The Europe NLP in Healthcare and Life Sciences Market is expected to witness market growth of 19.6% CAGR during the forecast period (2022-2028).
Huge amounts of unorganized patient data are entered into EHRs on a regular basis, but computers struggle to help clinicians aggregate this vital information. Structured data, such as claims or CCDAs / FHIR APIs, can help evaluate illness burden, but they only provide us a partial picture of the patient record. The vast majority of healthcare paperwork is unstructured and hence goes mostly unused because mining and extracting this data is difficult and time-consuming. That data is not in a useable shape for modern computer-based algorithms to retrieve without NLP technology.
Natural language processing in healthcare employs specialized engines capable of cleaning vast amounts of unstructured health data for previously ignored or incorrectly classified patient problems. In addition, Natural language processing of medical information employing machine-learned algorithms can reveal disease that hasn't been coded before, which is a critical aspect of finding HCC disease.
Almost all healthcare facilities in the region now employ electronic medical information systems to replace conventional handwritten medical reports. Structured diagnoses, radiographic examinations, test values, clinical symptoms, molecular biomarkers, and a slew of other treatment-related data are all stored and handled electronically. However, a significant proportion of clinical information is stored in unstructured or unknown formats. To re-use these unstructured data in the construction of personalized treatment algorithms, natural language processing (NLP) must be used to extract and arrange the data through the augmentation and annotation of correct terminology.
The technological requirements for structured data extraction in the creation of treatment algorithms are based on the continued development of semantic analysis approaches such as text mining. Clinical text notes or clinical outcome parameters have lately been substantially developed using natural language processing (NLP).
The Germany market dominated the Europe NLP in Healthcare and Life Sciences Market by Country in 2021, and is expected to continue to be a dominant market till 2028; thereby, achieving a market value of $439.9 million by 2028. The UK market is estimated to grow at a CAGR of 18.6% during (2022-2028). Additionally, The France market is expected to witness a CAGR of 20.5% during (2022-2028).
Based on Component, the market is segmented into Solution and Services. Based on Solution Type, the market is segmented into Clinical Variation Management, Population Health Management, Counter Fraud Management, and Others. Based on End User, the market is segmented into NLP for Physician, NLP for Patients, NLP for Researchers, and NLP for Clinical Operators. Based on NLP Type, the market is segmented into Rule-based, Statistical, and Hybrid. Based on Deployment Mode, the market is segmented into Cloud and On-premise. Based on Organization Size, the market is segmented into Large Enterprises and Small & Medium Enterprises (SMEs). Based on Application, the market is segmented into IVR, Summarization & Categorization, Reporting & Visualization, Pattern & Image Recognition, Text & Speech Analytics, Predictive Risk Analytics, and Others. Based on countries, the market is segmented into Germany, UK, France, Russia, Spain, Italy, and Rest of Europe.
The market research report covers the analysis of key stakeholders of the market. Key companies profiled in the report include 3M Company, IBM Corporation, Microsoft Corporation, Google LLC, Amazon Web Services, Inc., Cerner Corporation, Corti ApS, Lexalytics, Inc., Health Fidelity, Inc., and Linguamatics.
By Component
By Country
Huge amounts of unorganized patient data are entered into EHRs on a regular basis, but computers struggle to help clinicians aggregate this vital information. Structured data, such as claims or CCDAs / FHIR APIs, can help evaluate illness burden, but they only provide us a partial picture of the patient record. The vast majority of healthcare paperwork is unstructured and hence goes mostly unused because mining and extracting this data is difficult and time-consuming. That data is not in a useable shape for modern computer-based algorithms to retrieve without NLP technology.
Natural language processing in healthcare employs specialized engines capable of cleaning vast amounts of unstructured health data for previously ignored or incorrectly classified patient problems. In addition, Natural language processing of medical information employing machine-learned algorithms can reveal disease that hasn't been coded before, which is a critical aspect of finding HCC disease.
Almost all healthcare facilities in the region now employ electronic medical information systems to replace conventional handwritten medical reports. Structured diagnoses, radiographic examinations, test values, clinical symptoms, molecular biomarkers, and a slew of other treatment-related data are all stored and handled electronically. However, a significant proportion of clinical information is stored in unstructured or unknown formats. To re-use these unstructured data in the construction of personalized treatment algorithms, natural language processing (NLP) must be used to extract and arrange the data through the augmentation and annotation of correct terminology.
The technological requirements for structured data extraction in the creation of treatment algorithms are based on the continued development of semantic analysis approaches such as text mining. Clinical text notes or clinical outcome parameters have lately been substantially developed using natural language processing (NLP).
The Germany market dominated the Europe NLP in Healthcare and Life Sciences Market by Country in 2021, and is expected to continue to be a dominant market till 2028; thereby, achieving a market value of $439.9 million by 2028. The UK market is estimated to grow at a CAGR of 18.6% during (2022-2028). Additionally, The France market is expected to witness a CAGR of 20.5% during (2022-2028).
Based on Component, the market is segmented into Solution and Services. Based on Solution Type, the market is segmented into Clinical Variation Management, Population Health Management, Counter Fraud Management, and Others. Based on End User, the market is segmented into NLP for Physician, NLP for Patients, NLP for Researchers, and NLP for Clinical Operators. Based on NLP Type, the market is segmented into Rule-based, Statistical, and Hybrid. Based on Deployment Mode, the market is segmented into Cloud and On-premise. Based on Organization Size, the market is segmented into Large Enterprises and Small & Medium Enterprises (SMEs). Based on Application, the market is segmented into IVR, Summarization & Categorization, Reporting & Visualization, Pattern & Image Recognition, Text & Speech Analytics, Predictive Risk Analytics, and Others. Based on countries, the market is segmented into Germany, UK, France, Russia, Spain, Italy, and Rest of Europe.
The market research report covers the analysis of key stakeholders of the market. Key companies profiled in the report include 3M Company, IBM Corporation, Microsoft Corporation, Google LLC, Amazon Web Services, Inc., Cerner Corporation, Corti ApS, Lexalytics, Inc., Health Fidelity, Inc., and Linguamatics.
Scope of the Study
Market Segments Covered in the Report:
By Component
- Solution
- Services
- Clinical Variation Management
- Population Health Management
- Counter Fraud Management
- Others
- NLP for Physician
- NLP for Patients
- NLP for Researchers
- NLP for Clinical Operators
- Rule-based
- Statistical
- Hybrid
- Cloud
- On-premise
- Large Enterprises
- Small & Medium Enterprises (SMEs)
- IVR
- Summarization & Categorization
- Reporting & Visualization
- Pattern & Image Recognition
- Text & Speech Analytics
- Predictive Risk Analytics
- Others
By Country
- Germany
- UK
- France
- Russia
- Spain
- Italy
- Rest of Europe
Key Market Players
List of Companies Profiled in the Report:
- 3M Company
- IBM Corporation
- Microsoft Corporation
- Google LLC
- Amazon Web Services, Inc.
- Cerner Corporation
- Corti ApS
- Lexalytics, Inc.
- Health Fidelity, Inc.
- Linguamatics
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Table of Contents
Chapter 1. Market Scope & Methodology
Chapter 2. Market Overview
Chapter 3. Competition Analysis - Global
Chapter 4. Europe NLP in Healthcare and Life Sciences Market by Component
Chapter 5. Europe NLP in Healthcare and Life Sciences Market by End User
Chapter 6. Europe NLP in Healthcare and Life Sciences Market by NLP Type
Chapter 7. Europe NLP in Healthcare and Life Sciences Market by Deployment Mode
Chapter 8. Europe NLP in Healthcare and Life Sciences Market by Organization Size
Chapter 9. Europe NLP in Healthcare and Life Sciences Market by Application
Chapter 10. Europe NLP in Healthcare and Life Sciences Market by Country
Chapter 11. Company Profiles
Companies Mentioned
- 3M Company
- IBM Corporation
- Microsoft Corporation
- Google LLC
- Amazon Web Services, Inc.
- Cerner Corporation
- Corti ApS
- Lexalytics, Inc.
- Health Fidelity, Inc.
- Linguamatics
Methodology
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