As part of HUMAINE (HUMan centered AI NEtwork), we are developing a privacy-preserving automatic speech recognition (ASR) system for the use case of automated, structured documentation of nurse-patient interviews.This paper firstly describes the training and evaluation for our German-language end-to-end model. It is based on a convolutional recurrent DNN model and optimized using sequence-to-sequence learning.Utilizing SpeechBrain in training, we achieve a word error rate below 9% for an open-vocabulary task. The small memory footprint of our model allows for the recognizer to be executed on a desktop computer, enabling privacy-preserving, purely local speech transcription.Secondly, we describe our initial natural language processing (NLP) experiments for transforming text to structured information, based on an own labeled dialogue dataset, covering five thematic question-answer pairs of every-day dialogues. We evaluated different machine learning algorithms for grouping dialogue snippets to topics. Overall, a random forest model yielded the best classification accuracy of about 90%. Next, supported by health experts, we will create a database of realistic nurse-patient dialogues, containing information to fill in a specific professional patient admission form, to evaluate our NLP model.Within this contribution, we detail the concept and performance of our ASR and NLP system.