The a priori STEEM subscales could not be replicated by factor analysis. We developed an empirically grounded instrument capable of identifying areas of trainee concern. The majority of trainees reported high levels of satisfaction. The revised instrument has potential to complement other sources of information to facilitate surgical supervisors' difficult task of optimizing trainees' compatibility with their OT learning environments.

Accurately measuring the incidence of major postoperative complications is essential for funding and reimbursement of healthcare providers, for internal and external benchmarking of hospital performance and for valid and reliable public reporting of outcomes. Actual or surrogate outcomes data are typically obtained by one of three methods: clinical quality registries, clinical audit, or administrative data. In 2017 a perioperative registry was developed at the Alfred Hospital and mapped to administrative and clinical data. This study investigated the statistical agreement between administrative data (International Statistical Classification of Diseases and Related Health Problems (10th edition) Australian Modification codes) and clinical audit by anaesthetists in identifying major postoperative complications. The study population included 482 high-risk surgical patients referred to the Alfred Hospital anaesthesia postoperative service over two years. Clinical audit was conducted to determine the presence of major complications and these data were compared to administrative data. The main outcome was statistical agreement between the two methods, as defined by Cohen’s kappa statistic. Substantial agreement was observed for five major complications, moderate agreement for three, fair agreement for six and poor agreement for two. Sensitivity and positive predictive value ranged from 0 to 100%. Specificity was above 90% for all complications. There was important variation in inter-rater agreement. For four of the five complications with substantial agreement between administrative data and clinical audit, sensitivity was only moderate (61.5%–75%). Using International Statistical Classification of Diseases and Related Health Problems (10th edition) Australian Modification codes to identify postoperative complications at our hospital has high specificity but is likely to underestimate the incidence compared to clinical audit. Further, retrospective clinical audit itself is not a highly reliable method of identifying complications. We believe a perioperative clinical quality registry is necessary to validly and reliably measure major postoperative complications in Australia for benchmarking of hospital performance and before public reporting of outcomes should be considered.

Objective: Electronic scooter (eScooter) popularity has soared, despite public concerns around injury risk. We aimed to explore the burden of injury from eScooters presenting to Tasmania's major trauma centre during the first 6 months of the Hobart eScooter trial. Methods: We screened all ED presentations and interhospital transfers from 11 December 2021 to 26 June 2022 to identify eScooter-associated injury. All injuries were coded using the Abbreviated Injury Scale and an Injury Severity Score (ISS) was computed for each presentation. Data were entered into the Tasmanian Trauma Registry and then exported into standard statistical software for descriptive analysis. Results: There were 135 eScooter presentations to the ED and 31 were admitted. Patients were mostly young (median age 27 years) with an even sex distribution. Injuries were mostly minor (median ISS of 2) with injuries to soft tissues and the head and neck predominating. Approximately, onethird required operative interventions, but none required intensive care. Injured patients were less likely to have been wearing protective gear (36% helmet wearing) and alcohol intoxication was common (41% overall, 71% admitted patients). Conclusion:Overall, our 6-month prospective dataset shows that the Hobart eScooter trial has been associated with few major injuries.

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Intermediate and high-risk patients undergoing surgery are often managed on a surgical ward in the absence of haemodynamic or ventilatory support requirements necessitating intensive care unit or high dependency unit admission. We describe a model of care for the multidisciplinary management of selected postoperative patients and the epidemiology of patients managed using this model at a tertiary Australian hospital. Of 25,139 patients undergoing inpatient surgery at our institution over a two-year period, 506 (2%) were referred to the Perioperative Medicine team. The median age of patients referred was 74 years; 85% had an American Society of Anesthesiologists physical status ≥3, and 44% underwent emergency surgery. Major complication or death within 30 days was 44.2% (213/482). The most common complications, as defined by the American College of Surgeons National Surgical Quality Improvement Program were transfusion within 72 h (17.4%), pneumonia/aspiration pneumonitis (11.3%), and acute renal failure (10.6%); median time to Medical Emergency Team call was 146 (interquartile range 77–279) h. Sixty-six percent of referred patients (280/423) required an intervention during their time under the service. This high incidence indicates that this population of patients merits closer attention, including routine measurement and reporting of postoperative outcomes to monitor and improve quality of care at our institution as part of an integrated perioperative service. We believe that with so much current focus on perioperative medicine, it is important we translate this to clinical care by evolving traditional models of management into more innovative strategies to meet the complex demands of today’s surgical patients.