Construction of quality-assured infant feeding process of care data repositories: Construction of the perinatal repository (Part 2)

Digital Health Data Quality Issues: Systematic Review

“…Furthermore, inconsistent data entry can be caused by redundancy within the system because of structured versus unstructured data [ 108 ], which we label as the subtheme “ structuredness ,” and duplication across systems [ 39 , 48 , 104 , 109 , 172 , 173 ], which we label as the subtheme “ uniqueness .” Although structured data entry “facilitates information retrieval” [ 33 ] and is “in a format that enables reliable extraction” [ 18 ], the presence of unstructured fields leads to data duplication efforts, hampering uniqueness, as data are recorded in multiple places with varying degrees of granularity and level of detail.…”

Robust estimation of infant feeding indicators by data quality assessment of longitudinal electronic health records from birth up to 18 months of life

“…To control the possible selection biases by excluding subjects with non-tolerable problems (lost cases), in addition to the feeding data, the repository incorporates sufficient perinatal information [35] to compare the distribution of the population of valid subjects and lost subjects according to the factors related to the abandonment of breastfeeding described in the literature [43,44] (maternal age, early skin-to-skin contact, assessment of breastfeeding initiation, type of delivery, gestational age, …).…”

“…The DQ assessment of single contacts was performed in two steps. We first performed a preprocess using DQ methods and metrics from previous works [34,35] to assure the structural integrity of the data and solved de-duplication of data, obtaining a canonical structure where the DQ dimensions of contextualization, temporal stability, spatial stability and duplicity were assessed.…”

“…In previous works, the authors proposed a DQ assessment and assurance process for EHR data [33,34], including an application to perinatal data [35] based in 8 DQ dimensions: predictive value, correctness, duplication, consistency, completeness, contextualization, temporal-stability, and spatial-stability. Later, they designed a pilot project in Spain [36] that established the basis for reliable monitoring of infant feeding indicators, focusing on data standardization and data quality analysis, mainly of perinatal variables, without addressing the analysis of the longitudinal feeding register.…”

Diseño, construcción y evaluación de repositorios estandarizados con calidad de datos asegurada para la monitorización de la atención a la alimentación infantil

“…Para su implementación, se definió el proceso de atención a la alimentación al lactante (PAAL) (García-De-León-González et al, 2008), garantizando la continuidad asistencial entre el hospital y atención primaria. Con objeto de poder evaluar el PAAL, tanto a nivel de proceso como de resultados, era necesario ampliar el repositorio perinatal inicial creado en el hospital (García-de- León-Chocano et al, 2016), integrando en dicho repositorio la información de alimentación infantil hasta los dos años proveniente de las revisiones de salud infantil de atención primaria realizadas según el Programa de Atención al Niño y Adolescente de la Región de Murcia (PANA). El objetivo entonces era aplicar el proceso de construcción de repositorios con calidad asegurada (García-de-León-Chocano et al, 2015), utilizado previamente para construir el repositorio perinatal (García-de-León-Chocano et al, 2016), a la información del seguimiento de la alimentación hasta los 2 años, con motivo de obtener un IDR que recoja e integre la información necesaria de los dos niveles asistenciales implicados en el PAAL; con el fin último de constituir el IDR como fuente para la monitorización del PAAL.…”