FactsR: A Safer Method for Producing High Quality Healthcare Documentation
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Abstract (arXiv)
There are now a multitude of AI-scribing solutions for healthcare promising the utilization of large language models for ambient documentation. However, these AI scribes still rely on one-shot, or few-shot prompts for generating notes after the consultation has ended, employing little to no reasoning. This risks long notes with an increase in hallucinations, misrepresentation of the intent of the clinician, and reliance on the proofreading of the clinician to catch errors. A dangerous combination for patient safety if vigilance is compromised by workload and fatigue. In this paper, we introduce a method for extracting salient clinical information in real-time alongside the healthcare consultation, denoted Facts, and use that information recursively to generate the final note. The FactsR method results in more accurate and concise notes by placing the clinician-in-the-loop of note generation, while opening up new use cases within real-time decision support.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 15 May 2025.
Builds on
11 of 21 references.
References · 11
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
- 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
- 2024LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methodscited by 465
- 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 246
- 2025Survey on Evaluation of LLM-based Agentscited by 204
- 2020Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structurescited by 49
- 2022PriMock57: A Dataset Of Primary Care Mock Consultationscited by 31
- 2023SummQA at MEDIQA-Chat 2023:In-Context Learning with GPT-4 for Medical Summarizationcited by 13
- 2025Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Predictioncited by 12
- 2024What's Wrong? Refining Meeting Summaries with LLM Feedbackcited by 8
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Citing papers per year: 2026: 2.
2 papers.