TY - CHAP A1 - Gerlach, Navina A1 - Schlößler, Kathrin A1 - Steinbuck, Joana A1 - Bleek, Julian A1 - Günster, Christian A1 - Marschall, Ursula A1 - Schneider, Udo A1 - Horenkamp-Sonntag, Dirk A1 - Sundmacher, Leonie A1 - König, Hans-Helmut A1 - Zeymer, Uwe A1 - Schneider, Steffen A1 - Werdan, Karl A1 - Weber, Michael A1 - Schäfer, Corinna A1 - Klemperer, David A1 - Kopp, Ina A1 - Nothacker, Monika A1 - Donner-Banzhoff, Norbert T1 - 104 Reducing unwarranted variations in frequency of coronary angiographies in germany by describing, understanding and modifying local practice T2 - BMJ Evidence-Based Medicine, Poster and conversation presentations : Preventing Overdiagnosis, Abstracts, August 2018, Copenhagen N2 - Objectives Germany has one of the highest numbers of coronary angiography worldwide. Nevertheless, we find a great variation between German regions for both, diagnostic coronary angiographies and percutaneous coronary interventions (PCI). We assume that this variation is not only due to variation in morbidity but also reflects factors such as guideline adherence, physician-patient communication and access to care. In this mixed method project, we aim to first describe the variation of coronary angiographies and PCIs in different German regions. Secondly, we explore current practices and motives for (non-)adherences to guidelines in the diagnostic process of patients with suspected CHD. Based on these results, we consequently plan to develop a complex intervention (treatment pathway) to improve guideline adherence and thus appropriateness of coronary angiography. Method The projects will be organized according to the recommendation of the Medical Research Council for the development and evaluation of complex interventions. The first study will use descriptive methods based on routine data of three German Health Care Insurances and registry data to describe status quo and associated factors of coronary angiography. The second study will use qualitative methods to understand barriers and facilitators of guideline adherence and medical decision making. Furthermore, we will discuss variations in care and thereby identify implementation targets for the planned treatment pathway. Based on this information, we will develop local treatment pathways in four selected regions. Relevant peers will develop the local pathway in group discussions. Using this bottom-up approach, we directly address implementation challenges. Y1 - 2018 U6 - https://doi.org/10.1136/bmjebm-2018-111070.104 PB - BMJ Publishing Group Ltd ER - TY - CHAP A1 - Ranzenberger, Thomas A1 - Bocklet, Tobias A1 - Freisinger, Steffen A1 - Georges, Munir A1 - Glocker, Kevin A1 - Herygers, Aaricia A1 - Riedhammer, Korbinian A1 - Schneider, Fabian A1 - Simic, Christopher A1 - Zakaria, Khabbab ED - Baumann, Timo T1 - Extending HAnS: Large Language Models For Question Answering, Summarization, And Topic Segmentation In An ML-based Learning Experience Platform T2 - Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. März 2024 N2 - The use of chatbots based on large language models (LLMs) and their impact on society are influencing our learning experience platform Hochschul-Assistenz-System (HAnS). HAnS uses machine learning (ML) methods to support students and lecturers in the online learning and teaching processes [1]. This paper introduces LLM-based features available in HAnS which are using the transcript of our improved Automatic Speech Recognition (ASR) pipeline with an average transcription duration of 45 seconds and an average word error rate (WER) of 6.66% on over 8 hours of audio data of 7 lecture videos. A LLM-based chatbot could be used to answer questions on the lecture content as the ASR transcript is provided as context. The summarization and topic segmentation uses the LLM to improve our learning experience platform. We generate multiple choice questions using the LLM and the ASR transcript as context during playback in a period of 3 minutes and display them in the HAnS frontend. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-71032 SN - 978-3-95908-325-6 SP - 219 EP - 224 PB - TUDpress CY - Dresden ER -