Reimagine, reshape and redesign
The potential of AI in transforming health insurance claims management is vast, but realizing its full benefits requires more than just implementing new technology. In our previous blog on this subject, we explored how agentic AI can transform the health claims experience. In this blog, we will provide a roadmap as to how insurers can truly reap the full benefits by endorsing a holistic A.R.T. (“AI-powered, Resilient, Trusted”) reinvention model by rethinking core operations, empowering talent, and integrating AI-powered tools to achieve agility, resiliency, and measurable impact at scale. We will delve into the three key success factors for AI-led health claims modernization: Reimagining work, Reshaping the workforce, and Redesigning the workbench. By addressing these elements, insurers can not only streamline their processes but also build a more trusted and resilient organization that truly meets the needs of their policyholders.
1. Reimagining work
- Innovate across the ecosystem with the power of data: Engaging healthcare providers with integrated data, like electronic medical records, can enable a full range of tailored diagnosis, treatment, and post-hospitalization options, providing patients with better visibility of their health conditions.
- Operating model and process change, not just technology change: Data and AI enhance business outcomes, but technology alone isn’t enough. Modernizing ways of working, operating models, and processes is essential to fully leverage the technology’s potential.
- Identify quick wins: A pilot approach in targeted processes and user groups, with clear tangible outcomes, can boost confidence in new technology and provide learnings for broader rollout. For example, digital claims submission, automated adjudication, and threshold increases can quickly realize benefits and ease operational pressure as digital submissions rise.
2. Reshaping the workforce
- Human in the loop: Human reviews are essential to improve AI and analytics models, particularly in early stages and for edge cases, such as medical document remediation, eligibility checks, and fraud detection.
- Change management enables KPI achievement: Without familiarizing system users with new AI technologies and integrating these capabilities into daily operations, expected outcomes won’t be achieved. The future workforce must master skills like prompt engineering and low-code workflow modifications.
- User engagement and buy-in : AI use cases and solutions, along with business process designs, require employee buy-in. Design thinking workshops should prioritize value opportunities and requirements based on organizational context and needs, especially in early phases. Without business alignment, again, expected outcomes won’t be easily achieved.
3. Redesigning the workbench
- Selecting the right solution and technology: When planning AI architecture, consider Best-in-Class vs. Best-in-Breed approaches, tailored to business needs and technology strategy. Insurers are shifting to decoupled, Best-in-Breed architectures with specialized solutions and ecosystem integration, enabled by APIs and Cloud. Proactive vendor management is crucial to leverage these opportunities for efficiency, accuracy, and better customer experience.
- Leverage traditional analytics : Individual customer past claims history, similar claims case library and latest health trends should be leveraged to identify underclaim, overclaim, and fraudulent claim ranges and trends with built-in flexibility rather than a one-size-fits-all, rule-based approach.
- Data migration, solution deployment and testing with rigor: Data migration should be properly planned with a single end-to-end owner. Validating AI technology with real migrated and transactional data is crucial for adhering to responsible AI principles of fairness, transparency, explainability, and accuracy.
- Set a baseline scope and manage rigorously: Consider the scope of implementation across markets and ensure all stakeholders agree on baseline and expected outcomes. Scope creep is common with new, non-commoditized genAI technology.
- Establish a scalable digital core: With a strong digital core, insurers can shift from isolated 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 greater control and operational resilience.
Embracing the A.R.T of AI-led health claims modernization
With proven benefits and constant innovation, there is no doubt most insurers will eventually move towards AI-powered, resilient, trusted (A.R.T) health claims management. But early adopters are already reaping the rewards with our latest thought leadership showing that insurance financial outperformers are leading the way in automation and workflow management, digitization and operating model streamlining to enhance customer interactions. Specifically, 79% of outperformers are digitizing compared to 65% of their peers and the report highlights that this has enabled insurers to streamline claims processing for customers and improve sales partners’ efficiency. There are significant risk factors such as operation constraints and tech debt which need thorough planning and there is no one-size-fits-all approach for health claims modernization. It must be contextualized based on business and technology strategy. For extensive experience helping insurers deliver their transformation journey please contact us on linked in at Marco Tsui or Sher Li-Tan.
