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Machine Learning Analysis to Identify Data Entry Errors in Prehospital Patient Care Reports: A Case Study of a National Out-of-Hospital Cardiac Arrest Registry.
- Source :
- Prehospital Emergency Care; 2024, Vol. 28 Issue 1, p14-22, 9p
- Publication Year :
- 2024
-
Abstract
- Background: The objective of this study was to develop and validate machine learning models for data entry error detection in a national out-of-hospital cardiac arrest (OHCA) prehospital patient care report database. Methods: Adult OHCAs of presumed cardiac etiology were included. Data entry errors were defined as discrepancies between the coded data and the free-text note documenting the intervention or event; for example, information that was recorded as "absent" in the coded data but "present" in the free-text note. Machine learning models using the extreme gradient boosting, logistic regression, extreme gradient boosting outlier detection, and K-nearest neighbor outlier detection algorithms for error detection within nine core variables were developed and then validated for each variable. Results: Among 12,100 OHCAs, the proportion of cases with at least one error type was 16.2%. The area under the receiver operating characteristic curve (AUC) of the best-performing model (model with the highest AUC for each outcome variable) was 0.71–0.95. Machine learning models detected errors most efficiently for outcome place and initial rhythm errors; 82.6% of place errors and 93.8% of initial rhythm errors could be detected while checking 11 and 35% of data, respectively, compared to the strategy of checking all data. Conclusion: Machine learning models can detect data entry errors in care reports of emergency medical services (EMS) clinicians with acceptable performance and likely can improve the efficiency of the process of data quality control. EMS organizations that provide more prehospital interventions for OHCA patients could have higher error rates and may benefit from the adoption of error-detection models. [ABSTRACT FROM AUTHOR]
- Subjects :
- PREVENTION of medical errors
DATA quality
STATISTICS
CONFIDENCE intervals
CROSS-sectional method
MACHINE learning
MANN Whitney U Test
DOCUMENTATION
HUMAN services programs
INTER-observer reliability
CARDIAC arrest
HUMAN error
QUALITY assurance
EMERGENCY medical services
CHI-squared test
DESCRIPTIVE statistics
RESEARCH funding
PREDICTION models
LOGISTIC regression analysis
RECEIVER operating characteristic curves
STATISTICAL sampling
EMERGENCY medicine
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 10903127
- Volume :
- 28
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Prehospital Emergency Care
- Publication Type :
- Academic Journal
- Accession number :
- 174558585
- Full Text :
- https://doi.org/10.1080/10903127.2022.2137745