A preliminary study on evaluating Consultation Notes with Post-Editing
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Abstract (arXiv)
Automatic summarisation has the potential to aid physicians in streamlining clerical tasks such as note taking. But it is notoriously difficult to evaluate these systems and demonstrate that they are safe to be used in a clinical setting. To circumvent this issue, we propose a semi-automatic approach whereby physicians post-edit generated notes before submitting them. We conduct a preliminary study on the time saving of automatically generated consultation notes with post-editing. Our evaluators are asked to listen to mock consultations and to post-edit three generated notes. We time this and find that it is faster than writing the note from scratch. We present insights and lessons learnt from this experiment.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 1 of its 19 references that are in the map. First posted on arXiv 9 Apr 2021.
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1 of 19 references.
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Citing papers per year: 2022: 4; 2023: 2; 2024: 1; 2025: 3.
10 papers.
- 2023MeetingBank: A Benchmark Dataset for Meeting Summarizationcited by 81
- 2022Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generationcited by 33
- 2022PriMock57: A Dataset Of Primary Care Mock Consultationscited by 30
- 2025Hallucination Detection in Large Language Models with Metamorphic Relationscited by 24
- 2022User-Driven Research of Medical Note Generation Softwarecited by 13
- 2023Extrinsically-Focused Evaluation of Omissions in Medical Summarizationcited by 4
- 2024Toward Relieving Clinician Burden by Automatically Generating Progress Notes using Interim Hospital Datacited by 4
- 2022An Exploration of Post-Editing Effectiveness in Text Summarizationcited by 3
- 2025CLARITY: Clinical Assistant for Routing, Inference, and Triagecited by 2
- 2025How to Evaluate Medical AIcited by 1
- 2025CLARITY: Clinical Assistant for Routing, Inference, and Triage2 Oct 2025
- 2025How to Evaluate Medical AI15 Sep 2025
- 2025Hallucination Detection in Large Language Models with Metamorphic Relations20 Feb 2025
- 2024Toward Relieving Clinician Burden by Automatically Generating Progress Notes using Interim Hospital Data10 Oct 2024
- 2023Extrinsically-Focused Evaluation of Omissions in Medical Summarization14 Nov 2023
- 2023MeetingBank: A Benchmark Dataset for Meeting Summarization27 May 2023
- 2022An Exploration of Post-Editing Effectiveness in Text Summarization13 Jun 2022
- 2022User-Driven Research of Medical Note Generation Software5 May 2022
- 2022Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation1 Apr 2022
- 2022PriMock57: A Dataset Of Primary Care Mock Consultations1 Apr 2022