Analyzing electronic medical records to extract prepregnancy morbidities and pregnancy complications: Toward a learning health system

Abstract Introduction Preexisting and pregnancy‐related medical conditions frequently co‐occur, leading to multimorbidity (≥2 morbidities) in pregnant women, and much of this information is in semi‐structured format in electronic medical records (EMRs). The aim was to advance the learning health sys...

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Main Authors: Yitayeh Belsti, Lisa Moran, Aya Mousa, Rebecca Goldstein, Daniel Lorber Rolnik, Mahnaz Bahri Khomami, Mihiretu M. Kebede, Helena Teede, Joanne Enticott
Format: Article
Language:English
Published: Wiley 2025-04-01
Series:Learning Health Systems
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Online Access:https://doi.org/10.1002/lrh2.10473
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Summary:Abstract Introduction Preexisting and pregnancy‐related medical conditions frequently co‐occur, leading to multimorbidity (≥2 morbidities) in pregnant women, and much of this information is in semi‐structured format in electronic medical records (EMRs). The aim was to advance the learning health system as a platform for automating information extraction from EMRs and to uncover the prevalence of common morbidities during pregnancy and their association with pregnancy‐related complications. Methods This study included 48 502 pregnant women attending Monash Health maternity hospitals from 2016 to 2021. Natural language processing (NLP) was used to extract morbidities from semi‐structured text in EMRs. Chi‐squared tests were used to assess the association between morbidities of gestational diabetes mellitus (GDM) and other pregnancy complications. The k‐means clustering algorithm identified clusters of comorbid conditions associated with GDM. Results The most common comorbidities during pregnancy were vitamin deficiency (14 019; 28.9%), overweight (13 918; 28.7%), obesity (11 026; 22.7%), anemia and other blood‐related disorders (4821; 9.9%), mental health disorders (4314; 9.8%), asthma (4126; 8.5%), thyroid diseases (3576; 7.4%), endometrial disease (1927; 3.9%), cardiovascular disease (1525; 3.1%), and polycystic ovary syndrome (PCOS) (1464; 3.0%). While 22.5% of women had no medical conditions, 77.5% had one or more. Multimorbidity was associated with conditions including overweight, obesity, vitamin deficiency, thyroid disease, substance use, PCOS, GDM, and endometrial diseases. On cluster analysis, aged 35 years or older, overweight, vitamin deficiency, obesity, thyroid disease, asthma, uterine disease, other blood disorders, mental disorders, and PCOS were associated with GDM. Conclusions More than three‐quarters of pregnant women in the Australian urban setting experienced one or more morbidities during pregnancy, which can be associated with adverse pregnancy outcomes. This project contributes to developing a learning health system infrastructure to deliver high‐value maternal health care while reducing costs.
ISSN:2379-6146