CASE STUDY

Dimensional Model facilitates care gap in Analysis for Healthcare Payer

The Challenge

A nationally recognized health insurance organization has been hampered with slow data processing times affecting important HEDIS reporting initiatives across the entire organization. This is a direct result of a stale data model which has been setup to store data in multiple locations. To make matters worse, the internal engine is managed using a Run ID time frame. Because of this, users are required to have a firm understanding of Run ID protocol in order to extract the appropriate data from the appropriate hub. To compound the issue, the data is riddled with unnecessary fields that serve no real business purpose and causing confusion amongst end-users only further slowing down data processing and analysis & reporting capabilities.

The Conflict?


 

A growing healthcare payer organization is suffering from lagging data processing times and limited views of data that is adversely affecting the organizations HEDIS program.

The Solution

After a thoughtful discovery session, our team determined that a redesign of the current model into a modern dimensional model was needed to successfully consolidate the organization’s data. Ultimately, this would support faster processing times for their business intelligence resources as well as help the organization realize the strength of a single source of truth. It was also determined that the Run ID protocol needed to be removed from the model to give users access to a time-based and event-date-based system to elevate their HEDIS program. As part of the redesign, our team would also ensure that data types were uniformed and removed unnecessary fields that may cause confusion among the end-users.

Goals

By designing and incorporating a sophisticated dimensional model, the organization would improve their overall HEDIS reporting resulting in increased analytical ability and increased scores, which are associated with millions of dollars in reimbursements.

The Result

Effective communication with the organization’s business segments to answer specific questions regarding processes, use cases, data structure, and organizational needs allowed for the successful redesign of the data model which prioritized the consolidation of disparate data hubs into a central repository resulting in improved processing speeds and enhanced data visualization capabilities. We were also able to create dimensional models for specific HEDIS data that had never been exposed such as NCQA Benchmarking Data, CMS Stars Ratings, and RxAnte while continuing to provide product owners with their specific visions and requirements. This sophisticated redesign was also able to remove Run ID protocol allowing for longitudinal HEDIS reporting. As a result of our data solutions, the payer organization was able to produce consolidated dashboards and efficiently identify gaps in care, delineate areas for targeted outreach, and detect opportunities for provider billing education. These dashboards were also able to track the direct impact of outreach and provider education campaigns over time to determine the most effective approach.

Contact Us

Retail

Predictive Analytics Improve Business Outcomes for Retailers

If data is the new business currency, then predictive analytics are the means in which organizations can take control of that currency to maximize its benefits.
Retail

Artificial Intelligence (AI) Use Cases for the Retail Industry

Overall spending on Artificial Intelligence (AI) systems is projected to reach $79.2 billion in 2022, which is more than double the amount spent in 2019.
Retail

Automation Solutions Closes the Gap on Last Mile Delivery for Retailers

According to a recent retailer’s report conducted by Blue Yonder, only 14% of the 300 executives surveyed say their fulfillment locations are fully automated.