市場調查報告書
商品編碼
1470926
醫療保健和生命科學領域的 NLP 市場:按組件、按 NLP 類型、按 NLP 方法、按應用、按部署型態、按組織規模、按最終用戶 - 2024 年至 2030 年全球預測NLP in Healthcare & Life Sciences Market by Component (Services, Solution), NLP Type (Hybrid, Neural, Rule-Based), NLP Technique, Application, Deployment Mode, Organization Size, End-User - Global Forecast 2024-2030 |
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醫療保健和生命科學領域的 NLP 市場規模預計到 2023 年為 34 億美元,到 2024 年將達到 42.1 億美元,預計到 2030 年將達到 162 億美元,複合年成長率為 24.98%。
醫療保健和生命科學領域的自然語言處理 (NLP) 是一個跨學科領域,它利用計算語言學、人工智慧和資料科學,使設備能夠以有意義且有用的方式理解、解釋和生成人類語言。在醫療保健領域,我們分析臨床記錄、電子健康記錄(EHR)、研究文章、病患調查等,以改善病患治療結果、加強決策並提高效率。醫療保健資料的數位化不斷提高、對個人化醫療保健服務的需求不斷成長以及人工智慧和機器學習的進步正在推動市場需求。然而,與患者資料相關的隱私和安全問題以及缺乏高品質和多樣化的註釋資料集可能會阻礙市場成長。也就是說,醫療保健和生命科學解決方案中 NLP 的不斷進步以及患者照護預測分析的改進為 NLP 在醫療保健和生命科學市場的成長創造了潛在的機會。
主要市場統計 | |
---|---|
基準年[2023] | 34億美元 |
預測年份 [2024] | 42.1億美元 |
預測年份 [2030] | 162億美元 |
複合年成長率(%) | 24.98% |
使用 NLP 解決方案自動從組件醫療文件中提取見解並實現高級資料分析激增
託管服務是指將 NLP 功能外包給專業供應商,由他們負責 NLP 系統的持續管理和維護。這包括資料處理、系統維護以及使用最新醫學術語更新 NLP 模型等任務,使醫療機構能夠維護最新、準確的資訊,而無需內部技術專業知識即可訪問。專業服務為醫療保健和生命科學組織提供專業的 NLP 諮詢、支援和實施服務。在醫療保健和生命科學 NLP 中,解決方案是滿足該領域特定需求的軟體工具、平台和產品包。臨床變異管理使用 NLP 來識別和減少醫療保健服務的不一致情況。 NLP 提供對實踐變化的洞察,根據循證指南促進標準化護理,並透過分析臨床文件和患者結果來減少不必要的成本。詐欺管理利用 NLP 來檢測和防止醫療保健系統中的詐欺。透過審查保險申請和申請資訊等非結構化資料,NLP 可以幫助識別可能表明詐欺的模式和異常,從而保護財務資源並確保醫療保健業務的完整性。 NLP 透過挖掘臨床資料來支持人口健康管理,以識別公共衛生趨勢並按風險對患者群體進行分層。風險調整和申請管理中的 NLP 可確保醫療服務的準確編碼和記錄、最佳化報銷流程並支援合規性。 NLP 透過支援更準確的風險評分並自動從醫療記錄中提取相關資訊,簡化了申請到付款的生命週期。
NLP技術:擴大採用影像和模式識別技術來監測治療進展
影像和模式識別是 NLP 和機器學習的一個高級領域,其中的演算法旨在識別影像中的模式和視覺線索。這項技術在醫療保健和生命科學領域至關重要,這些領域需要分析 X 光、MRI 掃描和病理切片等醫學影像。命名實體識別是一種 NLP 技術,用於識別文本中的關鍵資訊並將其分類為預先定義的類別,包括藥物名稱、醫療狀況、劑量、生物過程等。在醫療保健和生命科學領域,NER 可以處理大量非結構化臨床記錄、研究論文和報告並提取相關實體。光學字元辨識 (OCR) 在數位化病患記錄、處方箋和研究資料方面非常有效,使資訊更易於存取並減少手動資料輸入錯誤。這種數位化對於將歷史病歷整合到現代電子病歷系統中至關重要。情緒分析是確定詞語背後的情緒和情緒基調的計算過程。它用於醫療保健和生命科學領域,用於衡量患者滿意度、分析調查反饋以及監控社交媒體有關公眾對健康相關主題的看法。這種 NLP 技術可以深入了解患者的經驗和情緒,並有助於評估醫療保健的品質。文字分類將文字資料分類為有組織的群組。在醫療保健和生命科學領域,這意味著將臨床文件分為不同類型,例如實驗室報告、放射學報告和臨床記錄。分類可幫助您有效地從醫學文獻中檢索訊息,並透過適當處理敏感資訊來支持合規性。主題建模有助於分析醫學文獻、病患回饋和線上健康論壇。主題建模有助於識別疾病爆發、了解公共衛生問題並透過揭示非結構化資料中的共同主題和趨勢來指導研究和發展。
部署模式:雲端基礎的自然語言處理(NLP)部署由於投資較少而廣泛採用。
自然語言處理(NLP)技術在醫療保健和生命科學領域的雲端基礎的部署是指將NLP應用程式託管在由外部雲端服務供應商管理的遠端伺服器上。這種部署模式提供了可擴展的運算資源,使其適合處理醫療記錄和科學文獻中常見的大量資料。此外,雲端上 NLP 解決方案可以輕鬆存取高級分析和機器學習模型,而無需對基礎設施進行大量前期投資,從而使醫療保健組織能夠降低成本、定期獲得軟體更新和研究,您可以從團隊之間加強協作中受益。本地直接在醫療保健或生命科學組織的本地基礎設施內安裝和運行 NLP 系統。這種方法可讓您將敏感的患者資訊和專有研究資料保留在組織的防火牆內,使您能夠更好地控制資料安全並遵守 HIPAA 和 GDPR 等法規要求。本地解決方案可以在硬體和軟體許可證方面擁有更高的初始投資,但允許完全客製化並與您現有的IT 生態系統整合,並且通常根據您的特定組織的需求進行客製化,以實現最佳化的性能。
最終用戶:醫療保健和生命科學醫生擴大自然語言處理 (NLP) 的使用
護理師和行政人員等臨床操作人員正在從自然語言處理 (NLP) 中受益,它可以增強工作流程並減輕管理負擔。 NLP 可以自動從患者管理系統的臨床記錄中提取相關資訊,透過理解和處理患者請求來改善調度,並促進即時文件支援。 NLP 技術透過提供更容易存取和互動的方式來管理患者的健康,從而為患者提供幫助。聊天機器人和虛擬助理可以解釋和回覆患者的詢問,並提供個人化的建議和支援。醫師利用 NLP 來簡化和增強臨床決策。 NLP工具可以處理和摘要大量非結構化臨床資料,例如患者病歷、檢查結果和放射學報告,為醫生提供簡潔且相關的資訊並節省時間。醫療保健和生命科學研究人員使用 NLP 從大量生物醫學文獻和臨床報告中獲取見解。 NLP 透過快速擷取和綜合已發表的研究結果中的發現,促進系統性文獻綜述。它還可以透過篩選電子健康記錄來找到符合特定標準的候選人,從而幫助確定臨床試驗的患者群體。
區域洞察
醫療保健和生命科學市場中的自然語言處理 (NLP) 在美洲不斷成長。這是因為美洲的投資主要針對人工智慧主導的新興企業以及大型科技公司和頂尖大學的研究舉措。 《一般資料保護規範》(GDPR) 法規將推動 NLP 技術中資料安全性和隱私性的提高,影響購買行為,有利於合規解決方案、策略合作夥伴關係和人工智慧研究資金籌措,並影響EMEA 的購買行為,進一步促進該地區的市場成長。同時,在中東國家,由於需要以多種語言提供醫療服務並改善偏遠地區的可近性,自然語言處理在醫療保健領域的應用正在大力推動。在亞太地區,主要市場參與者正在大力投資人工智慧醫療保健服務,並專注於迎合大量人群的解決方案,轉向行動醫療應用程式和線上醫療保健服務。此外,在亞太地區,各國政府正在積極推動人工智慧和自然語言處理的研究,並致力於透過基於技術的醫療保健解決方案來應對老齡化社會問題。
FPNV定位矩陣
FPNV 定位矩陣對於評估醫療保健和生命科學領域的 NLP 市場至關重要。我們檢視與業務策略和產品滿意度相關的關鍵指標,以對供應商進行全面評估。這種深入的分析使用戶能夠根據自己的要求做出明智的決策。根據評估,供應商被分為四個成功程度不同的像限:前沿(F)、探路者(P)、利基(N)和重要(V)。
市場佔有率分析
市場佔有率分析是一種綜合工具,可對醫療保健和生命科學 NLP 市場中供應商的現狀進行深入而深入的研究。全面比較和分析供應商在整體收益、基本客群和其他關鍵指標方面的貢獻,以便更好地了解公司的績效及其在爭奪市場佔有率時面臨的挑戰。此外,該分析還提供了對該行業競爭特徵的寶貴見解,包括在研究基準年觀察到的累積、分散主導地位和合併特徵等因素。詳細程度的提高使供應商能夠做出更明智的決策並制定有效的策略,從而在市場上獲得競爭優勢。
1. 市場滲透率:提供有關主要企業所服務的市場的全面資訊。
2. 市場開拓:我們深入研究利潤豐厚的新興市場,並分析其在成熟細分市場的滲透率。
3. 市場多元化:提供有關新產品發布、開拓地區、最新發展和投資的詳細資訊。
4. 競爭評估和情報:對主要企業的市場佔有率、策略、產品、認證、監管狀況、專利狀況和製造能力進行全面評估。
5. 產品開發與創新:提供對未來技術、研發活動和突破性產品開發的見解。
1. 醫療保健和生命科學領域的 NLP 市場規模和預測是多少?
2.在醫療保健和生命科學市場的 NLP 預測期內,需要考慮投資哪些產品、細分市場、應用程式和領域?
3. 醫療保健和生命科學領域的NLP市場的技術趨勢和法規結構是什麼?
4.醫療保健和生命科學NLP市場主要供應商的市場佔有率是多少?
5. 進入醫療保健和生命科學領域的 NLP 市場的適當型態或策略手段是什麼?
[187 Pages Report] The NLP in Healthcare & Life Sciences Market size was estimated at USD 3.40 billion in 2023 and expected to reach USD 4.21 billion in 2024, at a CAGR 24.98% to reach USD 16.20 billion by 2030.
Natural language processing (NLP) in Healthcare and Life Sciences is an interdisciplinary field that leverages computational linguistics, artificial intelligence, and data science to enable devices to understand, interpret, and generate human language in a meaningful and useful way. In healthcare, this involves the analysis of clinical notes, electronic health records (EHRs), research papers, patient surveys, and more, aiming to improve patient outcomes, enhance decision-making, and drive efficiencies. The increasing digitalization of healthcare data, growing demand for personalized healthcare services, and advancements in AI and machine learning increase the market demand. However, the privacy and security concerns related to patient data and the shortage of high-quality and diverse annotated datasets may impede the market's growth. Nevertheless, the ongoing advancements in NLP for healthcare and life sciences solutions and improvement in predictive analytics for patient care are expected to create potential opportunities for the growth of the NLP in the healthcare and life sciences market.
KEY MARKET STATISTICS | |
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Base Year [2023] | USD 3.40 billion |
Estimated Year [2024] | USD 4.21 billion |
Forecast Year [2030] | USD 16.20 billion |
CAGR (%) | 24.98% |
Component: Burgeoning utilization of NLP solution as it can automate the extraction of insights from medical texts, enable advanced data analytics
Managed services refer to outsourcing NLP functions to specialized vendors responsible for the continuous management and upkeep of NLP systems. This includes tasks such as data processing, system maintenance, and updating NLP models with the latest medical terminologies, ensuring healthcare organizations have access to up-to-date and accurate information without needing in-house technical expertise. Professional services involve expert consulting, support, and implementation services in NLP provided by professionals to healthcare and life sciences organizations. A solution in the context of NLP in healthcare & life sciences refers to a package of software tools, platforms, or products that address specific needs within the field. Clinical variation management utilizes NLP to identify and reduce inconsistencies in healthcare delivery. NLP draws insights into practice variations, promoting standardized care that aligns with evidence-based guidelines and reduces unnecessary costs by analyzing clinical documentation and patient outcomes. Counter-fraud management leverages NLP to detect and prevent fraudulent activities in healthcare systems. NLP helps identify patterns and anomalies that may indicate fraudulent behavior by scrutinizing unstructured data such as insurance claims and billing information, thereby protecting financial resources and maintaining integrity within healthcare operations. Population health management is supported by NLP by mining clinical data to identify public health trends and stratify patient populations by risk. NLP in risk adjustment & claim management ensures accurate coding and documentation for healthcare services, which optimizes reimbursement processes and supports compliance. NLP aids in more precise risk scoring and streamlines the claims lifecycle from submission to settlement by automating extracting relevant information from medical records.
NLP Technique: Growing adoption of image and pattern recognition technique as it monitors treatment progress
Image and pattern recognition is an advanced area of NLP and machine learning where algorithms are designed to identify patterns and visual cues within images. This technique is pivotal in healthcare and life sciences for analyzing medical imagery such as X-rays, MRI scans, and pathology slides. Named entity recognition is an NLP method used to identify and classify key information in text into predefined categories, including the names of drugs, medical conditions, dosages, and biological processes. In Healthcare and life Sciences, NER can process vast amounts of unstructured clinical notes, research papers, and reports to extract relevant entities. Optical character recognition (OCR) is invaluable for digitizing patient records, prescriptions, and research material, making the information more accessible and reducing manual data entry errors. This digitization is crucial for integrating historical health records into modern electronic health record systems. Sentiment analysis is the determining the sentiment or emotional tone behind words by computational process. It is used in healthcare and life sciences to gauge patient satisfaction, analyze survey feedback, and monitor social media for public perception of health-related topics. This NLP technique can provide insights into patient experiences and emotions, which is helpful in quality of care assessments. Text Classification involves categorizing textual data into organized groups. In Healthcare and Life Sciences, this could mean sorting clinical documents into various types, such as lab reports, radiology reports, clinical notes, etc. Categorization helps efficiently retrieve information from medical literature and supports compliance by properly handling sensitive information. Topic Modeling is useful in analyzing medical literature, patient feedback, and online health forums. Topic Modeling aids in identifying disease outbreaks, understanding public health concerns, and guiding research and development by uncovering prevailing themes and trends in unstructured data.
Deployment Mode: Significant adoption of Cloud-based deployment of Natural Language Processing (NLP) owing to the requirement of less investment
Cloud-based deployment of natural language processing (NLP) technologies in healthcare and life sciences refers to hosting NLP applications on remote servers, which external cloud service providers manage. This deployment mode offers scalable computing resources, making it suitable for handling large volumes of data commonly found in medical records and scientific literature. Moreover, on-cloud NLP solutions provide easy access to advanced analytics and machine learning models without significant upfront investment in infrastructure, enabling healthcare organizations to benefit from cost savings, regular software updates, and increased collaboration among research teams. On-premises deployment involves installing and running NLP systems directly within a healthcare or life science organization's local infrastructure. This approach offers greater control over data security and compliance with regulatory requirements, including HIPAA and GDPR, as sensitive patient information and proprietary research data can be kept within the organization's firewall. While on-premises solutions may require a higher initial investment in hardware and software licenses, they enable full customization and integration with existing IT ecosystems, often resulting in optimized performance tailored to specific organizational needs.
End-User: Evolving usage of natural language processing (NLP) by Physicians in Healthcare and life Sciences setting
Clinical operators, such as nurses and administrative staff, benefit from natural language processing (NLP) by enhancing workflows and reducing administrative burdens. NLP can automate extracting pertinent information from clinical notes for patient management systems, improve scheduling by understanding and processing patient requests, and facilitate real-time documentation support. NLP technologies empower patients by providing more accessible and interactive ways to manage their health. Chatbots and virtual health assistants can interpret and respond to patient inquiries, offering personalized advice and support. Physicians utilize NLP to streamline and enhance clinical decision-making. NLP tools can process and summarize large volumes of unstructured clinical data, such as patient medical histories, lab results, and radiology reports, providing physicians with concise, relevant information and saving time. Healthcare and life sciences researchers use NLP to glean insights from vast amounts of biomedical literature and clinical reports. NLP facilitates systematic literature reviews by quickly extracting and synthesizing findings from published studies. It also supports the identification of patient cohorts for clinical trials by sifting through electronic health records to find candidates who meet specific criteria.
Regional Insights
Natural language processing (NLP) in the Healthcare and Life Sciences market is evolving in the Americas owing to their investments are mainly directed towards AI-driven startups and research initiatives by large tech companies and top-tier universities. The General Data Protection Regulation (GDPR) regulations have driven advancements in data security and privacy in NLP technologies, influencing purchasing behavior favoring compliant solutions and strategic partnerships and funding for AI research, further increasing the market growth in the EMEA region. On the other hand, in Middle Eastern countries, NLP in healthcare is largely driven by the need to provide medical services in multiple languages and improve accessibility in remote regions. In Asia-Pacific, major market players are investing heavily in AI healthcare services, focusing on solutions that cater to the large population, with a shift towards mobile health applications and online healthcare services. In addition, in the Asia-Pacific region, governments are actively promoting research in AI and NLP, with initiatives focused on addressing the aging population through technology-enhanced healthcare solutions.
FPNV Positioning Matrix
The FPNV Positioning Matrix is pivotal in evaluating the NLP in Healthcare & Life Sciences Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).
Market Share Analysis
The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the NLP in Healthcare & Life Sciences Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.
Key Company Profiles
The report delves into recent significant developments in the NLP in Healthcare & Life Sciences Market, highlighting leading vendors and their innovative profiles. These include 3M Company, Amazon Web Services, Inc., Apixio Inc., Averbis, Cerner Corporation, Dolbey Systems Inc., Gnani.a, Google LLC by Alphabet Inc., Heath Fidelity, Hewlett Packard Enterprise Development LP, Inovalon, International Business Machines Corporation, Lexalytics by InMoment, Linguamatics by IQVIA, and Microsoft Corporation.
Market Segmentation & Coverage
1. Market Penetration: It presents comprehensive information on the market provided by key players.
2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.
3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.
4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.
5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.
1. What is the market size and forecast of the NLP in Healthcare & Life Sciences Market?
2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the NLP in Healthcare & Life Sciences Market?
3. What are the technology trends and regulatory frameworks in the NLP in Healthcare & Life Sciences Market?
4. What is the market share of the leading vendors in the NLP in Healthcare & Life Sciences Market?
5. Which modes and strategic moves are suitable for entering the NLP in Healthcare & Life Sciences Market?