Future-Proofing Growth of Clinical Knowledge Graph Technology Market, Size, Analytical Overview, Growth Factors, Demand and Trends Forecast to 2031 : Raapid, Datavid, Wisecube AI, Cambridge Semantics

The Clinical Knowledge Graph Technology Market is estimated for 2024 for the forecast period 2024-2031, as highlighted in a new report published by Worldwide Market Reports.

Market Overview:

Clinical knowledge graph technology facilitates integration and analysis of clinical data by linking heterogeneous healthcare data using semantic relationships. It allows clinicians to retrieve contextual and interconnected patient information from electronic health records and other clinical databases to enhance clinical decision making.

Market Dynamics:

Growing need for clinical data integration from disparate healthcare IT systems and increasing adoption of electronic health records are expected to drive the clinical knowledge graph technology market growth over the forecast period. Clinical knowledge graphs extract meaningful relationships between concepts in patient EHRs, medical literature and other knowledge bases which help clinicians gain a comprehensive view of patient conditions. This increases clinical efficiency and outcomes. Additionally, knowledge graphs also support development of AI/ML models by providing semantically interconnected datasets for training.

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Market Opportunity:

Precision or personalized medicine aims to stratify patients based on their specific genetic and molecular characteristics and provide customized treatment options accordingly. It holds promise to revolutionize disease management by improving outcomes and reducing adverse reactions from "one-size-fits-all" therapies. Knowledge graph technologies can power precision medicine initiatives by integrating multi-omics data sources like whole genomes, transcriptomes, proteomes, etc. of large patient cohorts with their electronic health records. Clinical decision support tools built using these knowledge graphs can then help clinicians determine the most effective therapeutic approaches or clinical trial eligibility tailored to an individual's molecular profile. This represents a major opportunity for knowledge graph vendors to support the growth of personalized healthcare.

Market Trend:

Regulators are increasingly mandating inclusion of real-world evidence or data gathered outside traditional clinical trials to determine drug safety and effectiveness. There is also a trend of patients taking a more proactive role in managing their conditions through wearables and apps that generate substantial health and wellness data. However, aggregating and analyzing this complex assortment of real-world and patient-generated data poses unique challenges. Leading knowledge graph solution providers are exploring methods to seamlessly fuse such heterogeneous datasets with clinical records and research information. The integration capability of knowledge graphs positioned at the intersection of disparate evidence sources offers opportunities to glean deeper insights from the expansive real-world front.

The Leading Players involved in the global Clinical Knowledge Graph Technology market are:

Raapid
Datavid
Wisecube AI
Cambridge Semantics
Ontotext
Elsevier

Segmentation by Type:

Structured Knowledge Graph
Unstructured Knowledge Graph

Segmentation by Applications:

Medical Diagnosis and Treatment
Drug Discovery
Others

Market Driver:

Clinical decision support systems have become increasingly important in healthcare to help clinicians and providers make well-informed decisions. They allow healthcare professionals to access up-to-date clinical knowledge and patient data at the point of care to determine the best course of treatment. This helps improve diagnostic accuracy, reduces medical errors, ensures evidence-based practice, and leads to better health outcomes overall. With the growing complexity of medical knowledge and treatments, clinical decision support becomes more crucial. Knowledge graph technologies play a key role in powering these systems by integrating diverse types of medical information and allowing seamless access and analysis of patient data.

Pharmaceutical and life sciences companies are under constant pressure to innovate new drugs and therapies faster and at lower costs. Knowledge graph technologies have emerged as a promising solution to help streamline drug discovery processes and make them more efficient. They allow companies to aggregate extensive internal research data as well as external clinical trial findings, published studies and other scientific information into knowledge bases. This centralized knowledge can then be automatically analyzed using techniques like semantic search, question answering and relationship extraction to identify new targets, biomarkers and pathways for developing novel drugs. The ability of knowledge graphs to integrate disparate data sources and uncover hidden insights is driving their increased adoption in the drug discovery domain.

Market Restrain:

One of the major challenges in widespread adoption of clinical knowledge graph technologies is dealing with sensitive patient data privacy and security concerns. Healthcare information contains some of the most confidential details like medical records, diagnosis, treatments, insurance and financial information. Any leaks or unauthorized access of such data can have severe legal and ethical implications. While knowledge graphs promise faster insights, their deployment requires strict protocols and controls to anonymize personal identifiers, secure access and audit usage. Addressing privacy through techniques like federated learning and decentralized data storage while also ensuring regulatory compliance remains an ongoing hurdle, especially as more real-world patient data is incorporated into knowledge bases.

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Reason to Buy

✔ Save and reduce time carrying out entry-level research by identifying the growth, size, leading players, and segments in the global Clinical Knowledge Graph Technology Market.
✔ Highlights key business priorities in order to guide the companies to reform their business strategies and establish themselves in the wide geography.
✔ The key findings and recommendations highlight crucial progressive industry trends in the Clinical Knowledge Graph Technology Market, thereby allowing players to develop effective long-term strategies in order to garner their market revenue.
✔ Develop/modify business expansion plans by using substantial growth offerings in developed and emerging markets.
✔ Scrutinize in-depth global market trends and outlook coupled with the factors driving the market, as well as those restraining the growth to a certain extent.
✔ Enhance the decision-making process by understanding the strategies that underpin commercial interest with respect to products, segmentation, and industry verticals.

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