Predictive Modeling with AI and ML for Small Business Health Plans: Improving Employee Health Outcomes and Reducing Costs

Abstract

As healthcare costs continue to rise, small businesses are increasingly seeking innovative ways to improve employee health outcomes while controlling expenses. Predictive modeling using Artificial Intelligence (AI) and Machine Learning (ML) offers a promising solution by enabling more proactive and personalized healthcare strategies. This paper explores the potential of AI and ML in the context of small business health plans, focusing on how these technologies can predict health risks, optimize care, and ultimately reduce costs. By analyzing employee health data, predictive models can identify at-risk individuals, suggest targeted interventions, and monitor the effectiveness of wellness programs. The integration of AI/ML can also enhance decision-making in plan design, offering tailored benefits that align with specific employee needs. This research highlights case studies demonstrating successful implementation of AI and ML-driven strategies, the challenges small businesses face in adoption, and the long-term impact on both employee well-being and financial sustainability. The findings underscore the transformative potential of these technologies in revolutionizing small business health plans, offering a path to improved health outcomes and reduced healthcare expenditures.