Next Level Analytics, in partnership with Project 3 Consulting, supported a large government agency in optimizing their claims processing workflow. The agency approached Next Level Analytics with a vague ask but with a complex need: they required a solution to track the performance, adoption, and ROI of a new in-house tool designed to expedite claims processing. Using Tableau, Next Level Analytics developed a comprehensive suite of dashboards that visualized the tool’s usage, provided actionable insights, and allowed the agency to monitor tool performance. By understanding the very complex workflows and wrangling disparate data, Next Level Analytics not only built multiple dashboards but also created a consolidated data layer that has laid the foundation for future analytical needs.

The client is a large government agency responsible for providing financial assistance, including benefits. The agency developed a new in-house tool using machine learning to expedite claims processing and reduce the growing backlog. They sought the expertise of Next Level Analytics, a partner of Project 3 Consulting, to help track the tool’s adoption, performance, and return on investment (ROI) through custom Tableau dashboards.

The agency faced several significant challenges:

• Backlog of disability claims: Due to the manual nature of the claims process, the agency struggled with a growing backlog of claims, causing delays and longer wait times for American citizens in need of disability benefits.
• Complexity of the tool and data: The new tool was part of a complex case processing workflow, with many stages, teams, and workflows involved in decision-making.
• Data disorganization: Their vast amount of data was scattered and poorly documented, making it difficult to integrate and utilize.
• Limited visibility into ROI: It was challenging to measure the ROI of the tool because it impacted only certain parts of overlapping stages in the case process, making it difficult to pinpoint its exact effect.

Next Level Analytics brought a strategic approach to solve the agency’s challenges:

• Tableau Dashboards: Developed over 12 robust Tableau dashboards to monitor key metrics such as tool adoption, user engagement, feature usage, and performance of the machine learning prediction model. This suite of dashboards continues to evolve, with new content being developed to meet the expanding needs of the agency.
• ETL and Data Integration: Designed a comprehensive ETL process that consolidated and transformed disparate data sources into a unified data layer, making reporting and analysis more efficient. This data layer not only served the immediate needs but is flexible enough to support future projects and evolving data requirements.
• Proactive Analytics: Provided the agency with the ability to track tool usage and performance in real-time, offering actionable insights into adoption, user behavior, and tool effectiveness. In addition, Next Level Analytics proactively built solutions to identify times when resource usage would be high, helping the agency allocate the appropriate server resources for pre-processing and task prioritization. This proactive approach ensures that they can stay ahead of potential bottlenecks and maintain system performance as usage scales. As their tool and workflows evolve, Next Level Analytics remains engaged, producing additional insights and content to further optimize the agency’s operations.

The implementation process was iterative and involved:

• Assessment: Understanding they agency’s complex case processing workflow and how the new tool integrated within it. Since the tool itself was being rolled out iteratively, Next Level Analytics needed to understand the various phases of the rollout and how each would impact the process and data. This required close collaboration with the agency to ensure that as the tool evolved, the data and workflows could be tracked and measured accurately at each stage. In addition to this, it was quickly realized that the data was disorganized and difficult to navigate. There was a lack of documentation and proper structure, making it challenging to integrate disparate data sources. Next Level Analytics had to wrangle and consolidate this “messy” data to make it usable for reporting and analysis.
• Dashboard Development: The team started by creating wireframe dashboards using the available data to provide immediate value. These initial dashboards gave the agency the ability to track basic metrics and begin monitoring tool performance. As the process continued, the dashboards were refined through ongoing collaboration with the agency to ensure they met their specific needs. Unlike one-off, one-dimensional solutions, these dashboards were designed to be dynamic and multi-use, catering to various levels and stakeholders across the agency. The goal was to create dashboards that could serve hundreds of use cases, offering powerful filtering and drill-down capabilities. This approach ensures that the dashboards remain flexible, self-service tools that empower users to explore data and insights in depth. The development process was dynamic, allowing for adjustments based on real-time feedback and the evolving scope of the agency’s goals, providing them with a scalable solution that adapts as their needs grow.
• ETL and Data Layer Creation: In the early stages, the agency’s data was scattered, disorganized, and lacked proper documentation, making it difficult to work with. Next Level Analytics designed an ETL process to consolidate and clean the data, transforming it into a unified data layer. This process allowed the team to create summary tables and data sources that simplified reporting and analytics. By structuring the data in an accessible and meaningful way, the solution not only addressed immediate needs but also created a flexible foundation that could support future reporting requirements and analytics as their needs grew.
• Continuous Enhancements: The implementation of the dashboards was an iterative process, with continuous refinement and improvement. Regular check-ins with the agency ensured the dashboards remained aligned with their changing needs and goals. As new data sources were identified and additional metrics became necessary, they were integrated into the dashboards, constantly enhancing their functionality. This iterative approach ensured that the dashboards evolved alongside their workflow and provided ongoing value, allowing the agency to gain deeper insights as their tool adoption grew and new challenges emerged.

The solution delivered significant benefits for the large government agency:

• Improved Tool Adoption: The agency saw an increase in tool adoption, with users growing from around 200 to nearly 8,000.
• Actionable Insights: Dashboards provided critical insights into user behavior, tool performance, and case processing efficiency, driving data-informed decision-making.
• Enhanced Performance Monitoring: Real-time monitoring of prediction models, document processing, and tool usage allowed the agency to make timely adjustments for better outcomes.
• Increased ROI: While measuring ROI in terms of direct case processing time reduction was challenging, the dashboards provided valuable insights into other key ROI metrics. These included improvements in prediction model accuracy, which enhanced decision-making, and increased case review accuracy, leading to fewer errors and more complete cases with more diagnoses tagged. The dashboards allowed the agency to track these improvements, demonstrating the tool’s growing effectiveness and impact on overall case processing quality.