Preventing critical failure. Can routinely collected data be repurposed to predict avoidable patient harm? A quantitative descriptive study Academic Article uri icon


  • ObjectivesTo determine whether sharing of routinely collected health service performance data could have predicted a critical safety failure at an Australian maternity service.DesignObservational quantitative descriptive study.SettingA public hospital maternity service in Victoria, Australia.Data sourcesA public health service; the Victorian state health quality and safety office—Safer Care Victoria; the Health Complaints Commission; Victorian Managed Insurance Authority; Consultative Council on Obstetric and Paediatric Mortality and Morbidity; Paediatric Infant Perinatal Emergency Retrieval; Australian Health Practitioner Regulation Agency.Main outcome measuresNumbers and rates for events (activity, deaths, complaints, litigation, practitioner notifications). Correlation coefficients.ResultsBetween 2000 and 2014 annual birth numbers at the index hospital more than doubled with no change in bed capacity, to be significantly busier than similar services as determined using an independent samples t-test (p<0.001). There were 36 newborn deaths, 11 of which were considered avoidable. Pearson correlations revealed a weak but significant relationship between number of births per birth suite room birth and perinatal mortality (r2=0.18, p=0.003). Independent samples t-tests demonstrated that the rates of emergency neonatal and perinatal transfer were both significantly lower than similar services (both p<0.001). Direct-to-service patient complaints increased ahead of recognised excess perinatal mortality.ConclusionWhile clinical activity data and direct-to-service patient complaints appear to offer promise as potential predictors of health service stress, complaints to regulators and medicolegal activity are less promising as predictors of system failure. Significant changes to how all data are handled would be required to progress such an approach to predicting health service failure.


  • Nowotny, BM
  • Davies-Tuck, M
  • Scott, B
  • Stewart, M
  • Cox, E
  • Cusack, K
  • Fletcher, M
  • Saar, E
  • Farrell, Tanya
  • Anil, S
  • McKinlay, L
  • Wallace, EM

publication date

  • 2019