Department of Anaesthesia, Mater Health, Brisbane, Australia.
Department of Anaesthesia, Mater Health, Brisbane, Australia; Mater Clinical Unit, University of Queensland School of Medicine, Brisbane, Australia.
Br J Anaesth. 2020 Dec;125(6):1079-1087. doi: 10.1016/j.bja.2020.06.068. Epub 2020 Aug 27.
Despite advances in business intelligence software and evidence that feedback to doctors can improve outcomes, objective feedback regarding patient outcomes for individual anaesthetists is hampered by lack of useful benchmarks. We aimed to address this issue by producing case-mix and risk-adjusted postanaesthesia care unit (PACU) length of stay (LOS) benchmarks for integration into modern reporting tools.
We extended existing hospital information systems to calculate predicted PACU LOS using a neural network trained on patient age, surgery duration, sex, operating specialty, urgency, weekday, and insurance status (n=100 511). We then calculated the difference between observed mean and predicted PACU LOS for individual doctors, and compared the results with and without case-mix adjustment. We report practical implications of using visual analytics dashboards displaying the difference between observed and predicted PACU LOS to provide feedback to anaesthetic doctors.
The neural network accounted for over half of observed variation in individual doctors' mean PACU LOS (mean predicted and mean actual LOS Spearman's r=0.57). Account for case-mix reduced apparent spread, with 80% of individual doctors falling in a band of 4.3 min after case-mix adjusting, compared with a range of 24 min without adjustment. Case-mix adjusting also identified different individual doctors as outliers (Weighted Cohen's kappa [κ]=0.27). Finally, we demonstrated that we were able to integrate the adjusted metrics into routine reporting tools.
With caution, case-mix adjustment of anaesthetic outcome measures such as PACU LOS potentially provides a useful continuous quality improvement tool. Unadjusted outcome measures are imprecise at best and misleading at worst.
尽管商业智能软件取得了进展，并且有证据表明向医生提供反馈可以改善结果，但由于缺乏有用的基准，因此无法为个别麻醉师提供有关患者结果的客观反馈。我们的目的是通过生成病例组合和风险调整后的麻醉后护理单元（PACU）住院时间（LOS）基准，将其集成到现代报告工具中，从而解决此问题。
我们扩展了现有的医院信息系统，使用针对患者年龄，手术持续时间，性别，手术专业，紧急情况，工作日和保险状况（n=100511）进行训练的神经网络来计算预测的 PACU LOS。然后，我们计算了每个医生的观察到的平均 PACU LOS 和预测的 PACU LOS 之间的差异，并比较了有和没有病例组合调整的结果。我们报告了使用显示观察到的和预测的 PACU LOS 之间差异的可视化分析仪表板为麻醉医生提供反馈的实际意义。
神经网络解释了个体医生的平均 PACU LOS 观察到的变异的一半以上（平均预测和平均实际 LOS Spearman's r=0.57）。病例组合调整后，80％的个体医生的 PACU LOS 在 4.3 分钟的范围内，而没有调整的范围为 24 分钟。病例组合调整还确定了不同的个体医生为异常值（加权 Cohen's kappa [κ]=0.27）。最后，我们证明我们能够将调整后的指标集成到常规报告工具中。
谨慎地调整麻醉结果指标（例如 PACU LOS）的病例组合，这可能是一种有用的持续质量改进工具。未经调整的结果指标最多是不精确的，最坏的情况是具有误导性的。
