Predictive Modeling of Health Insurance Claims in the United States

Abstract

Purpose: The aim of the study was to assess predictive modeling of health insurance claims in the United States.

Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.

Findings: Predictive modeling of health insurance claims has emerged as a crucial tool for insurance companies and healthcare providers to anticipate and manage costs effectively. By analyzing vast amounts of historical claims data, predictive models can forecast future claim volumes, identify high-risk individuals or groups, and predict the financial impact of various healthcare interventions. These models utilize advanced statistical techniques, machine learning algorithms, and data mining approaches to uncover patterns and trends within the data. Key findings suggest that predictive modeling can significantly improve risk assessment accuracy, leading to better resource allocation, fraud detection, and cost containment strategies.

Implications to Theory, Practice and Policy: Health belief model, diffusion of innovation theory and social determinants of health may be used to anchor future studies on assessing the predictive modeling of health insurance claims in the United States. Healthcare institutions and insurers should invest in infrastructure that supports the timely acquisition and processing of relevant data, ensuring the practical application of predictive modeling in day-to-day claims processing. Policymakers should actively collaborate with researchers, industry experts, and regulatory bodies to formulate and enforce ethical standards for the development and implementation of predictive models in health insurance claims.