Natural Language Processing (NLP) in Healthcare and Life Sciences Market, By Component Type (Technology (Interactive Voice Response, Optical Character Recognition, Pattern and Image Recognition, Auto Coding, Text Analytics, Speech Analytics), and Services), By Application (Drug Discovery, EHRs, Clinical Trial Analytics, Adverse Event Reporting, Others), By Deployment Mode (On-premise, Cloud), and By Region (North America, Latin America, Europe, Asia Pacific, Middle East, and Africa)

Published In : Sep 2023

Code : CMI2798

Pages :180

Formats :

Excel and PDF

Industry : Healthcare IT

Market Challenges And Opportunities

Global Natural Language Processing (NLP) in Healthcare and Life Sciences Market– Drivers

Emerging data operations and increasing usage of smart devices: The healthcare and life sciences industries generate a large amount of data, which is driven by compliance and regulatory requirements, record keeping, and patient care. The growing trend of digitization and data-driven solutions to improve healthcare delivery systems is driving the growth of this market.Therefore, increasing data-driven operations and improved decision-making abilities in physicians due to NLP are major factors driving growth of the market. For instance, in May 2018, Google AI collaborated with the University of California, San Francisco, Stanford Medicine, and the University of Chicago Medicine to integrate machine learning into electronic health records (EHR) in order to achieve scalable and accurate results and develop a data processing pipeline for transforming Electronic Health Records (EHRs) files into a standardized format.

Increasing awareness about connected health and organizations are focused on big data analytics.

Increasing customer awareness about connected health has had a great impact on the adoption of smart devices. The increasing adoption of smart devices is a key factor driving growth of the global natural language processing (NLP) in healthcare and life sciences market. As technology-enabled medical care involves health, digital health, and e-health services.

Major organizations are focused on implementing big data analytics, which provides insight from large data sets and also reduces costs. The data from medical research and the disease population are used to predict and prevent various diseases. For instance, in 2018, eClinicalWorks, a technology-driven company, announced the launch of new cloud-based services for acute care EHR settings and revenue cycle management in the healthcare industry. The platform is designed to create a structured or unified electronic health record with a wide range of capabilities, such as claiming partner of record (CPOR) data, infection control, and referrals management.

Global Natural Language Processing (NLP) in Healthcare and Life Sciences Market– Restraints

High training cost for NLP models: The high training cost for natural language processing models is unaffordable for start-up companies, which is expected to restraint the use of natural language processing models in start-up companies mainly present in lower and middle-income countries. For instance, Synced, a company focusing on reviewing the novel technologies, in April 2020, stated that the Stanford University professor of computer science (emeritus) conducted a survey on the cost for training NLP models. The survey was conducted on Google’s BERT language models, which stated that the approximate cost for training the companies on BERT models is approximately US$ 2.5k–US$ 50k (110 million parameter model). The BERT model is developed by Google and is used by all industries such as automobiles, pharma, and others, for data mining and other purposes. Counterbalance: In order to counterbalance the high costs associated with the NLP models, various methods are available to optimize the training model, which needs to be followed by the companies, so that it will help them in affording the models.