Reimagine, reinvent and redesign
Artificial intelligence has huge potential to transform health insurance claims management, but realizing its full benefits will require more than just implementing new technology. in our Previous blog On this topic, we explore how agent AI is transforming the health claims experience. In this blog, we will provide a roadmap on how to Insurers can truly reap the full benefits by recognizing holistic ART (“AI Powered, Resilient, Trusted”) reimagines the model to achieve agility, resilience and measurable impact at scale by rethinking core operations, empowering talent and integrating AI-driven tools. We’ll dive into three key success factors for AI-led modernization of health claims: reimagining work, reinventing the workforce, and redesigning the workbench. By addressing these elements, insurance companies can not only streamline their processes but also build a more trustworthy and resilient organization that truly meets the needs of their policyholders.
1. Reimagine work
- Leverage the power of data to innovate across your entire ecosystem: Enabling healthcare providers to leverage integrated data, such as electronic medical records, enables a full range of customized diagnoses, treatments and post-discharge options, giving patients a better understanding of their health.
- Operating model and process changes, not just technology changes: Data and AI can enhance business outcomes, but technology alone is not enough. Modernizing ways of working, operating models and processes is critical to leveraging the full potential of technology.
- sure quick win: A pilot approach with clear, tangible results within target processes and user groups can build confidence in the new technology and provide lessons for wider rollout. For example, digital claims submission, automated adjudication, and threshold increases can quickly realize benefits and ease operational pressures as digital submissions increase.
2. Reshape the workforce
- Humans in the loop: Human review is critical to improving AI and analytic models, especially in early stages and edge cases such as medical document remediation, eligibility checks, and fraud detection.
- Change Management Implementation KPIs: If system users are not familiar with new AI technologies and integrating these capabilities into daily operations, expected results will not be achieved. The future workforce must master skills such as rapid engineering and low-code workflow modifications.
- User engagement and recognition : AI use cases and solutions and business process design require employee buy-in. Design thinking workshops should prioritize value opportunities and requirements based on organizational context and needs, especially in the early stages. Likewise, without business coordination, the desired outcomes will not be easily achieved.
3. Redesign the workbench
- Choose the right solutions and technologies: When planning your AI architecture, consider best practices and best-of-breed approaches tailored to your business needs and technology strategy. Insurers are moving to a decoupled, best-of-breed architecture that integrates specialized solutions and ecosystems powered by APIs and the cloud. Proactive supplier management is critical to taking advantage of these opportunities to improve efficiency, accuracy and a better customer experience.
- Leveraging traditional analytics: Individual customers’ past claims history, a library of similar claims cases and the latest health trends should be leveraged to identify underreporting, overreporting and fraudulent claim coverage and trends, with built-in flexibility rather than a one-size-fits-all, rules-based approach.
- Rigorous data migration, solution deployment and testing: Data migration should be properly planned by a single end-to-end owner. Validating AI technology using real migration and transaction data is critical to adhering to responsible AI principles such as fairness, transparency, explainability, and accuracy.
- Set a baseline range and manage it strictly: Consider the scope of implementation across markets and ensure all stakeholders are aligned on baselines and expected outcomes. Scope creep is common in new non-commodity genAI technologies.
- Build a scalable digital core: With a strong digital core, insurers can move from siled AI pilots to enterprise-wide adoption, accelerating innovation and optimizing costs through reusable architectures and unified data pipelines. This approach enhances insights, minimizes redundant investments, and ensures better control and operational resilience.
Embracing the art of modernizing AI-led health claims
With proven benefits and continued innovation, there is no doubt that most insurance companies will eventually move to AI-driven, Resilient and Trustworthy (ART) health claims management. But early adopters are already reaping the rewards of our product Latest Thought Leadership Demonstrating that insurance finance outperformers are leading the way in automation and workflow management, digitization and operational model simplification to enhance customer engagement. Specifically, 79% of high-performing insurers are implementing digitalization, compared with 65% of their peers, which the report highlights allows insurers to streamline claims processing for customers and increase efficiency for sales partners. There are significant risk factors such as operational constraints and technical debt that require thorough planning, and there is no one-size-fits-all approach to health claims modernization. It must be specific based on business and technology strategies. For extensive experience helping insurance companies on their transformation journey, please contact us via the link below: Mark Xu or Sher Litan.
