Implementation of the WHO manual for Robson classification: an example from Sri Lanka using a local database for developing quality improvement recommendations.

Objectives This study aimed at describing the use of a prospective database on hospital deliveries for analysing caesarean section (CS) practices according to the WHO manual for Robson classification, and for developing recommendations for improving the quality of care (QoC). Design Observational study. Setting University Obstetric Unit at De Soysa Hospital for Women, the largest maternity unit in Sri Lanka. Data collection and analysis For each childbirth, 150 variables were routinely collected in a standardised form and entered into a database. Data were routinely monitored for ensuring quality. Information on deliveries occurring from July 2015 to June 2017 were analysed according the WHO Robson classification manual. Findings were discussed internally to develop quality improvement recommendations. Results 7504 women delivered in the hospital during the study period and at least one maternal or fetal pathological condition was reported in 2845 (37.9%). The CS rate was 30.0%, with 11.9% CS being performed prelabour. According to the Robson classification, Group 3 and Group 1 were the most represented groups (27.0% and 23.1% of population, respectively). The major contributors to the CS rate were group 5 (29.6%), group 1 (14.0%), group 2a (13.3%) and group 10 (11.5%). The most commonly reported indications for CS included abnormal cardiotocography/suspected fetal distress, past CS and failed progress of labour or failed induction. These suggested the need for further discussion on CS practices. Overall, 18 recommendations were agreed on. Besides updating protocols and hands-on training, activities agreed on included monitoring and supervision, criterion-based audits, risk management meetings and appropriate information for patients, and recommendations to further improve the quality of data. Conclusions This study provides an example on how the WHO manual for Robson classification can be used in an action-oriented manner for developing recommendations for improving the QoC, and the quality of data collected.