The Use of a Standardized Checklist for Image Acquisition: An Obstetric Ultrasound Quality Improvement Study

ABSTRACT Introduction A complete anatomical survey during the mid‐trimester ultrasound is critical for detecting fetal anomalies. In sub‐Saharan Africa, detection rates remain low, contributing to a significant disease burden. This study evaluated whether implementing a standardized electronic checklist, based on existing guidelines, would improve image acquisition. Methods We carried out a pre‐ and post‐intervention quasi‐experimental quality improvement study. An electronic checklist replaced the previous ultrasound report template. A baseline review involved 385 mid‐trimester scans conducted between November 2022 and December 2023. Following checklist implementation and targeted sonographer training, 238 scans were reassessed from July to December 2024. The image acquisition rate was determined as the ratio of required views obtained per scan. Additionally, acquisition rates for each anatomical view were evaluated across all scans. Results The mean image acquisition rate increased significantly from 60.0% (SD = 10.8%) to 88.1% (SD = 6.5%) post‐intervention (mean difference 28.1 percentage points, 95% CI 26.7–29.5; p < 0.001; Cohen's d = 2.99). Views with the lowest baseline acquisition rates included the cardiac outflow tracts, lateral ventricles, cavum septi pellucidi (CSP), facial structures, bowel, and kidneys. Post‐intervention, acquisition of these views improved significantly ( p < 0.001). Agreement between sonographer and reviewer checklists was high overall, but lower for CSP, bowel, and situs views. Conclusion A standardized electronic checklist significantly improved the acquisition of essential fetal anatomical views during mid‐trimester ultrasound. This low‐cost intervention can enhance prenatal diagnosis in resource‐limited settings, though ongoing training is necessary for sustained impact. Further research should evaluate whether these improvements translate into better diagnostic accuracy and maternal‐fetal outcomes and assess scalability in resource‐limited settings.