The Worldwide NLP In Healthcare And Life Sciences Industry Is Expected To Reach $6.8 Billion By 2028

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Dublin, July 01, 2022 (GLOBE NEWSWIRE) -- The 'Global NLP in Healthcare and Life Sciences Market Size, Share & Industry Trends Analysis Report By Component, By Solution Type, By End User, By NLP Type, By Deployment Mode, By Organization Size, By Application, By Regional Outlook and Forecast, 2022 - 2028' report has been added to ResearchAndMarkets.com's offering.
The Global NLP in Healthcare and Life Sciences Market size is expected to reach $6.8 billion by 2028, rising at a market growth of 20.3% CAGR during the forecast period.
Natural Language Processing (NLP) refers to a computer program's ability to comprehend and present data in the form of current human language, speech phrases, and text. In the healthcare industry, NLP is employed in a variety of ways, including improving the quality of care and raising outcomes, as well as automating virtual patient conversational activities. Email filtration, predictive messaging, smart assistance, digital phone calls, and language translations are all examples of where NLP is applied.
Doctors may spend as long as necessary with their patients and give them their undivided attention due to the NLP platform. A number of clinicians prefer printed or typed voice notes. As a result, the NLP platform may be utilized to accurately analyze speech and update data. Unstructured data in real-world data sources like EHRs, patient forums, and other sources make extracting usable insights from the data challenging and time-consuming. This issue is alleviated by AI-powered NLP technology. Pharma companies are using natural language processing (NLP) in drug discovery, text mining EHR data, and utilizing data to produce future insights for commercial advantages, resulting in actionable insights that improve care and efficacy. Furthermore, NLP has a wide range of applications in the pharmaceutical industry, including drug development, clinical trials, regulatory insights, market insights, real-world data, pharmacovigilance, and more.
Natural language processing for life sciences and healthcare sciences is a combination of artificial intelligence, computer science, and computational linguistics that allows computers to understand human speech as it is spoken. Clinicians and researchers can use it to produce, preserve, and utilize a variety of semi-structured and unstructured textual documents. In healthcare and life sciences, high-end NLP technologies for information extraction, automatic voice recognition, machine translation, and dialogue systems are used. NLP is an umbrella term for the process of employing computer algorithms to detect primary components of ordinary language and extract meaning from unstructured spoken or written material. Some NLP efforts aim to pass the Turing test by creating algorithmically-based creatures that can respond to conversations or searches in a human-like manner. Others employ voice recognition technology to try to interpret human speech, such as automated customer service programs.
COVID-19 Impact Analysis

The COVID-19 pandemic is causing havoc in the world. It has inflicted whole economies & businesses and affected the career and personal lives of individuals. As businesses turn their focus away from development prospects and toward implementing extraordinary measures to limit the negative impact of the COVID-19 pandemic, the stress to maintain the revenue levels at pre-COVID levels has become the new normal. Pharmaceutical and healthcare organizations, governments, and the larger scientific community are all attempting to assess the virus's impact and provide speedy, accurate treatments in the ongoing fight against COVID-19. NLP technology, according to a few suppliers in the industry, will enable fast, systematic, and comprehensive insight production from unstructured text.
Market Growth Factors

Need for Analyzing and Extracting Insights from Narrative Text

The need for improved utilization of unstructured data is being driven by a shift in business models and outcome expectations. Traditional health information systems have concentrated on extracting value from the relatively small quantities of structured healthcare data received through clinical channels. However, NLP can extract patient information from unstructured, free-form language and generate actionable data that can be utilized to improve patient care and expedite workflow. NLP systems that are well-designed can assess text-free dictation, recognize situations, and tag the most important clinical data items such as problems, social history, drugs, allergies, and treatments.
Development of Cognitive Computing

Some well-known businesses in the market have made considerable investments in semantic big data analytics and cognitive computing technologies in the healthcare and life sciences industry. NLP offers a wide range of applications in healthcare, from cutting-edge precision medicine applications to the simple task of coding a claim for reimbursement or billing. However, developing algorithms that are smart, accurate, and specific to ground-level concerns in the healthcare and life sciences industries will be critical to the success of deploying this technology. In order for patients to have an accurate record of their health in a language they can comprehend, NLP will have to achieve the dual aims of data abstraction and data presentation. Within the healthcare industry, this enhanced approach is expected to improve physical efficiency while lowering operating expenses.
Market Restraining Factors

High Cost of R&D in NLP

NLP is a technique for processing sequential data such as text, speech, financial data, time series, audio, and video that employs neural networks and deep learning algorithms. The most sophisticated technologies that are laying the groundwork for NLP to acquire momentum in the market are neural networks and deep learning. However, developing these technologies is extremely costly and necessitates a significant investment in both R&D funds and time, which is difficult for small or startup enterprises looking to enter the NLP market in healthcare and life sciences.

Key Topics Covered:
Chapter 1. Market Scope & Methodology
Chapter 2. Market Overview
2.1 Introduction
2.1.1 Overview
2.1.1.1 Market Composition and Scenario
2.2 Key Factors Impacting the Market
2.2.1 Market Drivers
2.2.2 Market Restraints
Chapter 3. Competition Analysis - Global
3.1 Cardinal Matrix
3.2 Recent Industry Wide Strategic Developments
3.2.1 Partnerships, Collaborations and Agreements
3.2.2 Product Launches and Product Expansions
3.2.3 Acquisition and Mergers
3.1 Market Share Analysis, 2020
3.2 Top Winning Strategies
3.2.1 Key Leading Strategies: Percentage Distribution (2018-2022)
3.2.2 Key Strategic Move: (Partnerships, Collaborations and Agreements: 2019, Jan - 2022, Mar) Leading Players
Chapter 4. Global NLP in Healthcare and Life Sciences Market by Component
4.1 Global Solution Market by Region
4.2 Global NLP in Healthcare and Life Sciences Market by Solution Type
4.2.1 Global Clinical Variation Management Market by Region
4.2.2 Global Population Health Management Market by Region
4.2.3 Global Counter Fraud Management Market by Region
4.2.4 Global Others Market by Region
4.3 Global Services Market by Region
Chapter 5. Global NLP in Healthcare and Life Sciences Market by End User
5.1 Global NLP for Physician Market by Region
5.2 Global NLP for Patients Market by Region
5.3 Global NLP for Researchers Market by Region
5.4 Global NLP for Clinical Operators Market by Region
Chapter 6. Global NLP in Healthcare and Life Sciences Market by NLP Type
6.1 Global Rule-based Market by Region
6.2 Global Statistical Market by Region
6.3 Global Hybrid Market by Region
Chapter 7. Global NLP in Healthcare and Life Sciences Market by Deployment Mode
7.1 Global Cloud Market by Region
7.2 Global On-premise Market by Region
Chapter 8. Global NLP in Healthcare and Life Sciences Market by Organization Size
8.1 Global Large Enterprises Market by Region
8.2 Global Small & Medium Enterprises (SMEs) Market by Region
Chapter 9. Global NLP in Healthcare and Life Sciences Market by Application
9.1 Global IVR Market by Region
9.2 Global Summarization & Categorization Market by Region
9.3 Global Reporting & Visualization Market by Region
9.4 Global Pattern & Image Recognition Market by Region
9.5 Global Text & Speech Analytics Market by Region
9.6 Global Predictive Risk Analytics Market by Region
9.7 Global Others Market by Region
Chapter 10. Global NLP in Healthcare and Life Sciences Market by Region
Chapter 11. Company Profiles
11.1 3M Company
11.1.1 Company Overview
11.1.2 Financial Analysis
11.1.3 Segmental and Regional Analysis
11.1.4 Research & Development Expense
11.1.5 Recent strategies and developments:
11.1.5.1 Acquisition and Mergers:
11.2 IBM Corporation
11.2.1 Company Overview
11.2.2 Financial Analysis
11.2.3 Regional & Segmental Analysis
11.2.4 Research & Development Expenses
11.2.5 Recent strategies and developments:
11.2.5.1 Product Launches and Product Expansions:
11.3 Microsoft Corporation
11.3.1 Company Overview
11.3.2 Financial Analysis
11.3.3 Segmental and Regional Analysis
11.3.5 Recent strategies and developments:
11.3.5.1 Partnerships, Collaborations, and Agreements:
11.3.5.2 Product Launches and Product Expansions:
11.3.5.3 Acquisition and Mergers:
11.4 Google LLC
11.4.1 Company Overview
11.4.2 Financial Analysis
11.4.3 Segmental and Regional Analysis
11.4.4 Research & Development Expense
11.4.5 Recent strategies and developments:
11.4.5.1 Partnerships, Collaborations, and Agreements:
11.4.5.2 Product Launches and Product Expansions:
11.5 Amazon Web Services, Inc.
11.5.1 Company Overview
11.5.2 Financial Analysis
11.5.3 Segmental and Regional Analysis
11.5.4 Recent strategies and developments:
11.5.4.1 Product Launches and Product Expansions:
11.6 Cerner Corporation (Oracle Corporation)
11.6.1 Company Overview
11.6.2 Financial Analysis
11.6.3 Regional Analysis
11.6.4 Research & Development Expense
11.6.5 Recent strategies and developments:
11.6.5.1 Partnerships, Collaborations, and Agreements:
11.7 Corti ApS
11.7.1 Company Overview
11.8 Lexalytics, Inc. (InMoment, Inc.)
11.8.1 Company Overview
11.8.2 Recent strategies and developments:
11.8.2.1 Partnerships, Collaborations, and Agreements:
11.9 Health Fidelity, Inc. (Edifecs, Inc.)
11.9.1 Company Overview
11.9.2 Recent strategies and developments:
11.9.2.1 Partnerships, Collaborations, and Agreements:
11.10. Linguamatics (an IQVIA Company)
11.10.1 Company Overview
11.10.2 Financial Analysis
11.10.3 Segmental and Regional Analysis
11.10.4 Recent strategies and developments:
11.10.4.1 Partnerships, Collaborations, and Agreements:

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