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Technical Overview of a Smart Data Fabric

What Are Smart Data Fabrics?

An enterprise data fabric is a new architectural approach that speeds and simplifies access to data assets across the entire business. It accesses, transforms, and harmonizes data from multiple sources, on demand, to make it usable and actionable for a wide variety of business applications.

Smart data fabrics take this approach one step further by embedding a wide range of analytics capabilities, including data exploration, business intelligence, natural language processing, and machine learning directly within the fabric, making it faster and easier for organizations to gain new insights and power intelligent predictive and prescriptive services and applications.

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May 09, 2025
Longitudinal Health Record
This briefing highlights how InterSystems is collaborating with diverse customer organizations to streamline the set-up of new data feeds to a high-performing longitudinal health record.
May 08, 2025
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InterSystems technology enables you to optimize your supply chain performance by predicting disruptions before they occur, and optimally handling them when they do.
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Simplifying Data Access with InterSystems FHIR Solutions
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Compare RAG, fine-tuning, and prompt engineering to find the best AI approach for your needs. Includes practical examples and an interactive decision tool.
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Internet of Healthcare Things
Developing solutions that deliver the promise of Internet of Healthcare Things (IoHT) can be difficult. To be successful, developers must capture vast amounts of medical device data in real time.
Apr 15, 2025
Enterprise Master Person Index
Next-Generation Enterprise Master Person Index for Identity Management Seamless patient, member, and beneficiary identification is essential for efficient operations across healthcare organisations and government agencies. Yet, fragmented systems, inconsistent identifiers, and data gaps continue to disrupt workflows, increase costs, and compromise care quality and service delivery. 35% of denied medical claims stem from inaccurate patient identification¹, while mergers and affiliations further complicate record consolidation. Even within a single information system, duplicate or overlaid records can introduce inefficiencies and risks; by creating costly administrative burdens and compromising the accuracy of AI workflows built on unreliable data. To maintain data integrity and prevent cascading errors, organisations must be able to detect problematic records in real time and trigger corrective actions.
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IDC InfoBrief
In today’s rapidly evolving healthcare landscape, the strategic implementation and optimization of advanced Electronic Health Record (EHR) systems continue to be paramount, despite significant prior investments in this area. Download the IDC InfoBrief

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