Natural Language Automation
Auto-Coding and Natural Language Processing
Executive summary
Without changes to current coding technology and processes, ICD-10 adoption will be very difficult for providers to absorb, due to the added complexity and coding overhead of
ICD-10.
Automated coding, based on NLP, can help reduce the coding burden.
Richard Wolniewicz, PhD, Director,
NLP Advanced Technology
3M Health Information Systems
Natural Language Processing (NLP) promises to reduce costs and improve quality for healthcare providers. By processing text directly with computer applications, an organization can leverage the wealth of available patient information in clinical documentation to improve communication between caregivers, reduce the cost of working with clinical documentation, and automate the coding and documentation improvement processes. Where other applications of technology often require caregivers to change their existing, proven processes to accommodate the technology, NLP allows applications to work with the most valuable form of clinical communication: the clinical narrative.
This paper introduces and describes NLP—also referred to as computational linguistics or “text mining”—from the healthcare perspective, and particularly addresses the technology in the context of auto-coding. There is a need to demystify NLP and improve expectations for it, because today’s healthcare organization can clearly benefit from this powerful tool.
Without changes to current coding technology and processes, ICD-10 adoption will be very difficult for providers to absorb, due to the added complexity and coding overhead of ICD-10. Automated coding, based on NLP, can help reduce the coding burden.
However, confusion still exists about what NLP is and what it can and cannot do, so it is vital that healthcare organizations and their technology leaders understand the NLP questions—and the answers—that other industries have addressed.
At the core of many NLP
Executive summary
Without changes to current coding technology and processes, ICD-10 adoption will be very difficult for providers to absorb, due to the added complexity and coding overhead of
ICD-10.
Automated coding, based on NLP, can help reduce the coding burden.
Richard Wolniewicz, PhD, Director,
NLP Advanced Technology
3M Health Information Systems
Natural Language Processing (NLP) promises to reduce costs and improve quality for healthcare providers. By processing text directly with computer applications, an organization can leverage the wealth of available patient information in clinical documentation to improve communication between caregivers, reduce the cost of working with clinical documentation, and automate the coding and documentation improvement processes. Where other applications of technology often require caregivers to change their existing, proven processes to accommodate the technology, NLP allows applications to work with the most valuable form of clinical communication: the clinical narrative.
This paper introduces and describes NLP—also referred to as computational linguistics or “text mining”—from the healthcare perspective, and particularly addresses the technology in the context of auto-coding. There is a need to demystify NLP and improve expectations for it, because today’s healthcare organization can clearly benefit from this powerful tool.
Without changes to current coding technology and processes, ICD-10 adoption will be very difficult for providers to absorb, due to the added complexity and coding overhead of ICD-10. Automated coding, based on NLP, can help reduce the coding burden.
However, confusion still exists about what NLP is and what it can and cannot do, so it is vital that healthcare organizations and their technology leaders understand the NLP questions—and the answers—that other industries have addressed.
At the core of many NLP