The NLP in healthcare and life sciences market size to grow from USD 2.2 billion in 2022 to USD 7.2 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 27.1% during the forecast period. Various factors such as rise in the need of predictive analytics technology to reduce risks and improve significant health concerns, and demand for improving EHR data usability are expected to drive the adoption of NLP solutions in healthcare and life sciences market.
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According to Lexalytics, NLP is a computing technique that analyzes text to determine its meaning, enabling computer applications that can communicate effectively with humans. Healthcare providers, pharmaceutical companies, and biotechnology firms all use text analytics and NLP technology to improve patient outcomes, streamline operations, and manage regulatory compliances.
Automated Registry Reporting segment to register the highest CAGR during the forecast period
The segmentation of the NLP in healthcare and life sciences market by the application segment includes sentiment analysis, drug discovery, clinical trial matching, dictation & EMR implications, automated registry reporting, AI chatbots & virtual scribe, and other applications (review management, question answering, spell correction, and email filtration). The automated registry reporting segment is developing rapidly due to many major technological advancements resulting in enhancing the efficiency of the overall industry. Health systems will need to recognize when an ejection percent is recorded as part of a note and preserve each number in a format that can be used by the organization’s analytics platform for automated registry reporting to implement automated reporting.
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Unique Features of the NLP in Healthcare and Life Sciences Market:
One of the most notable effects is the improvement of clinical documentation. NLP technology enable healthcare practitioners to accurately transcribe patient encounters and medical records, eliminating errors and saving time in the process. This directly benefits patient care by ensuring that records are not only complete but also free of mistakes.
Another distinguishing characteristic is its efficiency. NLP excels in extracting valuable insights from massive amounts of unstructured clinical and research data. It can analyse medical texts, research papers, and patient information to detect trends and patterns, thus speeding up medical research and decision-making processes. This capacity is important in a field where data-driven insights might make the difference between life-saving treatments and inefficiency.
NLP significantly improves clinical decision support. In order to make recommendations to healthcare professionals in real time, it can analyse patient data, symptoms, and medical history. Effective treatment regimens can be developed as a result, in addition to more precise diagnoses. Patient outcomes are enhanced as a result, and through improved treatment paths, healthcare expenditures can be decreased.
Pharmacovigilance and drug discovery in the life sciences sector are driven by NLP. It allows pharmaceutical companies to promptly respond to safety concerns by monitoring adverse drug reactions by analysing medical literature and patient complaints. The drug development process is accelerated and novel therapies are brought to market more quickly thanks to NLP’s assistance in discovering prospective drug candidates by analysing large datasets.
Patient interaction is changing as a result of healthcare chatbots that use NLP skills. These chatbots can communicate with patients in real-time, addressing their inquiries, setting up appointments, and even reminding them to take their medications. Patients will adhere to their treatment programmes more faithfully as a result, which not only promotes patient involvement.
Major Highlights of the NLP in Healthcare and Life Sciences Market:
The capacity of NLP to improve clinical documentation is one of its most important effects. Healthcare practitioners may now transcribing patient encounters and medical records with a level of accuracy that was previously unheard of, saving them considerable time and assuring accurate and thorough patient records. By streamlining administrative procedures, this function immediately enhances patient care in healthcare facilities.
Additionally, patient engagement is revolutionised by healthcare chatbots equipped with NLP skills. These chatbots can communicate with patients in real-time, addressing their inquiries, setting up appointments, and even reminding them to take their medications. Patients will adhere to their treatment programmes more faithfully as a result, which not only promotes patient involvement.
NLP is also crucial in clinical decision assistance. NLP makes real-time recommendations to healthcare practitioners by analysing patient data, symptoms, and medical history. This not only helps with more precise diagnosis, but also in developing individualised treatment regimens, resulting in better patient outcomes and cost savings.
Furthermore, NLP’s ability to extract insights from massive amounts of unstructured clinical and research data stands out. It can analyse medical texts, research articles, and patient information quickly, allowing healthcare workers and academics to see trends and patterns. This capability speeds up medical research, promotes data-driven decision-making, and eventually improves patient outcomes.
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Top Key Companies in the NLP in Healthcare and Life Sciences Market:
Some major players in the NLP in healthcare and life sciences market include IBM (US), Microsoft (US), Google (US), AWS (US), IQVIA (US), Oracle (US), Inovalon(US), Dolbey (US), Averbis (Germany), SAS Institute (US), Press Ganey (US), Ellipsis Health (US), Health Fidelity (US), Centene (US), Caption Health (US), Clinithink (US), HPE (US), Oncora Medical (US), Flatiron Health (US), Apixio (US), Forsee Medical (US), Gnani.ai (India), Notable (US), Biofourmis (US), Suki (US), Wave Health Technologies (US), Corti (Denmark), CloudMedx (US), MedlnReal (US), Emtelligent (US). These players have adopted various organic and inorganic growth strategies, such as new product launches, partnerships and collaborations, and mergers and acquisitions, to expand their presence in the global NLP in healthcare and life sciences market.
IBM is a multinational technology corporation offering infrastructure hosting and other technology services. The company operates through five major business segments: cloud and cognitive software, global business services, global technology services, systems, and global financing. With this powerful cloud platform, the company can cater to the requirements of different businesses worldwide. IBM caters to various verticals, including aerospace & defense, education, healthcare, oil & gas, automotive, electronics, insurance, retail & consumer products, banking & finance, energy & utility, life sciences, telecommunication, media & entertainment, chemical, government, manufacturing, travel & transportation, construction, and metals & mining.
In the NLP in the healthcare and life sciences market, the company offers IBM Watson Health. IBM launched the new Watson Health business unit to help patients, physicians, researchers, and insurers use data to achieve better health and wellness for all. IBM Watson Health’s importance is growing due to its voracious appetite for academic literature and its growing expertise in Clinical Decision Support (CDS) for precision. Large and growing enterprises support the use of Watson in the healthcare industry.
Microsoft is a prominent leader in the globe and provides software products along with diverse licensing suites. The company develops and supports software, services, devices, and solutions. Its product offerings include Operating Systems (OS), cross-device productivity applications, server applications, business solution applications, desktop and server management tools, software development tools, and video games. The company also designs, manufactures, and sells devices, such as PCs, tablets, gaming and entertainment consoles, other intelligent devices, and related accessories. It offers a range of services, which includes solution support, consulting services, and cloud-based solutions that provide customers with software, services, platforms, and content. The company also offers online advertising. It is a global leader in building analytics platforms and provides production services for the AI-infused intelligent cloud.
In the NLP in the healthcare and life sciences market, Microsoft provides innovative solutions and services that empower organizations to accomplish in the healthcare sector. It offers the Healthcare Bot service, which is a Software as a Service (SaaS) solution. The Health Bot Service implements NLP and AI technologies to understand the users’ intent and provide accurate information. The company’s partners use the Microsoft Health Bot Service to build health Bot instances that address a wide range of healthcare-specific use cases.
Amazon Web Services (AWS) is a subsidiary of Amazon and primarily offers cloud computing services in the form of web services. It offers a wide range of products and services to customers present in 190 countries. AWS’ product portfolio comprises segments, such as compute, storage, database, migration, network, and content delivery, developer tools, management tools, media services, ML, and analytics. The solutions segment offers websites and web apps, mobile services, back-up, storage and archive, financial services, and digital media. The company caters to various industry verticals, including media and entertainment, automotive, education, BFSI, game tech, government, healthcare, and life sciences, manufacturing, retail, telecommunications, oil and gas, and power utilities.
In the NLP in healthcare & life sciences market, AWS offers Amazon Comprehend Medical a HIPAA-eligible NLP service that can quickly and accurately extract information such as medical conditions, medications, dosages, tests, treatments and procedures, and protected health information while retaining the context of the information. An important feature behind Amazon Comprehend Medical is that users can use it with a simple API call and users don’t have to be a machine learning practitioner to take advantage of it. Amazon Comprehend Medical comes with several built-in features, such as identifying tests, treatments, and procedures as well as many other identities.
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