AMN Healthcare's AI deployment

 

Equitus.us KGN and its knowledge graph ecosystem (KGE) can significantly improve AMN Healthcare's AI deployment and cost controls by addressing key challenges in data unification, AI readiness, and operational efficiency. The healthcare industry, especially in recruitment, deals with vast amounts of fragmented, siloed data from various sources like EHRs, professional licenses, and communication logs. A knowledge graph, unlike traditional databases, creates a network of interconnected data points, allowing for a more complete and contextual understanding.

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KGE -KGNN Can Benefit AMN Healthcare

• Automated Data Integration: AMN Healthcare's current systems, like Smart Square and ShiftWise, manage data for scheduling, vendor management, and talent. Equitus KGNN's platform automates the ingestion, cleaning, and unification of both structured and unstructured data, eliminating the need for manual data preparation and complex ETL (extract, transform, load) pipelines. This reduces manual overhead and speeds up the process of making data AI-ready.

• Semantic Contextualization: KGNN transforms siloed data into a self-constructing knowledge graph, enriching it with correlations and real-world context. This means it can automatically uncover relationships between a clinician's qualifications, their location preferences, past work experience, and specific facility needs. For example, it could link a specific nurse's per-diem availability with a hospital's sudden staffing shortage, a task that might be difficult with traditional, fragmented data systems. This improved context leads to more confident and accurate AI deployments.

• Improved AI Deployment and Cost Control: By providing a semantically rich, AI-ready data layer, KGNN enables faster and more accurate AI deployments. For AMN, this means their AI models for predictive scheduling and talent matching would be more effective. The platform can help:

  • Increase AI accuracy: By providing a comprehensive view of data, it reduces the risk of AI "hallucinations" and improves the precision of matching the right clinician to the right role.

  • Lower infrastructure costs: Equitus.us offers the option to run KGNN on-premise with technologies like IBM Power10 servers, which can provide high-performance deep learning without the need for expensive GPUs or cloud dependency. This could help AMN lower hardware and energy costs while maintaining control over sensitive data.

  • Faster time-to-value: The platform's automated features accelerate the path from raw data to actionable intelligence, allowing AMN to deploy new AI-powered applications more quickly and see a return on investment sooner.

Ultimately, by unifying disparate data sources and providing a contextualized, AI-ready foundation, Equitus.us KGNN could empower AMN Healthcare to deploy more sophisticated and reliable AI solutions, leading to more efficient operations and significant cost savings in their core business of healthcare recruitment.

This video provides an overview of how knowledge graphs and AI are being used in the defense and intelligence sectors, demonstrating the technology's ability to unify and analyze complex, disparate data sources.

Advanced Knowledge Graph for Defense