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Healthcare Natural Language Processing Expects Steady Growth

Natural language processing is rising in the healthcare industry as organizations seek solutions to process unstructured data.

Source: Thinkstock

- Healthcare natural language processing (NLP) is expected to grow significantly over the next several years as organizations continue to adopt more advanced health IT infrastructure solutions, according to a recent report.

A Transparency Market Research report predicts the global healthcare NLP market will be worth $4.3 billion by 2024, growing significantly from the $936 million reported in 2015. Between 2016 and 2024, the market is projected to rise at a CAGR of 18.8 percent.

According to the report, meaningful comprehension of unstructured data is the main reason why healthcare organizations are interested in NLP. The overall adoption of more advanced health IT infrastructure technology is expected to significantly boost the market.

NLP is a type of artificial intelligence designed to learn and recognize speech to derive meaning from natural human language. NLP can learn and generate intelligent text as well as use natural language for data and analytic purposes.

“The demand for NLP technology is expected to surge in the coming years as it is being used as a strategic tool to derive meaningful comprehensive of clinical informatics for effective outcomes by the healthcare industry,” the report stated. “Used as a part of artificial intelligence systems, applications of NLP technologies are being deployed for predictive analysis and clinical decision support systems.”

The NLP market is divided into several segments: interactive voice response and speech analytics technologies, optical character recognition (OCR), automatic coding, text analytics, and pattern and image recognition. 

Infrastructure systems are accumulating massive amounts of raw data that can’t be converted without an artificial intelligence solution to sort through and process it.

Healthcare organizations are beginning to adopt NLP solutions to help complete more accurate electronic health records (EHRs) by translating free text into structured data. Many organizations are unable to use free text data because they do not have a big data solution that can understand and make connections with the unstructured text data.

NLP can potentially make clinicians’ documentation process easier by allowing them to dictate their notes while interacting with a patient, rather than putting in the data themselves.

Streamlining healthcare processes with NLP is also expected to reduce overall costs, the report stated. NLP changes the documentation process, making record keeping more efficient. This automation is expected to be one of the main features driving NLP growth over the next several years.

The improved documentation NLP through automation offers reduced chances of clerical error, reducing potential financial losses.

While NLP is generally looked at as a certain future for many healthcare organizations, adoption rates may be slow through the next several years due to exhaustive medical language. The report found that healthcare NLP adoption may lag slightly behind other industries because of medical vocabulary and abbreviations.

Situations when clinicians refer to the same abbreviations in multiple scenarios can lead to mistakes, which can complicate the analytics process and hinder the performance of an NLP system.

The Transparency Market Research report also identified several of the top vendors in healthcare NLP, including IBM, Apple, and Microsoft. These vendors continue to invest and research NLP technologies for the healthcare vertical. They are expected to focus on delivering text mining, advanced analytics, and cognitive intelligence for efficient healthcare data handling.

Several other reports have predicted a similar rate of growth for NLP. Last October ReportsnReports predicted that the NLP market would grow due to the huge surge in clinical data, the increased use of connected devices, and evolving consumer needs.

ReportsnReports added that pattern and image recognition would experience the highest compound annual growth because it is helpful in detecting images, pattern analysis, and gesture recognition, which is used for numerical data and text data.

 

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