AI and Deep Learning Techniques for Health Plan Satisfaction Analysis and Utilization Patterns in Group Policies

Abstract

Health insurance aims to protect and safeguard against incurring unexpected heavy healthcare bills. There are three main types of health insurance, namely indemnity, proprietary administration, and prepaid arrangement. Depending on the structure and type of benefits, policies are classified into individual, family, and group policies. The main concentration in the health insurance domain is on personal (individual or family) health policy, but in this work, group policy is emphasized where a large number of people are involved. Commitments that improve satisfaction with the health insurance-related functionalities for the consumer corporation can result in long-term advantages, such as transparency and business opportunities. Utilization patterns of the current insurance responsiveness are unknown and can lead to guidelines and approaches for profitable policy development and enhancement. The deep learning model has the inherent capacity to understand and represent an experienced person's problems very effectively, as well as their outlook on a particular product or system. In recent days, as a new potential dimension, several data-driven artificial intelligence methods and theoretical learning models have been used in healthcare-related satisfaction or mental illness predictive analysis, but the application of these techniques on the stochastic utilization statistics of health strategies is untold. Also, probabilistic data-driven AI technical learning methods will be used for further enhancement of these results, which will be computationally expensive but also a potential measure of surveillance use, in terms of performance and the development of policy forecasting groups. The organizations then use this data to prepare improved or more attractive plans for the upcoming operations on evaluating purposeful consumption organizations. This specific module's extent will be dealt with in a forthcoming paper. In this work, we recognize the use of AI methods to analyze the diagnostic instructions and operating information of these types of health strategies of organizations. We are designed to break the distinct outcomes into authentic utilization-styled prognoses of the available groups of these strategies. After defining these modules and debates in relevant environments, we then generalize the unknown effects that demonstrate outcomes of the above human-made game. Health plan satisfaction analysis is of considerable significance in health insurance contexts. Traditional methods validated through patient surveys can be subjective, time-consuming, and expensive. Advanced technologies have opportunities to understand the relative preferences and dislikes of the patients regarding these group policies optimized by the health plan companies. The policies are important and have considerable implications for the healthcare sector to optimize their priorities. To enhance these issues, in this module, we focus on audited stochastic utilization statistics of services in group policies during the last two years. Herein, it is important to formulate group policies, in detail, health strategies of the cellular and personal group, with their operating rates, as well as an appropriate attendance to utilization environment hazardous extremes: one which falls in the 'Safe Environment' and the second entitled 'Niched Friendly-Fire Environment.' Analyzing utilization statistics is one of the major branches of big data emerging visualization, and accessible assistance policies are expected to grow more prominently within the vicinity.