The Robson classification for caesarean section-A proposed method based on routinely collected health data

Area of Science:

• Obstetrics and Gynecology
• Health Informatics
• Public Health

Background:

• Increasing rates of caesarean sections and multiple pregnancies necessitate robust benchmarking tools.
• The Robson classification system aids in grouping caesarean section cases for outcome assessment.
• A novel method using routinely collected health data for classification was developed.

Purpose of the Study:

• To determine if routinely collected health data can be used for complex medical classifications.
• To validate the Robson classification's capability in grouping mothers and newborns for outcome analysis.

Main Methods:

• Utilized a structured query language (SQL) code on administrative, electronic health record, and laboratory data.
• Implemented and validated an automated query for Robson classification.
• Linked maternal Robson class to corresponding newborn data.

Main Results:

• Successfully implemented and validated an automated Robson classification system.
• Demonstrated that Robson classes are associated with outcome parameters and costs.
• Established a linkage between maternal and newborn data within the classification.

Conclusions:

• Complex queries on routinely collected health data are effective for medical classification and quality monitoring.
• Automated classification using health data can support risk stratification and quality assurance.
• This approach enhances the discussion for adopting automated health data classification for quality assurance.