10 January 2018
NLP Market

The top three players leading the pack in the global healthcare natural language processing market held a share of 41% in 2015. IBM Corporation, Apple, Inc., and Microsoft Corporation emerged as the leading players due to ongoing investments in research and development of NLP technologies for the healthcare sector. In the coming years, players are expected to focus on delivering text mining, advanced analytics, and cognitive intelligence for efficient handling of healthcare data to stay ahead of the competition, states Transparency Market Research.

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According to the research report, the global healthcare natural language processing market is expected to be worth US$4.3 bn by the end of 2024 as compared to US$936 mn in 2015. During the forecast years of 2016 and 2024, the global market is projected to rise at a CAGR of 18.8%.

North America to Remain Ahead as Region Embraces Technological Advancements

In terms of geography, the global healthcare natural language processing market is segmented into North America, Europe, Asia Pacific, the Middle East and Africa, and South America. Out of these, North America is expected to lead the global market in the coming years due to growing technological advancements and a strong presence of NLP companies in the region. By the end of 2024, the global market is expected to acquire a share of 38.2% in the global market.

On the basis of technology, the global market is segmented into machine translation, automatic summarization, information extraction, and text and voice processing. Analysts state that the machine translation and information extraction collectively held a dominant share in the global market in 2016. However, in the forecast period, text and voice processing segments are projected to exhibit the fastest CAGR of 19.9%.

Meaningful Comprehension of Unstructured Data Boosts Adoption of Healthcare NLP Technologies

The growing adoption of advanced technologies to simplify clinical solutions is anticipated to boost the overall market. The increasing usage of electronic mediums to maintain health records systems has created a humungous amount of raw data. This unstructured accumulation of crucial data about patient information pertaining to diseases, surgical procedures, medications, and other treatments has created a significant need for healthcare natural language processing technologies. The rising usage of electronic systems in public and private hospitals and amongst physicians is anticipated fuel the growth of this market. Healthcare NLP allows conversion of unstructured data into structured, making the documentation process meaningful.

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Streamlining of processes in the healthcare industry by adopting natural language processing technologies are anticipated to bring down the overall costs. The new methods have also eliminated the conventional ways of documentation processes, which, in turn, has made record keeping efficient and effective. Thus, the positive aspects of NLP and growing demand for automation are projected to drive the growth of the overall market. The growing focus of healthcare organizations to improve their clinical documentation by minimizing the chances of incidental errors to reduce financial losses and enhance patient care are estimated to encourage the uptake of healthcare natural language processing technologies.

Poor Understanding of Exhaustive Medical Vocabulary to Hamper Market Growth

The exhaustive medical vocabulary, medical concepts, and abbreviations, which are continuously increasing have created the biggest challenge for the global healthcare natural language processing market. Incorrect understanding of this vocabulary, wherein a same abbreviation can be used in multiple scenarios can lead to ambiguity and blunders. These challenges can complicate the processes of analyzing the data and thus hamper the performance of the NLP systems. The market is also facing $@!#% growth due to the slow penetration of electronic healthcare record systems, which are projected to have a direct impact on the demand for NLP technologies.

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