Development and validation of software tool for using original Robson ten group classification system for analysing caesarean section rates: a step towards minimizing errors from a large volume centre in a resource country

Authors

  • Gayathri Sreekumar Department of Obstetrics and Gynaecology, JIPMER, Puducherry, India
  • Sasirekha Rengaraj Department of Obstetrics and Gynaecology, JIPMER, Puducherry, India https://orcid.org/0000-0002-6440-2039
  • Uma Vijayasundaram Department of Computer Science, Pondicherry University, Puducherry, India
  • Aman Kumar Department of Computer Science, Pondicherry University, Puducherry, India
  • Saiful Islam Tapadar Department of Computer Science, Pondicherry University, Puducherry, India
  • Prasad Indrajit Satyendra Department of Computer Science, Pondicherry University, Puducherry, India
  • Srujan Sarathi Samal Department of Computer Science, Pondicherry University, Puducherry, India

DOI:

https://doi.org/10.18203/2320-1770.ijrcog20241437

Keywords:

RTGCS, Robson classification, CS, Caesarean audit, Application, App

Abstract

Background: To develop and validate a software tool for classifying caesarean sections (CS) using original Robson ten group (RTG) system of classification and to compare it with manual entry period using excel sheet.

Methods: The study was conducted in a tertiary care centre to include the data of all women delivered between July 1st 2023 and September 30th 2023. Two months retrospective data was collected for developing the App and it was validated by comparing its accuracy with that of three health care workers. The App was developed to enable classification using a logic-driven if-else condition structure. It was tested in real time for a month period.

Result: A total of 227 deliveries were collected for development of App and it was found to have maximum agreement with that of the gold standard with Kappa score 1, whereas the maximum accuracy of the health workers was 0.933 Kappa score with respect to gold standard. The App was tested in real time and compared with the data entered manually. The misclassification and missing data were nil which used to be 15-20% in groups 2, 10 and 9 when the data were entered manually.

Conclusions: This App was found to be helpful in saving time and minimize human errors in large institutions. It provided accurate data on RTG classification and it ensured all the details for classifying CS are entered. It helps to audit CS systematically.

References

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Published

2024-05-29

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Section

Original Research Articles