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Greater health data lays the groundwork for the implementation of AI algorithms. A large part of industry focus of implementation of AI in the healthcare sector is in the clinical decision support systems. As more data is collected, machine learning algorithms adapt and allow for more robust responses and solutions. [111] Numerous companies are ...
A group of systems that implement the same integration profile address the need/scenario in a mutually compatible way. For example, the Digital Imaging and Communications in Medicine (DICOM) standards specify many different formats for image data. A given set of images that might comply with some optional parts of the standards might still not ...
Health care analytics is the health care analysis activities that can be undertaken as a result of data collected from four areas within healthcare: (1) claims and cost data, (2) pharmaceutical and research and development (R&D) data, (3) clinical data (such as collected from electronic medical records (EHRs)), and (4) patient behaviors and preferences data (e.g. patient satisfaction or retail ...
Health information technology (HIT) is "the application of information processing involving both computer hardware and software that deals with the storage, retrieval, sharing, and use of health care information, health data, and knowledge for communication and decision making". [8]
Galaxy is a web platform for data-intensive biology using geographically-distributed supercomputers. [56] LabKey Server is an extensible platform for integrating, analyzing and sharing all types of biomedical research data. It provides secure, web-based access to research data and includes a customizable data processing pipeline.
Emails, audio recordings, or physician notes about a patient are examples of unstructured health data. While advances in health information technology have expanded collection and use, the complexity of health data has hindered standardization in the health care industry. [2]
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