Clifton David A, Clifton Lei, Sandu Dona-Maria, Smith G B, Tarassenko Lionel, Vollam Sarah A, Watkinson Peter J
Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
Centre for Statistics in Medicine, University of Oxford, Oxford, UK.
BMJ Open. 2015 Jul 3;5(7):e007376. doi: 10.1136/bmjopen-2014-007376.
To understand factors associated with errors using an established paper-based early warning score (EWS) system. We investigated the types of error, where they are most likely to occur, and whether 'errors' can predict subsequent changes in patient vital signs.
Retrospective analysis of prospectively collected early warning system database from a single large UK teaching hospital.
16,795 observation sets, from 200 postsurgical patients, were collected. Incomplete observation sets were more likely to contain observations which should have led to an alert than complete observation sets (15.1% vs 7.6%, p<0.001), but less likely to have an alerting score correctly calculated (38.8% vs 30.0%, p<0.001). Mis-scoring was much more common when leaving a sequence of three or more consecutive observation sets with aggregate scores of 0 (55.3%) than within the sequence (3.0%, p<0.001). Observation sets that 'incorrectly' alerted were more frequently followed by a correctly alerting observation set than error-free non-alerting observation sets (14.7% vs 4.2%, p<0.001). Observation sets that 'incorrectly' did not alert were more frequently followed by an observation set that did not alert than error-free alerting observation sets (73.2% vs 45.8%, p<0.001).
Missed alerts are particularly common in incomplete observation sets and when a patient first becomes unstable. Observation sets that 'incorrectly' alert or 'incorrectly' do not alert are highly predictive of the next observation set, suggesting that clinical staff detect both deterioration and improvement in advance of the EWS system by using information not currently encoded within it. Work is urgently needed to understand how best to capture this information.
了解使用既定的纸质早期预警评分(EWS)系统时与错误相关的因素。我们调查了错误的类型、最可能出现错误的位置,以及“错误”是否能预测患者生命体征的后续变化。
对来自英国一家大型教学医院的前瞻性收集的早期预警系统数据库进行回顾性分析。
收集了来自200名术后患者的16795组观察数据。与完整的观察数据相比,不完整的观察数据更有可能包含本应发出警报的观察结果(15.1%对7.6%,p<0.001),但正确计算出警报分数的可能性更小(38.8%对30.0%,p<0.001)。当出现连续三个或更多总分为0的观察数据序列时,误评分更为常见(55.3%),而在序列内则较少见(3.0%,p<0.001)。“错误地”发出警报的观察数据之后更有可能紧接着出现正确发出警报的观察数据,而不是无错误未发出警报的观察数据(14.7%对4.2%,p<0.001)。“错误地”未发出警报的观察数据之后更有可能紧接着出现未发出警报的观察数据,而不是无错误发出警报的观察数据(73.2%对45.8%,p<0.001)。
漏报在不完整的观察数据中以及患者首次出现不稳定时尤为常见。“错误地”发出警报或“错误地”未发出警报的观察数据对下一组观察数据具有高度预测性,这表明临床工作人员通过使用EWS系统目前未编码的信息,能在EWS系统之前提前察觉到病情的恶化和改善。迫切需要开展工作以了解如何最好地获取这些信息。