Is developing a clinical decision support system based on the Robson's classification sufficient to reduce cesarean rates?

Abstract

The aim of this study is to analyze the trend of cesarean section rates in a public hospital maternity after the implementation of a Clinical Decision Support System (SADC) for the Robson Classification. The implementation developed a model based on a decision tree that proposes the classification of pregnant women into ten groups based on six basic variables. After implantation, the cesarean section rates was observed between 2016 and 2017. In this period, there were 4,155 deliveries (37.6% cesarean sections). The evolution of cesarean section rates followed a growth trend, as well as the proportion of cesareans section between groups classified as 1 to 4, groups of pregnant women where cesarean sections are very preventable. In the two years it was observed that the SADC had no impact on the reduction of cesarean rates. It was concluded that in health services, work processes need to be articulated with timely decision-making strategies, so that the execution of actions can promote the qualification of services and improve the quality of patient care.