INTRODUCTION Neurosurgery remains among the highest malpractice risk specialties. This study aimed to identify areas in neurosurgery associated with litigation, attendant causes and costs. METHODS Retrospective analysis was conducted of 42 closed litigation cases treated by neurosurgeons at one hospital between March 2004 and March 2013. Data included clinical event, timing and reason for claim, operative course and legal outcome. RESULTS Twenty-nine claims were defended out of court and twelve were settled out of court. One case required court attendance and was defended. Of the 42 claims, 28, 13 and 1 related to spinal (0.3% of caseload), cranial (0.1% of caseload) and peripheral nerve (0.07% of caseload) surgery respectively. The most common causes of claims were faulty surgical technique (43%), delayed diagnosis/misdiagnosis (17%), lack of information (14%) and delayed treatment (12%), with a likelihood of success of 39%, 29%, 17% and 20% respectively. The highest median payouts were for claims against faulty surgical technique (£230,000) and delayed diagnosis/misdiagnosis (£212,650). The mean delay between clinical event and claim was 664 days. CONCLUSIONS Spinal surgery carries the highest litigation risk versus cranial and peripheral nerve surgery. Claims are most commonly against faulty surgical technique and delayed diagnosis/misdiagnosis, which have the highest success rates and payouts. In spinal surgery, the most common cause of claims is faulty surgical technique. In cranial surgery, the most common cause is lack of information. Claims may occur years after the clinical event, necessitating thorough contemporaneous documentation for adequate future defence. We emphasise thorough patient consultation and meticulous surgical technique to minimise litigation in neurosurgical practice.

We aim to describe the outcomes after chronic subdural hematoma drainage (CSDH) management in a large cohort of patients on antithrombotic drugs, either antiplatelets or anticoagulants, at presentation and to inform clinical decision making on the timing of surgery and recommencement of these drugs. We used data from a previous UK-based multi-center, prospective cohort study. Outcomes included recurrence within 60 days, functional outcome at discharge, and thromboembolic event during hospital stay. We performed Cox regression on recurrence and multiple logistic regression on functional outcome. There were 817 patients included in the analysis, of which 353 (43.2%) were on an antithrombotic drug at presentation. We observed a gradual reduction in risk of recurrence for patients during the 6 weeks post-CSDH surgery. Neither antiplatelet nor anticoagulant drug use influenced risk of CSDH recurrence (hazard ratio, 0.93; 95% confidence interval [CI], 0.58-1.48; p = 0.76) or persistent/worse functional impairment (odds ratio, 1.08; 95% CI, 0.76-1.55; p = 0.66). Delaying surgery after cessation of antiplatelet drug did not affect risk of bleed recurrence. There were 15 in-hospital thromboembolic events recorded. Events were more common in the group pre-treated with antithrombotic drugs (3.3%) compared to the non-antithrombotic group (0.9%). Patients on an antithrombotic drug pre-operatively were at higher risk of thromboembolic events with no excess risk of bleed recurrence or worse functional outcome after CSDH drainage. The data did not support delaying surgery in patients on antithrombotic therapy. In the absence of a randomized controlled trial, early surgery and early antithrombotic recommencement should be considered in those at high risk of thromboembolic events.

There is much discussion concerning ‘digital transformation’ in healthcare and the potential of artificial intelligence (AI) in healthcare systems. Yet it remains rare to find AI solutions deployed in routine healthcare settings. This is in part due to the numerous challenges inherent in delivering an AI project in a clinical environment. In this article, several UK healthcare professionals and academics reflect on the challenges they have faced in building AI solutions using routinely collected healthcare data.These personal reflections are summarised as 10 practical tips. In our experience, these are essential considerations for an AI healthcare project to succeed. They are organised into four phases: conceptualisation, data management, AI application and clinical deployment. There is a focus on conceptualisation, reflecting our view that initial set-up is vital to success. We hope that our personal experiences will provide useful insights to others looking to improve patient care through optimal data use.