Improving the Error Review Process for Incident Reports at UT Southwestern Through the Use of a Standardized Taxonomy Tool

BACKGROUND: At UT Southwestern and in other hospital systems, medical errors are often the result of latent system errors. These errors may remain unreported due to a variety of reasons. When they are reported, the defect analysis protocols triggered by error review are often inadequate for identification and correction of the underlying system defect. The aim of this project was to improve the process for classifying medical errors at UT Southwestern by testing the use of a standardized taxonomy tool, refining the tool to reduce inter-rater variability, and incorporating its use into the existing workflow for real-time error review. Of particular interest was the subset of medical errors related to OR-ICU handover. METHODS/INTERVENTION: The taxonomy tool used in this project was created by Dr. Isaac Lynch, cardiac anesthesia and intensive care faculty at UT Southwestern. It is a form that allows categorization of several important parameters described by both the World Health Organization's 2005 "Draft Guidelines for Adverse Event Reporting and Learning Systems" and 2014 "Minimal Information Model for Patient Safety Incident Reporting" guidelines. For example, the tool allows for designation of an error as being "administrative," "medication," or "handover-related." Properly trained Clinical Safety personnel can use the taxonomy tool to quickly review incident reports in real-time as they are submitted. For this project, all ICU incident reports submitted through the UT Southwestern Clements University hospital reporting system in 2015, a total of 1317 reports, were reviewed and classified according to the taxonomy tool. The collected data was analyzed using REDCapTM built-in statistical modeling tools for hazard identification and trends. Based on this initial trial run, the taxonomy tool was refined to improve functionality and reduce inter-rater variability that may occur when different people use the tool. Analysis of the data also showed how taxonomic categorization can highlight system errors that are in need of targeted intervention. Finally, a process map was created to illustrate how use of this taxonomy tool can be incorporated into the existing error review workflow at UT Southwestern. RESULTS: Analysis of the incident reports from 2015 and classification according to the taxonomy tool revealed some specific areas in need of process improvement. For example, there were a significant number of reports related to inappropriate specimen labeling and also many reports describing patient pressure ulcers. Use of the taxonomy tool enabled identification and classification of these medical errors. Future, in-depth analysis can then be used to inform targeted intervention. Notable statistics from the analysis are that 46% of the reports described errors occurring during perioperative care, 18% were medication errors, and 18% were diagnostic errors. To further classify the factors contributing to these different errors, nearly 52% of the total incident reports were latent system errors, 58% could be attributed to staff error, and 45% had some component of patient cause. Of note, a significant 70% of the latent system errors were equipment-related. Also, only 5.5% of the reports described errors that were probably related to handover, with 78.4% unlikely to be related to handover. Finally, 61% of the medical errors caused temporary harm to the patient, with 1.9% describing errors that contributed to patient death. CONCLUSION: This project demonstrated that it is both practical and helpful to use a standardized taxonomy tool for routine, real-time error review. Review of a single incident report and completion of the corresponding taxonomy form takes an average of 7 minutes for trained personnel so there will not be a significant additional work burden for Clinical Safety specialists. The data collected in this project has been helpful for identifying specific system errors, and classification of the types of error has utility in determining the best intervention strategies. The gradual accumulation of data can also be used for trend identification and epidemiological studies. In terms of next steps, the taxonomy tool, which has already been modified based on this initial test run, will undergo further trial testing to improve inter-rater reliability. This will involve training other medical students in the tool's proper use and having them repeat error review on a subset of the 2015 ICU reports, before then continuing the analysis with ICU reports from subsequent years. Future projects will also focus on integrating use of the taxonomy tool into the existing error review workflow, developing a dashboard module for real-time trend analysis, and enabling human languages algorithm functionality. The vision is for the taxonomy tool to be employed by Clinical Safety personnel in real-time to generate useable classification data for fixing latent system errors.