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Paper Number
1195
Paper Type
Completed
Description
Electronic health records (EHR) have significantly amplified the volume of information accessible in the healthcare sector. Nevertheless, this information load also translates into elevated workloads for clinicians engaged in extracting and generating patient information. Natural Language Process (NLP) aims to overcome this problem by automatically extracting and structuring relevant information from medical texts. While other methods related to artificial intelligence have been implemented successfully in healthcare (e.g., computer vision in radiology), NLP still lacks commercial success in this domain. The lack of a structured overview of NLP systems is exacerbating the problem, especially with the emergence of new technologies like generative pre-trained transformers. Against this background, this paper presents a taxonomy to inform integration decisions of NLP systems into healthcare IT landscapes. We contribute to a better understanding of how NLP systems can be integrated into daily clinical contexts. In total, we reviewed 29 papers and 36 commercial NLP products.
Recommended Citation
Braun, Marvin; Kolbe, Lutz; and Neumann, Caspar, "Natural Language Processing for Medical Texts – A Taxonomy to Inform Integration Decisions into Clinical Practice" (2023). ICIS 2023 Proceedings. 8.
https://aisel.aisnet.org/icis2023/ishealthcare/ishealthcare/8
Natural Language Processing for Medical Texts – A Taxonomy to Inform Integration Decisions into Clinical Practice
Electronic health records (EHR) have significantly amplified the volume of information accessible in the healthcare sector. Nevertheless, this information load also translates into elevated workloads for clinicians engaged in extracting and generating patient information. Natural Language Process (NLP) aims to overcome this problem by automatically extracting and structuring relevant information from medical texts. While other methods related to artificial intelligence have been implemented successfully in healthcare (e.g., computer vision in radiology), NLP still lacks commercial success in this domain. The lack of a structured overview of NLP systems is exacerbating the problem, especially with the emergence of new technologies like generative pre-trained transformers. Against this background, this paper presents a taxonomy to inform integration decisions of NLP systems into healthcare IT landscapes. We contribute to a better understanding of how NLP systems can be integrated into daily clinical contexts. In total, we reviewed 29 papers and 36 commercial NLP products.
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16-HealthCare