MedT5SQL: a transformers-based large language model for text-to-SQL conversion in the healthcare domain.

Introduction: In response to the increasing prevalence of electronic medical records (EMRs) stored in databases, healthcare staff are encountering difficulties retrieving these records due to their limited technical expertise in database operations. As these records are crucial for delivering appropriate medical care, there is a need for an accessible method for healthcare staff to access EMRs. Methods: To address this, natural language processing (NLP) for Text-to-SQL has emerged as a solution, enabling non-technical users to generate SQL queries using natural language text. This research assesses existing work on Text-to-SQL conversion and proposes the MedT5SQL model specifically designed for EMR retrieval. The proposed model utilizes the Text-to-Text Transfer Transformer (T5) model, a Large Language Model (LLM) commonly used in various text-based NLP tasks. The model is fine-tuned on the MIMICSQL dataset, the first Text-to-SQL dataset for the healthcare domain. Performance evaluation involves benchmarking the MedT5SQL model on two optimizers, varying numbers of training epochs, and using two datasets, MIMICSQL and WikiSQL. Results: For MIMICSQL dataset, the model demonstrates considerable effectiveness in generating question-SQL pairs achieving accuracy of 80.63%, 98.937%, and 90% for exact match accuracy matrix, approximate string-matching, and manual evaluation, respectively. When testing the performance of the model on WikiSQL dataset, the model demonstrates efficiency in generating SQL queries, with an accuracy of 44.2% on WikiSQL and 94.26% for approximate string-matching. Discussion: Results indicate improved performance with increased training epochs. This work highlights the potential of fine-tuned T5 model to convert medical-related questions written in natural language to Structured Query Language (SQL) in healthcare domain, providing a foundation for future research in this area.

The Role of Mindfulness in Mitigating the Negative Consequences of Technostress.

IT offers significant benefits both to individuals and organisations, such as during the Covid-19 pandemic where technology played a primary role in aiding remote working environments; however, IT use comes with consequences such as 'technostress' - stress arising from extended use of technology. Addressing the paucity of research related to this topic, in this study, we examine the role of mindfulness and IT mindfulness to both mitigate the impact of technostress and alleviate its negative consequences; revealing that mindfulness can reduce technostress and increase job satisfaction, while IT mindfulness can enhance user satisfaction and improve task performance. Moreover, our work sheds light on the under-researched relationship between mindfulness and IT mindfulness; showing that the latter has a stronger influence on IT related outcomes; revealing the valuable role of mindfulness and IT mindfulness in the workplace and offering important implications to theory and practice.