Practical Considerations for Developing Clinical Natural Language Processing Systems for Population Health Management and Measurement.

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. ©Suzanne Tamang, Marie Humbert-Droz, Milena Gianfrancesco, Zara Izadi, Gabriela Schmajuk, Jinoos Yazdany. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 03.01.2023.

Citation

Suzanne Tamang, Marie Humbert-Droz, Milena Gianfrancesco, Zara Izadi, Gabriela Schmajuk, Jinoos Yazdany. Practical Considerations for Developing Clinical Natural Language Processing Systems for Population Health Management and Measurement. JMIR medical informatics. 2023 Jan 03;11:e37805

PMID: 36595345

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