The Challenge

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

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

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.
• 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.

The Challenge

• A top regional health insurance organization was burdened with legacy processes effecting the quality of care for their 3.9 million members
• These older processes required overnight queries and had difficulties handling multiple workloads
• This latency was due to very poor coding standards, bad versioning control as well as a problematic environment management system.
• Because of this, users grew increasingly dubious of the system which led to gaps in their data and more importantly gaps in their effectiveness of care

The Solution

• After a brief discovery session, our team of healthcare and data & analytics experts began working to improve processing speeds by consolidating, restructuring and automating deployments.
• This was accomplished by introducing Git Version Control software and DevOps repos and pipelines to continuously plan, develop and release updates in a timely fashion.
• We also implemented peer review tracking on all push requests to production. This permits users to dig into the code further allowing for quicker bug fixes to maintain optimal query and commute speeds.

The Result

• By transitioning the organization away from their legacy systems to modern solutions like Git and DevOps, our client was able to reduce processing speeds by 3 to 4 hours on a single project.
• As a result, they can now continuously improve processes with version control software, simultaneous maintain stability, and develop and improve the overall quality of the process with peer review tracking.
• We are proud of our work with this client because not only did we boost query speeds, but in doing so we also improve the collective confidence in the organization’s data services allowing for users to close the gaps for better effectiveness of member care.