Research Article
AI-Enabled Automation Solution for Utilization Management in Healthcare Insurance
@INPROCEEDINGS{10.1007/978-3-031-35081-8_24, author={Gaurav Karki and Jay Bharateesh Simha and Rashmi Agarwal}, title={AI-Enabled Automation Solution for Utilization Management in Healthcare Insurance}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part II}, proceedings_a={ICISML PART 2}, year={2023}, month={7}, keywords={Utilization Management Prior Authorization Healthcare Insurance Claim Processing Deep Learning Artificial Intelligence Automation Natural language processing}, doi={10.1007/978-3-031-35081-8_24} }- Gaurav Karki
Jay Bharateesh Simha
Rashmi Agarwal
Year: 2023
AI-Enabled Automation Solution for Utilization Management in Healthcare Insurance
ICISML PART 2
Springer
DOI: 10.1007/978-3-031-35081-8_24
Abstract
As businesses advance toward digitalization by automating an increasing number of procedures, unstructured forms of text in documents present new challenges. Most organizational data is unstructured, and this phenomenon is on the rise. Businesses like healthcare and insurance are embracing business process automation and making considerable progress along the entire value chain. Artificial intelligence (AI) algorithms that help in decision-making, connect information, interpret data, and apply the insights gained to rethink how to make better judgments are necessary for business process automation.
A healthcare procedure called Prior Authorization (PA) could be made better with the help of AI. PA is an essential administrative process that is a component of their utilization management systems, and as a condition of coverage, insurers require providers to obtain preapproval for the provision of a service or prescription. The processing of insurance claim documents can be facilitated using Natural Language Processing (NLP). This paper describes the migration of manual procedures to AI-based solutions in order to accelerate the process. The use of text similarity in systems for information retrieval, question-answering, and other purposes has attracted significant research. This paper suggests using a universal sentence encoder, a more focused strategy, to handle health insurance claims. By extracting text features, including semantic analysis with sentence embedding, the context of the document may be determined. The outcome would have a variety of possible advantages for members, providers, and insurers. AI models for the PA process are seen as promising due to their accuracy and speed of execution.
