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

With an increasing rate of caesarean sections as well as rising numbers of multiple pregnancies, valid classifications for benchmarking are needed. The Robson classification provides a method to group cases with caesarean section in order to assess differences in outcome across regions and sites. In...

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Bibliographic Details
Published in:	PloS one 2020-11, Vol.15 (11), p.e0242736-e0242736
Main Authors:	Triep, Karen, Torbica, Nenad, Raio, Luigi, Surbek, Daniel, Endrich, Olga
Format:	Article
Language:	eng
Subjects:	Automation Biology and Life Sciences Cases (containers) Cesarean Section Classification Codes Computer and Information Sciences Data warehouses Electronic Data Processing Electronic Health Records Electronic medical records Female Gynecology Hospitals Humans Laboratories Maternal health services Medicine and Health Sciences Multiple births Newborn babies Obstetrics People and Places Population studies Pregnancy Quality assurance Quality management Queries Query languages Research and Analysis Methods Routinely Collected Health Data Structured Query Language-SQL
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Summary:	With an increasing rate of caesarean sections as well as rising numbers of multiple pregnancies, valid classifications for benchmarking are needed. The Robson classification provides a method to group cases with caesarean section in order to assess differences in outcome across regions and sites. In this study we set up a novel method of classification by using routinely collected health data. We hypothesize i that routinely collected health data can be used to apply complex medical classifications and ii that the Robson classification is capable of classifying mothers and their corresponding newborn into meaningful groups with regard to outcome. The study was conducted at the coding department and the department of obstetrics and gynecology Inselspital, University Hospital of Bern, Switzerland. The study population contained inpatient cases from 2014 until 2017. Administrative and health data were extracted from the Data Warehouse. Cases were classified by a Structured Query Language code according to the Robson criteria using data from the administrative system, the electronic health record and from the laboratory system. An automated query to classify the cases according to Robson could be implemented and successfully validated. A linkage of the mother's class to the corresponding newborn could be established. The distribution of clinical indicators was described. It could be shown that the Robson classes are associated to outcome parameters and case related costs. With this study it could be demonstrated, that a complex query on routinely collected health data would serve for medical classification and monitoring of quality and outcome. Risk-stratification might be conducted using this data set and should be the next step in order to evaluate the Robson criteria and outcome. This study will enhance the discussion to adopt an automated classification on routinely collected health data for quality assurance purposes.
ISSN:	1932-6203 1932-6203