The National Health Service (NHS) uses the Friends and Family Test (FFT) to gather patient feedback aimed at improving service quality and satisfaction. The current workflow involves FFT feedback processed manually by the Patient Experience team to identify sentiment and theme, which is a time-consuming and unsustainable approach given the large volume of data. To address this, we proposed and implemented a two-phase solution that is adaptable with limited computational resources that has no access to GPUs: (1) training and evaluation of lightweight, fine-tuned transformer-based models using routinely collected, expert-reviewed feedback data, and (2) deployment of these models within an automated pipeline for sentiment prediction and theme classification, involving a human-in-the-loop verification process. Our experiments and results validated on expert-annotated data collected over a period of 3.5 years achieve the best performance with an F1-score of 88.14% in sentiment classification and 71.47% in theme classification. This paper details the implementation, testing, and monitoring of our proposed solution in a paediatric healthcare setting, highlighting the ability to generate near-real-time results. Our deployed solution has substantially decreased the time needed to manually process approximately 2000 feedback pieces each month by delivering automated outcomes daily, a task that previously required several days for completion. In addition, we discuss the broader implications of our findings and potential enhancements for scalable deployment across NHS trusts. Source code will be open-sourced and published here: https://github.com/gosh-dre/FFT-NLP-Pipeline.

SmallEiffel is an Eiffel compiler which uses a fast simple type inference mechanism to remove most late binding calls, replacing them by static bindings. Starting from the system's entry point, it compiles only statically living code, which saves compiling and then removing dead code. As the whole system is analyzed at compile time, multiple inheritance and genericity do not cause any overhead.SmallEiffel features a coding scheme which eliminates the need for virtual function tables. Dynamic dispatch is implemented without any array access but uses a simple static binary branch code. We show that this implementation makes it possible to use modern hardware very efficiently. It also allows us to inline more calls even when dynamic dispatch is required. Some more dispatch sites are removed after the type inference algorithm has been performed, if the different branches of a dispatch site lead to the same code.The advantage of this approach is that it greatly speeds up execution time and considerably decreases the amount of generated code.

We present a syntactic approach to technical drawing dimensions analysis. A specific grammar is used to describe dimensions of drawings. This grammar can be graphically designed by combining different graphic primitives. The algorithm used for analysis can start at different points of the grammar. The analysis proceeds bottom-up and top-down according to previously obtained results.

Two kinds of knowledge can be distinguished in technical document interpretation: structural and syntactic knowledge, referring to the representation rules of objects, and semantic knowledge, referring to the objects themselves. It is shown how drawing analysis can be seen as a reading of the document in terms of discrete and non-discrete signs, the reading strategies being activated by specific symbols. An example of such a reading for a specific layer, annotations such as dimensioning, is presented.>