Using Artificial Intelligence and Big Data-Based Documents to Optimize Medical Coding
Clinical information systems (CISs) in some hospitals streamline the data management from data warehouses. These warehouses contain heterogeneous information from all medical specialties that offer patient care services. It is increasingly difficult to manage large volumes of data in a specific clin...
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| Main Authors: | , , |
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| Format: | Ebooks |
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IntechOpen,
2019-06-13.
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| Online Access: | Get Online |
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| Summary: | Clinical information systems (CISs) in some hospitals streamline the data management from data warehouses. These warehouses contain heterogeneous information from all medical specialties that offer patient care services. It is increasingly difficult to manage large volumes of data in a specific clinical context such as quality coding of medical services. The document-based not only SQL (NoSQL) model can provide an accessible, extensive, and robust coding data management framework while maintaining certain flexibility. This paper focuses on the design and implementation of a big data-coding warehouse, and it also defines the rules to convert a conceptual model of coding into a document-oriented logical model. Using that model, we implemented and analyzed a big data-coding warehouse via the MongoDB database and evaluated it using data research mono- and multi-criteria and then calculated the precision of our model. |
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| Item Description: | https://mts.intechopen.com/articles/show/title/using-artificial-intelligence-and-big-data-based-documents-to-optimize-medical-coding |