Optical Character Recognition (OCR) is extremely useful in various sectors for exploring massive archived data. This technology enables the digitization of printed and handwritten texts that are frequently present in the medical field. For instance, medication error (ME) reports were previously and still in some healthcare facilities written manually, this has led to the accumulation of numerous handwritten data that are unfortunately challenging to exploit. Their digitization through OCR allows extracting important data from these documents and using them to populate the database to implement future analysis techniques to optimize the medication error management process. This paper presents a transformer-based handwritten recognition architecture that employs the Transformer-Based Optical Character Recognition (TrOCR) model combined with image segmentation techniques. Although the TrOCR model provided by Microsoft performs reasonably well in handwritten recognition, it is limited to English text because its pretrained version was trained exclusively on English samples. This limitation is problematic for us, as our task involves digitizing French medication dictation errors. Additionally, its limitation to processing single-line text images impairs its ability to recognize paragraphs. To address these limitations, we will fine-tune the model on French handwritten data and integrate a single-line level segmentation technique, thereby overcoming these constraints. Therefore, the preliminary results from implementing our proposed architecture are promising for the digitization of medication error reports.

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,

The Medication Use Process (MUP) plays a vital role in ensuring the appropriate and safe use of medications. However, this process is exposed to many risks in particular medication errors. These later present a significant challenge, that may have negative consequences for patients. Implementing a medication error reporting and analyzing tool would facilitate an in-depth analysis of the causes of errors and enable the implementation of corrective measures. Additionally, a specific risk management model for medication management, which includes regular monitoring and preventive actions, is essential to prevent medication errors. By implementing these measures, We guarantee the attainment of secure and efficient management of MUP, resulting in better therapeutic results and heightened patient safety. In this work, we propose a tool that helps healthcare professionals create reports and analyze medication errors. This tool has been made and tested with healthcare professionals of the Intercommunal Hospital Center Castres-Mazamet (CHIC). This collaborative effort aimed not only to detect areas for improvement but also to instigate positive change in patient care. By leveraging our innovative tool, healthcare providers were empowered to proactively analyze and report medication errors.

Medication errors associated with the Medication Use Process present significant risks and require effective management. However, current practices do not address these risks. There is a lack of awareness that medication errors are unintended risks, and a lack of dedicated systems to manage them. To overcome these limitations, a digital system exploiting massive medical data (big data) is proposed. By integrating various data sources, this system aims to provide adaptable medication errors’ management and continuous improvement. This article presents an overview of the system requirements and highlights the potential of Data Mining in healthcare. Implementing this system could revolutionize medication error management and improve patient safety.

Improving patient safety and quality of care has become a priority for healthcare organizations, given the frequency and potential severe clinical consequences of medication errors and adverse drug events during the Medication-use Process. Medication error reporting can be one most effective strategies to achieve these goals. Indeed, high error reporting rates indicate a positive safety culture rather than an unsafe healthcare environment. It is through the identification of these errors safety barriers can be put in place to prevent a similar event from occurring in the future. However, current healthcare organizations still suffer from a lack of attention in this context, especially in establishing a digital tool dedicated to medication error reporting, which is the motivation for the work described in this paper. The latter proposes a novel tool to make the local reporting of medication errors easier and encourage reporting these errors with the healthcare professionals' confidentially. The overall response to the tool provided was positive from the staff participating.