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.(JMIR Med Inform 2023;11:e37805) doi: 10.2196/37805

Automated summary

Experts highlight the gap between clinical NLP research and real-world applications, offering practical considerations for developing NLP systems that can support clinical needs and improve health outcomes. These considerations involve assessing data and compute resources readiness, organizational incentives, and feasibility of implementation. They are beneficial for the design of future clinical NLP projects in various settings, including large health systems and clinical practices with electronic medical records.