Optimizing Medical Service Request Processes through Language Modeling and Semantic Search

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Abstract
Medical service requests are a crucial part of the workflow in hospitals and healthcare organizations. However, the process of requesting medical services can be time consuming and can require physicians and medical personnel to navigate complex interfaces and enter detailed information about the requested service. In this paper, we propose a system that uses machine learning techniques such as large language models and semantic search to optimize the process of requesting medical services. Our approach enables physicians to request medical services using natural language rather than navigating complex interfaces, allowing for more efficient and flexible interactions with hospital information systems. We evaluate our approach on real-world data and discuss the implications of our work for the future of digital health care. Our results suggest that our approach has the potential to streamline the process of requesting medical services and reduce the time and manual effort required in the daily hospital routine.
Funding Information
  • This research was supported by the Bavarian State Ministry for Science and the Arts within the Digitalisierungszentrum für Präzisions- und Telemedizin (DZ.PTM) project, as part of the master plan BAYERN DIGITAL II.