Implementing the Robson Classification for caesarean sections in Pakistan: experience, challenges, and lessons...

Area of Science:

• Maternal and Newborn Health
• Healthcare System Strengthening
• Obstetrics and Gynecology

Background:

• The Robson Classification System is crucial for optimizing caesarean section rates and enhancing maternal and newborn health quality improvement.
• Institutionalizing evidence-based practices like the Robson Classification is vital for healthcare system advancement.

Purpose of the Study:

• To detail the strategy and lessons learned from integrating the Robson Classification System into Pakistan's national health system.
• To establish a sustainable framework for optimizing caesarean section practices and improving maternal care outcomes.

Main Methods:

• Developed a comprehensive training package focused on building capacity among senior clinicians to become master trainers.
• Created a mobile application to facilitate efficient data collection and analysis for the Robson Classification.
• Conducted training workshops in 2020 across selected public tertiary teaching hospitals, followed by a year of comprehensive birth data collection.

Main Results:

• The Robson Classification System has been successfully embedded in 57% of Pakistan's public, tertiary, teaching hospitals.
• A network of master trainers is established in every province, supported by a robust dataset.
• The initiative demonstrates readiness for national scale-up, with established infrastructure and trained personnel.

Conclusions:

• The integration of the Robson Classification System in Pakistan is a significant step towards optimizing caesarean section rates and improving maternal health.
• Sustained government commitment, continuous training, and robust data quality assurance are essential for long-term success.
• The initiative provides a scalable model for other low- and middle-income countries seeking to improve obstetric care quality.