Designing AI-Powered Neural Networks for Real-Time Insurance Benefit Analysis and Financial Assistance Optimization in Healthcare Services

Abstract

The intersection of healthcare services and artificial intelligence has gotten more attention with the advancement of technology. Artificial intelligence technologies traditionally have been widely used for predictive analytical modeling implementation in finance applications. The following research extends the utility of AI despair neural networks into healthcare decision-making processes, focusing on the real-time claim-based insurance benefit analysis in healthcare services. Designed state-of-art AI despair neural networks are able to analyze insurance policies and remaining eligible insurance amounts to pay. The integration of AI despair neural networks with it, along with the financial assistance optimization engine for claim policies can therefore assist the financial officers in making more real-time financial decisions regarding the healthcare treatment services arrangement or the patient referral. The insurance company can also set more effectively beneficial policies with benefits in improving patients’ retention and services loyalty. Moreover, if this real-time assistive artificial intelligent utility is accepted by the healthcare benefit analysis department, it can facilitate the advance arrangement in healthcare service by integrating meaningful predictive models, providing more practical use of it.
In healthcare services, although the healthcare treatments require charges up front prior to treatment, there are various cases of medical financial hardship under the current economic situation in the world. From the medical services provider’s view, usually healthcare services can be arranged in advance before the patient starts the treatments. It would benefit both the insurance company, as well as the healthcare medical service providers, to set an insurance benefit analysis to assist the medical services benefit financial control department. Initially, a feasibility study was conducted, in which probability distribution of medical expense was observed against the initial visits medical diagnosis parameters for the probability distribution was dynamically accumulated from demographic information. Afterwards, the switching temporal data was observed, regarding its frequency distribution characterization. Information Modeling with Holistic Vision Mechanism can effectively manage the hierarchical statistical medical information, and it is shown that it is fit for the medical case since commonly multiple measurements formats are used. The selection of optimal odds-ratio distance can be determined by a model-free postulation approach. Lastly, a proposal can guide total medical expense and conditional probability distribution of benefit insurance expense amount estimator, as well as the financial assistance payment from the beginning of incurred medical expenses time, with the aid of a predictor engine. The financial assistance optimization engine design combines both dynamic programming and probit models.