单到电子健康记录(EHR)中的临床决策支持(CDS)工具中识别高危患者。 >材料和方法经过验证的纸质工具首先由PICU临床医生和临床信息学家改编,然后按照CDS设计的最佳实践整合到临床工作流程中。利用了基于供应商的规则引擎。利滕伯格的评估框架有助于指导总体评估。初步测试在EHR开发环境中进行,并在实时生产环境中完成了更严格的评估,测试和反馈。为了验证CDS规则引擎的数据质量,还创建了回顾性结构化查询语言(SQL)数据查询。作为过程指标,在实施前和实施后调查中测量了准备情况。 >结果已部署该系统,每月评估4个临床团队中的约340位独特患者。在30天的时间内对15分钟间隔的回顾性SQL进行的验证表明,没有丢失触发间隔,并且显示出99.3%的正触发一致性。备战表明跨多个领域都有所改善,达到了我们90%的优先目标。 >结论我们描述了实时CDS工具的成功改编和实施,以识别有恶化风险的PICU患者。在推荐更广泛的应用之前,有必要对工具在临床结果上的有效性进行多中心评估。

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