Dataset Information

Factors contributing to uncertainty in paediatric abdominal ultrasound reports in the paediatric emergency department.

ABSTRACT:

Background

Abdominal pain, which is a common cause of children presenting to the paediatric emergency department (PED), is often evaluated by ultrasonography (US). However, uncertainty in US reports may necessitate additional imaging.

Objective

In this study, we evaluated factors contributing to uncertainty in paediatric abdominal US reports in the PED.

Materials and methods

This retrospective cohort study included children younger than 18 years of age who underwent abdominal US in the PED of the study hospital between January 2017 and December 2019. After exclusion, the researchers manually reviewed and classified all US reports as 'certain' or 'uncertain'. Univariate and multivariate logistic regression analyses were performed to identify the factors contributing to uncertain reports.

Results

In total, 1006 patients were included in the final analysis., 796 patients were tagged as having certain reports, and 210 as having uncertain reports. Children with uncertain reports had a significantly higher rate of undergoing an additional computed tomography (CT) scan (31.0% vs. 2.5%, p < 0.001) and a longer PED median length of stay (321.0 (Interquartile range (IQR); 211.3-441.5) minutes vs. 284.5 (IQR; 191.8-439.5) minutes, p = 0.042). After logistic regression, US performed by a radiology resident (odds ratio, 5.01; 95% confidence interval, 3.63-7.15) was the most significant factor contributing to uncertainty in paediatric abdominal US reports followed by obesity and age.

Conclusion

Several factors contribute to uncertainty in paediatric abdominal US reports. Uncertain radiological reports increase the likelihood of additional CT scans. Measures to improve the clarity of radiological reports must be considered to improve the quality of care for children visiting the PED.

SUBMITTER: Hwang S

PROVIDER: S-EPMC10566031 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

Factors contributing to uncertainty in paediatric abdominal ultrasound reports in the paediatric emergency department.

Hwang Soyun S Chung Hyun Jung HJ Park Joong Wan JW Lee Eui Jun EJ Lee Ha Ni HN Kim Jin Hee JH Jue Jie Hee JH Choi Young Hun YH Jung Jae Yun JY

BMC emergency medicine 20231010 1

<h4>Background</h4>Abdominal pain, which is a common cause of children presenting to the paediatric emergency department (PED), is often evaluated by ultrasonography (US). However, uncertainty in US reports may necessitate additional imaging.<h4>Objective</h4>In this study, we evaluated factors contributing to uncertainty in paediatric abdominal US reports in the PED.<h4>Materials and methods</h4>This retrospective cohort study included children younger than 18 years of age who underwent abdomin ...[more]

PMID: 37817121

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Project description:BackgroundReliably abstracting outcomes from free-text electronic health records remains a challenge. While automated classification of free text has been a popular medical informatics topic, performance validation using real-world clinical data has been limited. The two main approaches are linguistic (natural language processing [NLP]) and statistical (machine learning). The authors have developed a hybrid system for abstracting computed tomography (CT) reports for specified outcomes.ObjectivesThe objective was to measure performance of a hybrid NLP and machine learning system for automated outcome classification of emergency department (ED) CT imaging reports. The hypothesis was that such a system is comparable to medical personnel doing the data abstraction.MethodsA secondary analysis was performed on a prior diagnostic imaging study on 3,710 blunt facial trauma victims. Staff radiologists dictated CT reports as free text, which were then deidentified. A trained data abstractor manually coded the reference standard outcome of acute orbital fracture, with a random subset double-coded for reliability. The data set was randomly split evenly into training and testing sets. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for certainty and temporal status. Findings were filtered for low certainty and past/future modifiers and then combined with the manual reference standard to generate decision tree classifiers using data mining tools Waikato Environment for Knowledge Analysis (WEKA) 3.7.5 and Salford Predictive Miner 6.6. Performance of decision tree classifiers was evaluated on the testing set with or without NLP processing.ResultsThe performance of machine learning alone was comparable to prior NLP studies (sensitivity = 0.92, specificity = 0.93, precision = 0.95, recall = 0.93, f-score = 0.94), and the combined use of NLP and machine learning showed further improvement (sensitivity = 0.93, specificity = 0.97, precision = 0.97, recall = 0.96, f-score = 0.97). This performance is similar to, or better than, that of medical personnel in previous studies.ConclusionsA hybrid NLP and machine learning automated classification system shows promise in coding free-text electronic clinical data.

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Dataset Information

Factors contributing to uncertainty in paediatric abdominal ultrasound reports in the paediatric emergency department.

Background

Objective

Materials and methods

Results

Conclusion

Publications

Factors contributing to uncertainty in paediatric abdominal ultrasound reports in the paediatric emergency department.

Similar Datasets

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