The use of computers in clinical trials

From mouthpiece of an emerging specialty to voice for high-quality research: the first 100 years of the British Journal of Anaesthesia

The British Journal of Anaesthesia (BJA) celebrates its centenary in 2023, and with it 100 yr of continuous anaesthesia research publication. As an editorially and financially independent journal, the BJA faced a rapidly changing anaesthesia profession, health system, and publishing world without the security of institutional support. In its early days, the Journal was vocal about the challenging conditions faced by anaesthetists before the National Health System was established, and was essential in advocating for the specialty. Although the years after World War II brought improving fortunes for the specialty, the BJA found itself struggling to publish. As the Journal's fortunes began to improve, a new research and healthcare context emerged, radically changing the face of anaesthesia research and practice, to which the Journal needed to adapt. In spite of a range of challenges throughout the years, the BJA has developed into an international, future-focused, well-respected publication. This could not have been achieved without continual transformation, and the willingness to take risks and meet the changing times head on.

Comparing Medical Record Abstraction (MRA) Error Rates in an Observational Study to Pooled Rates Identified in the Data Quality Literature

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

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.

Results

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).

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.

A Rule-Based Data Quality Assessment System for Electronic Health Record Data

OBJECTIVE

Rule-based data quality assessment in health care facilities was explored through compilation, implementation, and evaluation of 63,397 data quality rules in a single-center case study to assess the ability of rules-based data quality assessment to identify data errors of importance to physicians and system owners.

METHODS

We applied a design science framework to design, demonstrate, test, and evaluate a scalable framework with which data quality rules can be managed and used in health care facilities for data quality assessment and monitoring.

RESULTS

We identified 63,397 rules partitioned into 28 logic templates. A total of 819,683 discrepancies were identified by 4.5% of the rules. Nine out of 11 participating clinical and operational leaders indicated that the rules identified data quality problems and articulated next steps that they wanted to take based on the reported information.

DISCUSSION

The combined rule template and knowledge table approach makes governance and maintenance of otherwise large rule sets manageable. Identified challenges to rule-based data quality monitoring included the lack of curated and maintained knowledge sources relevant to data error detection and lack of organizational resources to support clinical and operational leaders with investigation and characterization of data errors and pursuit of corrective and preventative actions. Limitations of our study included implementation within a single center and dependence of the results on the implemented rule set.

CONCLUSION

This study demonstrates a scalable framework (up to 63,397 rules) with which data quality rules can be implemented and managed in health care facilities to identify data errors. The data quality problems identified at the implementation site were important enough to prompt action requests from clinical and operational leaders.