Poor quality of care is a leading cause of excess morbidity and mortality in low-and middleincome countries (LMICs). Improving the quality of healthcare is complex, and requires an interdisciplinary team equipped with the skills to design, implement and analyse settingrelevant improvement interventions. Such capacity is limited in many LMICs. However, training for healthcare workers in quality improvement (QI) methodology without buy-in from multidisciplinary stakeholders and without identifying setting-specific priorities is unlikely to be successful. The Care Quality Improvement Network (CQIN) was established between Network for Improving Critical care Systems and Training (NICST) and University College London Centre for Perioperative Medicine, with the aim of building capacity for research and QI. A two-day international workshop, in collaboration with the College of Surgeons of Sri Lanka, was conducted to address the above deficits. Innovatively, the CQIN adopts a learning health systems (LHS) approach to improving care by leveraging information captured through the NICST electronic multi-centre acute and critical care surveillance platform. Fifty-two delegates from across the CQIN representing clinical, civic and academic healthcare stakeholders from six countries attended the workshop. Mapping of care processes enabled identification of barriers and drivers to the delivery of care and facilitated the selection of feasible QI methods and matrices. Six projects, reflecting key priorities for improving the delivery of acute care in Asia, were collaboratively developed: improving assessment of postoperative pain; optimising sedation in critical care; refining referral of deteriorating patients; reducing surgical site infection after caesarean section; reducing surgical site infection after elective general surgery; and improving provision of timely electrocardiogram recording for patients presenting with signs of acute myocardial infarction. Future project implementation and evaluation will be supported with resources and expertise from the CQIN partners. This LHS approach to building capacity for QI may be of interest to others seeing to improve care in LMICs.

Purpose of ReviewThis article offers an overview of the history and features of Improvement Science in general and some of its applications to Anaesthesia in particular.Recent FindingsImprovement Science is an evolving discipline aiming to generate learning from quality improvement interventions. An increasingly common approach to improving Anaesthesia services is to employ large-scale perioperative data measurement and feedback programmes. Improvement Science offers important insights on questions such as which indicators to collect data for; how to capture that data; how it can be presented in engaging visual formats; how it could/should be fed back to frontline staff and how they can be supported in their use of data to generate improvement.SummaryData measurement and feedback systems represent opportunities for anaesthetists to work with multidisciplinary colleagues to help improve services and outcomes for surgical patients. Improvement Science can help evaluate which approaches work, and in which contexts, and is therefore of value to healthcare commissioners, providers and patients.

Acute postoperative pain is common, distressing and associated with increased morbidity. Targeted interventions can prevent its development. We aimed to develop and internally validate a predictive tool to preemptively identify patients at risk of severe pain following major surgery. We analysed data from the UK Perioperative Quality Improvement Programme to develop and validate a logistic regression model to predict severe pain on the first postoperative day using pre-operative variables. Secondary analyses included the use of peri-operative variables. Data from 17,079 patients undergoing major surgery were included. Severe pain was reported by 3140 (18.4%) patients; this was more prevalent in females, patients with cancer or insulindependent diabetes, current smokers and in those taking baseline opioids. Our final model included 25 preoperative predictors with an optimism-corrected c-statistic of 0.66 and good calibration (mean absolute error 0.005, p = 0.35). Decision-curve analysis suggested an optimal cut-off value of 20-30% predicted risk to identify high-risk individuals. Potentially modifiable risk factors included smoking status and patient-reported measures of psychological well-being. Non-modifiable factors included demographic and surgical factors. Discrimination was improved by the addition of intra-operative variables (likelihood ratio v 2 496.5, p < 0.001) but not by the addition of baseline opioid data. On internal validation, our pre-operative prediction model was well calibrated but discrimination was moderate. Performance was improved with the inclusion of peri-operative covariates suggesting pre-operative variables alone are not sufficient to adequately predict postoperative pain.