Authors:
Giovana Jaskulski Gelatti
1
;
2
;
Pedro Pereira Rodrigues
1
;
3
and
Ricardo João Cruz Correia
1
;
3
Affiliations:
1
Centre for Health Technology and Services Research, 4200-450, Porto, Portugal
;
2
Institute of Mathematical and Computer Sciences of University of São Paulo, 13566-590, São Carlos, Brasil
;
3
Universidade do Porto, Faculty of Medicine of University of Porto, 4200-319, Porto, Portugal
Keyword(s):
Health Policies, Medical Information, Health Data Science, Computer Support, Robson Classification, Obstetrics.
Abstract:
Introduction: In 2015 the Directorate-General for Health of Portugal published new standards (DGS 001/2015) for the registration of cesarean section indicators. The existing scenario was the lack of data, influencing the quality of indicators and analyses on them. The use of a single computer tool was encouraged to register and compare indicators between hospitals with special attention to the Robson Classification as it employs basic information of pregnancy to classify all deliveries in 10 groups. The selected tool was Obscare software.
Aim: Describe the scenario on data quality by analyzing the completeness of obstetric records from 2016 to 2018 of the variables used in Robson’s classification collected by the Obscare tool.
Methods: The completeness is evaluated using a number of missing values. The lower the completeness, the higher the number of missing values. Also, we perform the imputation of data based on basic concepts and analyzed the participation of this data in the in
dication of the type of delivery to be performed according to classification suggested by DGS 001/2015.
Results: From 2016 to 2018, 5922 number of pregnancies resulted in 5922 of Robson Classifications. The variables with lower completeness were related to previous cesarean section (77%) and previous pregnancies (43%). After imputation, it fell to 3.9% and 0.56%, respectively causing 4.6% of discarded data from the total. Discussion: There is a significant amount of missing data in basic variables used to study the classification of delivery type. We believe that encouraging data completion with the possibility of comparing data between hospitals should be a priority in the health area.
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