HeaRT: Health Record Timeliner to visualise patients' medical history from health record text

TL;DR

HeaRT is a system that visualizes patients' medical histories from unstructured EHR text using advanced language models to extract and chronologically align clinical entities, providing clear timelines to improve understanding and reduce clinical errors.

Contribution

This paper introduces HeaRT, a novel web-based visualization system that leverages a state-of-the-art language model to extract and align clinical entities from unstructured EHR text into coherent timelines.

Findings

Successfully generated clinical timelines from radiology reports written by different radiologists.

Achieved practical performance in extracting and visualizing medical histories from unstructured text.

Demonstrated feasibility of timeline visualization for complex medical records.

Abstract

Electronic health records (EHRs), which contain patients' medical histories, tend to be written in freely formatted (unstructured) text because they are complicated by their nature. Quickly understanding a patient's history is challenging and critical because writing styles vary among doctors, which may even cause clinical incidents. This paper proposes a Health Record Timeliner system (HeaRT), which visualises patients' clinical histories directly from natural language text in EHRs. Unlike only a few previous attempts, our system achieved feasible and practical performance for the first time, by integrating a state-of-the-art language model that recognises clinical entities (e.g. diseases, medicines, and time expressions) and their temporal relations from the raw text in EHRs and radiology reports. By chronologically aligning the clinical entities to the clinical events extracted from a medical report, this web-based system visualises them in a Gantt chart-like format. Our novel evaluation method showed that the proposed system successfully generated coherent timelines from the two sets of radiology reports describing the same CT scan but written by different radiologists. Real-world assessments are planned to improve the remaining issues.