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

Area of Science:

• Health Informatics
• Artificial Intelligence in Medicine
• Clinical Natural Language Processing

Background:

• A significant gap exists between clinical natural language processing (NLP) research and its real-world implementation.
• This gap hinders the adoption of NLP for critical applications like clinical decision support.

Purpose of the Study:

• To outline practical considerations for developing clinical NLP systems.
• To bridge the gap between NLP research and practical clinical applications.
• To improve health outcomes through better NLP system design.

Main Methods:

• This viewpoint enumerates key factors for successful clinical NLP implementation.
• Considerations include data and compute resource readiness.
• Organizational incentives and implementation feasibility are also discussed.

Main Results:

• Identified critical considerations for developing and deploying clinical NLP systems.
• Highlighted the importance of data readiness, organizational buy-in, and ongoing monitoring.
• Provided a framework applicable to diverse healthcare settings.

Conclusions:

• Addressing practical challenges is essential for translating clinical NLP research into impactful applications.
• A systematic approach considering data, organization, and implementation will enhance NLP's role in healthcare.
• These considerations can guide future clinical NLP projects globally.