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.