Garza Maryam Y, Williams Tremaine B, Ounpraseuth Songthip, Hu Zhuopei, Lee Jeannette, Snowden Jessica, Walden Anita C, Simon Alan E, Devlin Lori A, Young Leslie W, Zozus Meredith N
University of Arkansas for Medical Sciences.
University of Colorado Denver, Anschutz Medical Campus.
Res Sq. 2023 Mar 27:rs.3.rs-2692906. doi: 10.21203/rs.3.rs-2692906/v1.
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.
Using a moderator meta-analysis employed with Q-test, the MRA error rates from the meta-analysis of the literature were compared with the error rate from a recent study that implemented formalized MRA training and continuous QC processes.
The MRA process for data acquisition in clinical research was associated with both high and highly variable error rates (70 - 2,784 errors per 10,000 fields). 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) (or 104 - 257 errors per 10,000 fields), 4.00 - 5.53 percentage points less than the observed rate from the literature (p<0.0001).
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.
病历摘要提取（MRA）是临床研究中常用的数据收集方法，但容易出错，并且在研究过程中很少对质量控制（QC）措施的影响进行评估，且评估结果也不一致。作为一项正在进行的观察性研究的一部分，我们采用了一种新颖的、标准化的MRA-QC框架，以控制MRA错误率。为了评估我们框架的有效性，我们将我们的错误率与未报告使用正式MRA-QC方法的传统MRA研究的错误率进行了比较。因此，本研究的目的是将文献中得出的MRA错误率与一项使用MRA作为唯一数据收集方法并采用MRA-QC框架的研究中发现的错误率进行比较。
使用带有Q检验的调节元分析，将文献元分析中的MRA错误率与最近一项实施了正式MRA培训和持续QC流程的研究中的错误率进行比较。
临床研究中数据采集的MRA过程与高且高度可变的错误率相关（每10000个字段有70 - 2784个错误）。使用我们的MRA-QC框架的研究的错误率在1.04%（乐观的全字段率）和2.57%（保守的填充字段率）之间（即每10000个字段有104 - 257个错误），比文献中观察到的率低4.00 - 5.53个百分点（p<0.0001）。
文献综述表明，不同研究中与MRA相关的准确性差异很大。然而，我们的结果表明，通过适当的培训和持续的QC，可以在临床研究过程中显著控制MRA错误率。
