Tamang Suzanne, Humbert-Droz Marie, Gianfrancesco Milena, Izadi Zara, Schmajuk Gabriela, Yazdany Jinoos
Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, CA, United States.
Department of Veterans Affairs, Office of Mental Health and Suicide Prevention, Program Evaluation Resource Center, Palo Alto, CA, United States.
JMIR Med Inform. 2023 Jan 3;11:e37805. doi: 10.2196/37805.
Experts have noted a concerning gap between clinical natural language processing (NLP) research and real-world applications, such as clinical decision support. To help address this gap, in this viewpoint, we enumerate a set of practical considerations for developing an NLP system to support real-world clinical needs and improve health outcomes. They include determining (1) the readiness of the data and compute resources for NLP, (2) the organizational incentives to use and maintain the NLP systems, and (3) the feasibility of implementation and continued monitoring. These considerations are intended to benefit the design of future clinical NLP projects and can be applied across a variety of settings, including large health systems or smaller clinical practices that have adopted electronic medical records in the United States and globally.
专家们已经注意到临床自然语言处理（NLP）研究与实际应用（如临床决策支持）之间存在令人担忧的差距。为了帮助弥合这一差距，在本观点文章中，我们列举了一系列实际考量因素，用于开发支持实际临床需求并改善健康结果的NLP系统。这些因素包括确定：（1）用于NLP的数据和计算资源的准备情况；（2）使用和维护NLP系统的组织激励措施；（3）实施和持续监测的可行性。这些考量旨在有益于未来临床NLP项目的设计，并可应用于各种环境，包括美国及全球范围内采用电子病历的大型医疗系统或小型临床实践机构。
