The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Transcription Error Rates in Retrospective Chart Reviews
Abstract
Electronic health record (EHR) technologies have improved the ease of access to structured clinical data. The standard means by which data are collected continues to be manual chart review. The authors compared the accuracy of manual chart review against modern electronic data warehouse queries. A manual chart review of the EHR was performed with medical record numbers and surgical admission dates for the 100 most recent inpatient venous thromboembolic events after total joint arthroplasty. A separate data query was performed with the authors' electronic data warehouse. Data sets were then algorithmically compared to check for matches. Discrepancies between data sets were evaluated to categorize errors as random vs systematic. From 100 unique patient encounters, 27 variables were retrieved. The average transcription error rate was 9.19% (SD, ±5.74%) per patient encounter and 11.04% (SD, ±21.40%) per data variable. The systematic error rate was 7.41% (2 of 27). When systematic errors were excluded, the random error rate was 5.79% (SD, ±7.04%) per patient encounter and 5.44% (SD, ±5.63%) per data variable. Total time and average time for manual data collection per patient were 915 minutes and 10.3±3.89 minutes, respectively. Data collection time for the entire electronic query was 58 seconds. With an error rate of 10%, manual chart review studies may be more prone to type I and II errors. Computer-based data queries can improve the speed, reliability, reproducibility, and scalability of data retrieval and allow hospitals to make more data-driven decisions. [ Orthopedics . 2020;43(5):e404–e408.]
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“While this was significantly better than the conventional HTR results, we should note that it is still about 60% less accurate than the upper error rates reported for non-expert human transcribers (Feng et al 2020;Nordo et al 2017;Oliveira 2018;Stolcke 2017).”
From Unlocking the archives: Using large language models to transcribe handwritten historical documents (2025) · cites this paper as Feng et al 2020
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Cited by
Unlocking the archives: Using large language models to transcribe handwritten historical documents
Humphries, M., Leddy, L. C., Downton, Q., Legace, M., McConnell, J., Murray, I., & Spence, E. (2025) · Historical Methods: A Journal of Quantitative and Interdisciplinary History · doi:10.1080/01615440.2025.2500309 · Cited by 72
Metadata
- Authors
- Feng, J. E., Anoushiravani, A. A., Tesoriero, P. J., Ani, L., Meftah, M., Schwarzkopf, R., & Leucht, P.
- Venue
- Orthopedics
- Publisher
- SLACK, Inc.
- Published
- 2020-09
- Volume
- 43
- Issue
- 5
- ISSN
- 0147-7447
- 1938-2367
- Cited by
- 31
- References
- 3
- Language
- en
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