An Examination of Machine Learning in the Process of Data Integration

© 2023 by IJCTT Journal
Volume-71 Issue-6
Year of Publication : 2023
Authors : Shashank Agarwal
DOI :  10.14445/22312803/IJCTT-V71I6P115

How to Cite?

Shashank Agarwal, "Graph Networks: Transforming Provider Affiliations for Enhanced Healthcare Management ," International Journal of Computer Trends and Technology, vol. 71, no. 6, pp. 86-90, 2023. Crossref,

Provider affiliations are critical in healthcare because they facilitate care coordination, resource allocation, referral management, and network optimization. Traditional methods of establishing and maintaining affiliations rely on manual procedures, human contacts, and geographical proximity, resulting in inefficiencies and limits in accurate recording and keeping full affiliation data. To overcome these challenges, a new approach utilizing graph networks has emerged as a promising solution. Graph networks provide a comprehensive and intuitive framework for modelling and analyzing complicated interactions within provider networks. They enable healthcare organizations to integrate heterogeneous data sources, improve visibility, and gain sophisticated analytics and data-driven insights. In this paper, we focus on the application of graph networks in healthcare organizations. We discuss the advantages and challenges of implementing graph networks and discuss how to manage these challenges effectively.

Graphical Networks, Provider affiliation, Artificial intelligence, Optimization, Artificial neural network.


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