Case-mix adjusted postanaesthesia care unit length of stay and business intelligence dashboards for feedback to anaesthetists

Abstract

Background 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. Methods 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. Results The neural network accounted for over half of observed variation in individual doctors' mean PACU LOS (mean predicted and mean actual LOS Spearman's r2=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. Conclusion 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.