Automated Processing of Medication Error Reports with a GPT Transformer Model

Abstract

Because of their potentially serious consequences, Medication errors (ME) represent a major challenge for health-care facilities. To manage these errors and minimize their seriousness, healthcare professionals follow a collaborative management process that relies on reporting and then analyzing the reports filled in by them via various reporting tools, both digital and paper-based. These tools can be customized requiring specific information to be entered in multiple forms with commonly the presence of textual descriptions to fill in a free-text field. Therefore, text analysis is crucial for thoroughly understanding and effectively analyzing medication errors. Given the large volume of reports to be quickly processed, it is essential to help healthcare professionals prioritize which ME to analyze. In this context, we propose, in this work, processing ME reports with natural language processing tasks using Transformer models such as GPT. In this study, we present the extraction of key information from the reports to help structure textual descriptions of ME with the GPT-4 transformer model. The results obtained show the potential of this model to extract relevant information from ME descriptions in French language without any deep fine-tuning,

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