Learning from medical errors to prevent their recurrence is an important component of any healthcare system's quality and safety improvement functions. Traditionally, this been achieved principally from review of adverse clinical outcomes. The opportunity to learn systematically and in a system manner from patient complaints and litigation has been less well harnessed. Herein we describe the pathways and processes for both patient complaints and medicolegal claims in Victoria, and Australia more broadly, and assess the potential for these to be used for system improvement. We conclude that both patient complaints and medicolegal claims could afford the potential to additionally inform and direct safety and quality improvement. At present neither patient complaints nor medicolegal claims are used systematically to improve patient safety. We identify how this may be done, particularly through sharing findings across agencies. Patient complaints and medicolegal claims are accepted parts of the healthcare industry. However, using these in a shared and collated manner as part of an improvement agenda has not been widely considered or proposed. This paper provides a summary of the patient complaint and medicolegal landscape in public hospital system in Australia broadly, and Victoria more specifically, identifying the agencies involved and the opportunities for sharing learnings. The paper draws on existing literature and experiences from both Australia and elsewhere to propose a framework whereby complaints and claims data could be shared systematically and strategically to reduce future harm and improve patient care. We offer an approach for practitioners, healthcare managers and policy makers in all Australian jurisdictions to design and implement a statewide capacity to share patient complaints and medicolegal claims as an additional component of system quality and safety.

Background: The Healthcare Complaints Analysis Tool (HCAT) is a coding taxonomy developed to interrogate patient complaints for quality and safety improvement lessons. The reliability of the tool has been tested in whole-of-system and whole-of-service settings. We sought to assess whether the taxonomy is functional at the level of a single hospital department. Objectives: To demonstrate the feasibility of applying HCAT in the setting of a large maternity department with a view to using it to inform quality and safety improvement opportunities. Methods: All 200 de-identified complaints made between 1 April 2011 and 30 April 2016 to a multi-site maternity service were collated. Each complaint entry included a summary of complaint content, complaint report date, complaint closure date and an incident severity rating (ISR). HCAT was applied to the analysis of complaints using a previously validated content analysis framework. A coding flowchart was developed to aid classification. Results: The 200 complaints involved 567 issues, an average of 2.8 issues per complaint. The most common issues were rude behaviour (n ¼ 46), poor communication (n ¼ 38), complaints relating to the quality of medical care (n ¼ 36), nursing care (n ¼ 35), surgical/medical complications (n ¼ 28) and complaints relating to the attitude of staff members (n ¼ 23). Complaints in the clinical domain made up the greatest proportion of both severe (ISR 1-66.7%) and moderate (ISR 2-64.5%) incidents. Conclusions: Using a reliable taxonomy, we were able to successfully interrogate patient complaints, identifying quality improvement targets within a single maternity service. The taxonomy appears suitable for adoption and application across health jurisdictions.

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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