Discharge summary hospital course summarisation of in patient Electronic Health Record text with clinical concept guided deep pre-trained Transformer models

• Published in: Journal of biomedical informatics 2023-05, Vol.141, p.104358-104358, Article 104358
• Main Authors: Searle, Thomas, Ibrahim, Zina, Teo, James, Dobson, Richard J.B.
• Format: Article
• Language: English
• Subjects: Clinical natural language processing; Clinical text summarisation; Documentation; Electronic Health Records; Health Records, Personal; Hospitals; Humans; Natural Language Processing; Patient Discharge; Pre-trained, deep learning, fine-tuned models for clinical summarisation
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2023.104358

Brief Hospital Course (BHC) summaries are succinct summaries of an entire hospital encounter, embedded within discharge summaries, written by senior clinicians responsible for the overall care of a patient. Methods to automatically produce summaries from inpatient documentation would be invaluable in reducing clinician manual burden of summarising documents under high time-pressure to admit and discharge patients. Automatically producing these summaries from the inpatient course, is a complex, multi-document summarisation task, as source notes are written from various perspectives (e.g. nursing, doctor, radiology), during the course of the hospitalisation. We demonstrate a range of methods for BHC summarisation demonstrating the performance of deep learning summarisation models across extractive and abstractive summarisation scenarios. We also test a novel ensemble extractive and abstractive summarisation model that incorporates a medical concept ontology (SNOMED) as a clinical guidance signal and shows superior performance in 2 real-world clinical data sets. [Display omitted] •Discharge summary summarisation via clinically guided deep learning.•Deep learning natural language processing models with clinical concept guidance.•Inpatient discharge summary text summarisation with natural language processing.•Ensemble extractive/abstractive text summarisation for discharge summary text.