Kirsten Peremore
Natural language processing is a portion of artificial intelligence that allows computers to understand and interpret human language. With approximately 80% of medical data being unstructured, manually analyzing data can be time-consuming and error-prone. Natural language processing, or NLP, helps healthcare organizations extract insights from datasets. This is possible through the use of machine learning and linguistic rules, which make functions like the acceleration of data analysis and speech-to-text dictation possible.
Levels of NLP and how it is applied in healthcare
Phonological analysis
The most basic level of NLP, phonological analysis, focuses on the patterns of sound in language. It’s not as common in healthcare organizations but can be beneficial as a foundation for speech recognition technology. It supports speech-to-text systems that convert spoken language into written text.
Morphological analysis
This type of analysis consists of analyzing words and their constituent parts, such as roots and prefixes, to understand their meanings. It can be used to help identify complex medical terms and their variations to extract information from clinical notes and patient records.
Lexical analysis
Also known as tokenization, the process breaks down text into individual words or tokens. Lexical analysis helps extract specific medical terms and concepts from unstructured data, such as patient symptoms or medical names, which are then used to populate electronic health records (EHRs).
Syntactic analysis
Syntactic analysis involves analyzing the grammatical structure of sentences to understand how words relate to each other. It helps in identifying relationships between different elements in clinical notes, like linking symptoms to diagnoses or treatments.
Semantic analysis
The level focuses on understanding the meaning of text, including concepts and context. It helps in extracting meaningful insights from clinical data like identifying patterns in patient histories or detecting health risks. Semantic analysis can help healthcare providers make informed decisions based on fully fleshed-out patient profiles.
Pragmatic analysis
This level involves understanding the context in which language is used based on factors like the speaker's intent and audience perspective. It supports effective communication between patients and providers by interpreting the context of patient queries or concerns to create more personalized and empathetic responses.
The contribution of machine learning and linguistic rules
Machine learning algorithms allow for NLP systems to learn from large amounts of data. These models operate by being trained on large datasets of clinical notes and medical records to extract specific medical terms, diagnose conditions, or help predict patient outcomes.
Linguistic rules, on the other hand, provide NLP systems with the ability to understand the nuances of language, including syntax, semantics, and pragmatics. These rules ensure that NLP algorithms can accurately interpret the context and meaning of medical texts.
According to a 2020 study published in the Journal of the American Medical Association, “NLP provides a reliable, valid, and efficient tool to quantify communication between healthcare professionals and patient family members.”
Combining these rules helps extract information from HIPAA compliant emails or text messages and integrate the data into EHRs. It supports the rapid collection of patient data from overcrowded inboxes in a way that does not rely on manual data collection. In turn, staff benefit from the automation of administrative tasks.
FAQs
Can NLP be used for review management and sentiment analysis in healthcare?
Yes, NLP helps monitor and manage online reviews, analyze patient sentiments, and understand customer attitudes.
Is NLP used in telemedicine for healthcare communications?
AI-powered chatbots equipped with NLP capabilities can handle preliminary patient questioning and prepare records for further review by healthcare professionals.
How does NLP help in clinical trial management?
It analyzes patient records to identify those who meet trial criteria, speeding up recruitment and ensuring that trials are matched with the right patients.
