Using patient factors to predict obstetric complaints and litigation: A mixed methods approach to quality improvement

Abstract

Background: Traditionally, managing patient complaints and medicolegal claims has been largely a reactive process. However, attention has recently turned to systematically learning from complaints and litigation to prevent recurrence. Within a high-volume maternity service, we explored whether developing predictive tools for patient complaints and litigation to support proactive management was feasible. Objectives: To develop and assess two screening tools to predict the likelihood of (i) patient complaints and/or (ii) medicolegal claims arising from maternity care and to assess practitioner awareness of patient risk factors. Methods: Births between 1 April 2011 and 30 April 2016 at a university hospital maternity service in Melbourne, Australia were considered. Univariate binary logistic regression was performed to identify the variables contributing to complaints and claims. Backwards-stepwise logistic regression was applied to develop each screening tool. Clinicians completed a survey to assess awareness of identified risk factors. Results: In the study period, there were 41,443 births, 173 complaints and 19 claims. The complaints tool had only fair predictive capacity (receiver operating characteristic 0.72, p < 0.001) and the claims tool failed. Neither approach afforded sufficient discrimination to be useful in routine predictive modelling. One hundred and one practitioners completed the survey (response rate 15.7%). Practitioners were better at recognising risk factors for legal claims than for patient complaints. Conclusion: Whilst new risk factors for patient complaints and medicolegal claims were identified, we were unable to develop a screening tool that was sufficiently discriminatory to be useful in routine predictive triaging. However, increasing practitioner awareness of key risk factors may afford opportunities to improve care quality.

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