Study on the Accuracy of Official Recording of Nosological Codes in an Italian Regional Hospital Registry

Lovison, G; Bellini, Paolo

doi:10.1055/s-0038-1635560

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…The investigation by Lovison and Bellini reported in this issue [2] concerns the reliability of medical record abstracting in a region of northern Italy. Although the fiscal interests noted by the North American researchers were not a issue in the Italian study, Lovison and Bellini discovered an overall coding error rate of 12.4 percent.…”

Section: A Musenmentioning

confidence: 99%

The Strained Quality of Medical Data

Musen¹

1989

Methods Inf Med

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Section: A Musenmentioning

confidence: 99%

The Strained Quality of Medical Data

Musen¹

1989

Methods Inf Med

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Comparing Medical Record Abstraction (MRA) error rates in an observational study to pooled rates identified in the data quality literature

Garza,

Williams,

Ounpraseuth

et al. 2024

BMC Med Res Methodol

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Background Medical record abstraction (MRA) is a commonly used method for data collection in clinical research, but is prone to error, and the influence of quality control (QC) measures is seldom and inconsistently assessed during the course of a study. We employed a novel, standardized MRA-QC framework as part of an ongoing observational study in an effort to control MRA error rates. In order to assess the effectiveness of our framework, we compared our error rates against traditional MRA studies that had not reported using formalized MRA-QC methods. Thus, the objective of this study was to compare the MRA error rates derived from the literature with the error rates found in a study using MRA as the sole method of data collection that employed an MRA-QC framework. Methods A comparison of the error rates derived from MRA-centric studies identified as part of a systematic literature review was conducted against those derived from an MRA-centric study that employed an MRA-QC framework to evaluate the effectiveness of the MRA-QC framework. An inverse variance-weighted meta-analytical method with Freeman-Tukey transformation was used to compute pooled effect size for both the MRA studies identified in the literature and the study that implemented the MRA-QC framework. The level of heterogeneity was assessed using the Q-statistic and Higgins and Thompson’s I 2 statistic. Results The overall error rate from the MRA literature was 6.57%. Error rates for the study using our MRA-QC framework were between 1.04% (optimistic, all-field rate) and 2.57% (conservative, populated-field rate), 4.00–5.53% points less than the observed rate from the literature ( p < 0.0001). Conclusions Review of the literature indicated that the accuracy associated with MRA varied widely across studies. However, our results demonstrate that, with appropriate training and continuous QC, MRA error rates can be significantly controlled during the course of a clinical research study. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-024-02424-x.

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Quantifying Data Quality for Clinical Trials Using Electronic Data Capture

Nahm¹,

Pieper

Cunningham

2008

PLoS ONE

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BackgroundHistorically, only partial assessments of data quality have been performed in clinical trials, for which the most common method of measuring database error rates has been to compare the case report form (CRF) to database entries and count discrepancies. Importantly, errors arising from medical record abstraction and transcription are rarely evaluated as part of such quality assessments. Electronic Data Capture (EDC) technology has had a further impact, as paper CRFs typically leveraged for quality measurement are not used in EDC processes.Methods and Principal FindingsThe National Institute on Drug Abuse Treatment Clinical Trials Network has developed, implemented, and evaluated methodology for holistically assessing data quality on EDC trials. We characterize the average source-to-database error rate (14.3 errors per 10,000 fields) for the first year of use of the new evaluation method. This error rate was significantly lower than the average of published error rates for source-to-database audits, and was similar to CRF-to-database error rates reported in the published literature. We attribute this largely to an absence of medical record abstraction on the trials we examined, and to an outpatient setting characterized by less acute patient conditions.ConclusionsHistorically, medical record abstraction is the most significant source of error by an order of magnitude, and should be measured and managed during the course of clinical trials. Source-to-database error rates are highly dependent on the amount of structured data collection in the clinical setting and on the complexity of the medical record, dependencies that should be considered when developing data quality benchmarks.

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Study on the Accuracy of Official Recording of Nosological Codes in an Italian Regional Hospital Registry

Cited by 4 publications

References 10 publications

The Strained Quality of Medical Data

The Strained Quality of Medical Data

Comparing Medical Record Abstraction (MRA) error rates in an observational study to pooled rates identified in the data quality literature

Quantifying Data Quality for Clinical Trials Using Electronic Data Capture

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