Discharge Summary Hospital Course Summarisation of In Patient Electronic Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer Models

TL;DR

This paper explores deep learning models, including a novel ensemble approach guided by clinical concepts, to automatically generate hospital discharge summaries from inpatient records, aiming to reduce clinician workload.

Contribution

Introduces a novel ensemble summarisation model incorporating SNOMED clinical ontology for improved hospital course summarisation.

Findings

Deep learning models outperform traditional methods in summarisation tasks.

The ensemble model with clinical concept guidance shows superior performance.

Results validated on two real-world clinical datasets.

Abstract

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.