Epilepsy is a common neurological disorder characterised by recurrent seizures. Almost half of patients who have an unprovoked first seizure (UFS) have additional seizures and develop epilepsy. No current predictive models exist to determine who has a higher risk of recurrence to guide treatment. Emerging evidence suggests alterations in cognition, mood and brain connectivity exist in the population with UFS. Baseline evaluations of these factors following a UFS will enable the development of the first multimodal biomarker-based predictive model of seizure recurrence in adults with UFS.

200 patients and 75 matched healthy controls (aged 18–65) from the Kingston and Halifax First Seizure Clinics will undergo neuropsychological assessments, structural and functional MRI, and electroencephalography. Seizure recurrence will be assessed prospectively. Regular follow-ups will occur at 3, 6, 9 and 12 months to monitor recurrence. Comparisons will be made between patients with UFS and healthy control groups, as well as between patients with and without seizure recurrence at follow-up. A multimodal machine-learning model will be trained to predict seizure recurrence at 12 months.

This study was approved by the Health Sciences and Affiliated Teaching Hospitals Research Ethics Board at Queen’s University (DMED-2681-22) and the Nova Scotia Research Ethics Board (1028519). It is supported by the Canadian Institutes of Health Research (PJT-183906). Findings will be presented at national and international conferences, published in peer-reviewed journals and presented to the public via patient support organisation newsletters and talks.

NCT05724719.

Despite their widespread use, the evidence base for the effectiveness of quality improvement collaboratives remains mixed. Lack of clarity about ‘what good looks like’ in collaboratives remains a persistent problem. We aimed to identify the distinctive features of a state-wide collaboratives programme that has demonstrated sustained improvements in quality of care in a range of clinical specialties over a long period.

Qualitative case study involving interviews with purposively sampled participants, observations and analysis of documents.

The Michigan Collaborative Quality Initiatives programme.

38 participants, including clinicians and managers from 10 collaboratives, and staff from the University of Michigan and Blue Cross Blue Shield of Michigan.

We identified five features that characterised success in the collaboratives programme: learning from positive deviance; high-quality coordination; high-quality measurement and comparative performance feedback; careful use of motivational levers; and mobilising professional leadership and building community. Rigorous measurement, securing professional leadership and engagement, cultivating a collaborative culture, creating accountability for quality, and relieving participating sites of unnecessary burdens associated with programme participation were all important to high performance.

Our findings offer valuable learning for optimising collaboration-based approaches to improvement in healthcare, with implications for the design, structure and resourcing of quality improvement collaboratives. These findings are likely to be useful to clinicians, managers, policy-makers and health system leaders engaged in multiorganisational approaches to improving quality and safety.