Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
UCSF School of Medicine, San Francisco, California, USA.
J Palliat Med. 2024 Apr;27(4):447-450. doi: 10.1089/jpm.2023.0528. Epub 2024 Feb 6.
Despite its growth as a clinical activity and research topic, the complex dynamic nature of advance care planning (ACP) has posed serious challenges for researchers hoping to quantitatively measure it. Methods for measurement have traditionally depended on lengthy manual chart abstractions or static documents (e.g., advance directive forms) even though completion of such documents is only one aspect of ACP. Natural language processing (NLP), in the form of an assisted electronic health record (EHR) review, is a technological advancement that may help researchers better measure ACP activity. In this article, we aim to show how NLP-assisted EHR review supports more accurate and robust measurement of ACP. We do so by presenting three example applications that illustrate how using NLP for this purpose supports (1) measurement in research, (2) detailed insights into ACP in quality improvement, and (3) identification of current limitations of ACP in clinical settings.
尽管作为一种临床活动和研究课题，预先医疗指示计划（ACP）的复杂动态性质对希望对其进行定量测量的研究人员提出了严峻挑战。测量方法传统上依赖于冗长的手动图表抽象或静态文档（例如，预先指示表格），尽管完成此类文档只是 ACP 的一个方面。自然语言处理（NLP）以辅助电子健康记录（EHR）审查的形式，是一项技术进步，可能有助于研究人员更好地衡量 ACP 活动。在本文中，我们旨在展示 NLP 辅助 EHR 审查如何支持更准确和强大的 ACP 测量。我们通过介绍三个示例应用程序来实现这一点，这些应用程序说明了出于此目的使用 NLP 支持（1）研究中的测量，（2）质量改进中对 ACP 的详细了解，以及（3）在临床环境中确定当前 ACP 的局限性。
