The success of value-based care payment models, population health analytics, and machine learning initiatives are all highly dependent on the availability of accurate and complete patient medical records. A significant barrier to any of these initiatives is a lack of semantic interoperability across information sources. The increasing demand for high-quality, low latency semantic interoperability has outpaced the industry’s ability to manage semantic normalization efforts using manual approaches. Automation that leverages artificial intelligence combined with human exception management can significantly improve the throughput, quality, and consistency of aggregated patient data, while dramatically reducing the cost associated with the process.
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