- June 28, 2021
- Posted by: Suhith Kumar
- Category: Text Analytics
Languages are not without complexities. Yet, for humans, those complexities are less noticeable. Not only is speaking a language to convey information a repeatable and trained habit, communication can also take the form of signals, images, words and more which further simplifies the process.
The scenario, however, is a lot different for computers. The large volumes of unstructured data, lack of real-world context or intent and an absence of formal rules contribute to the challenge for computers to understand and process human language.
Natural language processing (NLP solutions) is a branch of artificial intelligence (AI) that enables computers to understand human language. The goal of NLP is to design systems that uncover the meaning of text and perform tasks such as topic classification, translation, grammar checking and more.
The objective of NLP is to make the complex, varied and ambiguous human language easy to understand for machines. The technology works by applying linguistics to analyze grammar and meanings of each word before leveraging algorithms to create systems capable of carrying out the various tasks.
As per a report, the global market size of natural language processing is forecast to reach USD 35.1 billion by the year 2026 from USD 11.6 billion in 2020, at a compound annual growth rate (CAGR) of 20.3 percent during the forecast period. Increased adoption of cloud-based solutions, rising technological investments in healthcare, ever-increasing use of smart devices and more are contributing to the growth of the NLP market.
Natural Language Processing In Healthcare
In recent years, adoption of NLP in the healthcare and life sciences sector has grown rapidly. A large volume of data collected—estimated to be 80 percent—is in an unstructured format and of bad quality. This contributes to the challenge in extracting and analyzing the data to enhance healthcare processes.
Unstructured data is unusable and consumes time and effort if the process of structuring it is manual. Another downside is that it hinders effective decision-making without the help of analytics.
NLP, as an automated process, helps uncover the gold mine of information hidden within unstructured data and ultimately contributes to enhanced patient care.
The global NLP market size for the healthcare and life sciences industry is forecast to reach USD 3.7 billion by the year 2025, more than double the current value of USD 1.5 billion (2020).
An increased use of predictive analytics to improve patient outcomes and an ever-growing demand for enhancing electronic health record data usability to improve patient care are among the key factors driving the growth of NLP adoption in healthcare.
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Current NLP Use Cases In Healthcare
The technology is helping the healthcare sector in myriad ways. At a fundamental level, it comprehends human speech and extracts meaning. NLP also enables physicians to use key information for decision-making and analytics by unlocking the unstructured data from documents and databases and mapping out the basic values and concepts.
Apart from these, NLP use cases in healthcare include:
- Speech recognition
- Data mining
- Clinical documentation
- Automated registry reporting
Natural language processing helps transcribe large volumes of clinical notes from speech to text, thus reducing the physicians’ task of dictating notes and also helping avoid duplication.
NLP helps in mining the unstructured patient data, which in turn enables organizations to reduce the subjectivity in decision-making and enhancing the level of patient care.
The AI-based technology enables physicians to focus more on patient care by freeing them of the manual and complex structure of electronic medical records (EMR).
Natural language processing tools could in the future be applied to public data sets and social media websites to identify social determinants of health and the efficacy of wellness programs and other initiatives.
Automated Registry Reporting
Reporting has troubled the IT systems in the healthcare industry when measures like ejection fraction have not been stored as discrete values. For automated reporting, on the other hand, systems must determine when an ejection fraction is recorded as part of a note. It must also save the values in forms that can be leveraged by analytics platforms for the purpose of regulatory reporting.
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Emerging use cases of NLP in healthcare
Some of the NLP use cases in healthcare that promise to have an immediate impact are:
- Clinical decision support
- Clinical trial matching
- Risk adjustment
Clinical Decision Support
NLP in combination with machine learning can enable physicians to make better clinical decisions. Some areas of healthcare, like medical errors, require better ways of surveillance. Also, natural language processing is being leveraged to help healthcare professionals perform diagnosis and check for symptoms.
Clinical Trial Matching
Current advancements indicate that NLP does have the potential to automate the process of trial matching. A few companies are already using NLP analytics software to resolve the challenges in trial matching, which is an essential and existing use case of natural language processing.
NLP has the capability to assign risk factors to patients and provide a score to predict healthcare costs. Risk adjustment models such as hierarchical condition category coding will be increasingly used.
Next-gen Use Cases Of NLP In Healthcare
Some of the NLP use cases that could be realized in the near-future are:
- Population surveillance
- Ambient virtual scribe
- Computational phenotyping
Current administrative databases do not provide the granularity necessary to implement population surveillance and identify key socio-cultural differences. Applying natural language processing to electronic medical records can help identify a subset of an ethnic/racial group to map and document the health disparities.
Ambient Virtual Scribe
NLP could, in combination with artificial intelligence, completely revolutionize the process of clinical documentation. With the help of speech recognition software, human scribes could be completely removed from clinical documentation.
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Natural language processing has the potential to help physicians in phenotyping patients to perform analysis. NLP will enable phenotypes to be defined by a patient’s existing condition.
The wide-ranging use cases of NLP promise to revolutionize the industry by creating exciting, new opportunities in healthcare delivery and patient experience. NLP as a technology will get smarter in the years to come and enable providers to positively impact health outcomes.
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