Toward Reporting Support and Quality Assessment for Learning from Reporting: A Necessary Data Elements Model for Narrative Medication Error Reports

To understand and prevent medication errors, spontaneous reporting systems are developed and implemented to aggregate medication error reports for root cause analysis (RCA). Despite of the rich relational information in medication error reports, low quality, especially incompleteness, impedes effective utilization of the reports for analyzing and learning. The lack of a completeness evaluation tool for narrative medication error reports is a barrier to improving the quality of reports. Moreover, no effective mechanisms are integrated in reporting systems for knowledge support upon reporting. In this study, we developed a minimal data model which defines necessary elements in narrative medication error reports and utilized it to evaluate patient safety organization (PSO) medication reports. This study holds promise in bridging the gap between the low quality of narrative reports and the needs of analyzing and learning from medication errors