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The Harvard medical practice study trigger system performance in deceased patients

机译:哈佛医学实践研究触发死者的系统性能

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To detect possible threats to quality and safety, multiple systems have been developed. One of them is retrospective chart review. A team of experts scrutinizes medical records, selected by trigger systems, to detect possible adverse events (AEs). The most important AEs and more hints for possible improvement of care appear in deceased patients. Using triggers in a sample of these patients might increase the performance and lower the burden of scrutinizing records without possible preventable AEs. The aim of this study was therefore to determine the performance of the trigger system in a sample of deceased patients and to calculate the specificity and the sensitivity of this trigger system for predicting AEs. We performed a study in which the records of deceased patients were screened for triggers by a team of trained nurses. A sample of 100 medical records was randomly selected out of records which had been screened between 2012 and 2015 for the first time, prior to the study in 2016. For the determination of significant differences between the first and second screening, McNemar's test of symmetry was used. Also, observed agreement, Cohen's Kappa and prevalence-adjusted and-bias-adjusted-kappa (PABAK) statistics were calculated. This was done for the two trigger rounds on both any trigger present and for every trigger separately. The observed agreement for any given trigger was 75% with a Kappa and PABAK of 0.5. For the individual triggers, the observed agreement was on average 90%. The corresponding Kappa was on average 0.42 (range: -?0.03-0.78) and the average PABAK was 0.8 (range: 0.44-0.92). Two adverse events were found in cases without triggers previously. The recalculated specificity and sensitivity for the original population were 58 and 92% respectively. For the reproducibility of triggers it seems that some perform better than others, but on average this is to our opinion suboptimal. The low specificity implies that many records are selected without AEs. This leads to a high false-positive rate making this labour-intensive record review process costly. Therefore, research for better and more expedient systems is required.
机译:为了检测对质量和安全的可能威胁,已经开发了多种系统。其中一个是回顾性图表审查。专家组团队审查由触发系统选择的医疗记录,以检测可能的不良事件(AES)。最重要的AES和更多暗示用于可能改善护理的患者出现。在这些患者的样本中使用触发器可能会增加性能并降低审查记录的负担,而无需可预防的AES。因此,本研究的目的是确定触发系统在已故患者样本中的触发系统的性能,并计算该触发系统的特异性和灵敏度来预测AES。我们进行了一项研究,其中通过培训的护士团队筛查了死者患者的记录。在2016年研究之前,在2012年至2015年第一次被筛选的记录中随机选择了100名医学记录的样本。为了确定第一和第二筛选之间的显着差异,McNemar对称性的对称性是用过的。此外,考虑了观察到的协议,Cohen的Kappa和普及调整后的和偏见调整的-Kappa(Pabak)统计数据。这是在两个触发器上的两个触发器上完成的,并且每次触发器都是单独的。观察到的任何给定触发的协议为75%,kappa和pabak为0.5。对于个体触发,观察到的协议平均为90%。相应的Kappa平均为0.42(范围: - ?0.03-0.78),平均pabak为0.8(范围:0.44-0.92)。在没有先前触发的情况下发现了两个不良事件。原始群体的重新计算的特异性和敏感度分别为58%和92%。对于触发器的再现性,似乎有些人比其他人表现更好,但平均这是我们的意见次优。低特异性意味着选择许多记录没有AES。这导致了高伪阳性率,使得这种劳动密集型记录审查过程昂贵。因此,需要研究更好,更有利的系统。

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