CASE STUDY

Eliminating Data Silos saves Healthcare Payer nearly $4 MM yearly

The Challenge

Clinical personnel at a top regional health insurance organization were unable to make real time decisions about care management because of disparate data systems and data silos. Access to this data in real time would allow care managers to drive greater quality care for members. Some major hurdles attributed to these data silos included:

Multiple manual processes to consolidate data
• Technical debt from duplicative data in multiple silos
• Decreased capacity to work on advanced analytics caused by the increased need for data wrangling
• The difficulties of building technical teams outside of IT

The Conflict


Care managers at a healthcare payer organization are suffering from siloed data effecting data quality and hampering capabilities to make real time decisions creating risk for both members and the organization.

The Solution

Integrate disparate data sources from data silos into a quality data models (QDM) to help improve data integrity and standardization with the support of a holistic methodology that combines People (data experts), Process (data governance), and Technology (modern data platform). This proven framework will eliminate data fragmentation as well as enhance data exchange capabilities for the organization.

Goals

Unify all data sources to eliminate data silos and redundancies giving personnel access to quality data that will improve care management capabilities.

The Result

By centralizing all data sources and aligning that framework with business goals, the organization began breaking down data silos and optimized their data with a consolidated approach that saved the organization nearly $4 MM yearly in operational efficiencies. Within this centralized approach, clinical personnel now have timely access to the full breadth of trusted data and can make informed decisions about best care practices that relate directly to the member’s health.

To take this a step further, harnessing a single source of quality data allowed for continuous tracking and monitoring of quality metrics over time, making it possible for automation of patient follow-ups and outreach. Automation helped increase quality scores (HEDIS and Stars Ratings) and more importantly, improve the quality of care that members experience for the organization.

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.