Risk Assessment Models for the Health Insurance Sector

Abstract

Purpose: The article discusses the use of risk assessment models in the health insurance sector, aiming to enhance the quality of care provided to individuals by leveraging technologies such as cloud-based platforms and remote medical sensors.

Methodology: The article reviews various papers on the topic, examining studies ranging from the impact of co-insurance in Vietnam to the architecture of e-health systems. It also discusses different models for connecting body sensors to cloud-based systems, emphasizing the importance of algorithm and shared data models (SDMs) for the health and insurance industries.

Findings: Findings highlight the increasing trend of individuals, families, groups, corporate houses and governments leveraging health insurance policies to mitigate risks, even in areas lacking basic primary health facilities. The article underscores the significance of technologies like data mining and machine learning in adding value to the insurance sector.

Practical Implications: The article presents an architecture for health risk analysis monitoring, consisting of multiple layers to ensure effective risk management.

Originality: The interdisciplinary study of merging designs for healthcare and insurance is depicted as an ongoing process aimed at improving overall care quality. The article explores innovative approaches and platforms, showcasing the originality in addressing challenges within the health insurance sector.

Citation

Reepu, , Kumar, P. and Singh, M. (2024), "Risk Assessment Models for the Health Insurance Sector", Taneja, S., Kumar, P., Reepu, Kukreti, M. and Özen, E. (Ed.) Data Alchemy in the Insurance Industry, Emerald Publishing Limited, Leeds, pp. 211-219. https://doi.org/10.1108/978-1-83608-582-920241032