PERSPECTIVES观点

Mary Steps Out: Capturing Patient Experience through Qualitative and AI Methods

V. Renard and Others

Abstract

Frank Jackson’s 1982 thought experiment, “Mary’s Room,” illustrates the philosophical divide between propositional and experiential knowledge. We present a compelling case for the incorporation of lived experience into biomedical research and advocate the integration of AI — particularly large language models (LLMs) such as GPT-4 — to bridge this epistemological gap. When paired with sophisticated natural language processing techniques, LLMs could systematically analyze qualitative data from disconnected electronic health record data. We explore methodologic use cases — including grounded theory and thematic analysis — while addressing the challenges of analytical fidelity and bias reduction with continuous human oversight. We suggest that AI-augmented qualitative research can uncover hidden insights from a multitude of disparate datasets, revealing patient experiences that would otherwise remain inaccessible. This integrated approach could enrich the understanding of health and disease, while ensuring it is as inclusive and reflective of human complexity as the lives it seeks to understand and improve.

DOI: 10.1056/AIp2400567

全文链接：https://ai.nejm.org/doi/full/10.1056/AIp2400567

玛丽走出房间：通过定性和人工智能方法捕捉患者体验

V. Renard 等人

摘要： Frank Jackson在1982年的思想实验“玛丽的房间”揭示了命题知识和体验知识之间的哲学分歧。我们提出了一个令人信服的案例，将生活体验纳入生物医学研究，并主张整合人工智能——特别是大型语言模型（LLMs）如GPT-4——以弥合这一认识论差距。当与复杂的自然语言处理技术配对时，LLMs可以系统地分析来自分散的电子健康记录数据的定性数据。我们探讨了方法论用例——包括基础理论和主题分析——同时解决了在持续的人类监督下减少分析保真度和偏见的挑战。我们认为，人工智能增强的定性研究可以从众多不同的数据集中发现隐藏的洞见，揭示否则无法获得的患者体验。这种综合方法可以丰富我们对健康和疾病的理解，同时确保它尽可能地包容和反映人类复杂性，正如它试图理解和改善的生活一样。

NEJM AI, Volume 1 No. 12 December 2024

译文来自于AI工具Kimi