Research into automatic text simplification aims to promote access to information for all members of society. To facilitate generalizability, simplification research often abstracts away from specific use cases, and targets a prototypical reader and an underspecified content creator. In this paper, we consider a real-world use case – simplification technology for use in Dutch municipalities – and identify the needs of the content creators and the target audiences in this use case. The stakeholders envision a system that (a) assists the human writer without taking over the task; (b) can provide diverse alternative outputs, tailored for specific target audiences; and (c) can explain and motivate the suggestions that it outputs. These requirements call for technology that is characterized by modularity, explainability, and variability. We believe that these are important research directions that require further exploration.

Electronic Health Records contain a lot of information in natural language that is not expressed in the structured clinical data. Especially in the case of new diseases such as COVID-19, this information is crucial to get a better understanding of patient recovery patterns and factors that may play a role in it. However, the language in these records is very different from standard language and generic natural language processing tools cannot easily be applied out-of-the-box. In this paper, we present a fine-tuned Dutch language model specifically developed for the language in these health records that can determine the functional level of patients according to a standard coding framework from the World Health Organization. We provide evidence that our classification performs at a sufficient level to generate patient recovery patterns that can be used in the future to analyse factors that contribute to the rehabilitation of COVID-19 patients and to predict individual patient recovery of functioning.