Rearchitecting Health Data Platform to Boost Analytics Capability
The Inroads Team unified disparate data through a rearchitecture, making it accessible to more stakeholders for diverse needs, from web design to healthcare decisions.
One of Inroads’ core capabilities is reducing time-to-delivery for analytics on client datasets through engineering — and sometimes re-engineering — their data solutions. We value the opportunity to help our clients make more data-informed decisions and enhance their consumer engagement strategies.
The Inroads team worked with a government agency to rearchitect their health and insurance data analytics platform. Our collaboration enhanced the efficiency of their database and codebase, improved platform consistency and maintainability, reduced overhead time for developers, and minimized risk for human error. This rearchitecture has facilitated workflow improvements for stakeholders across the client organization.
We collaborated with our client’s leadership and partner teams to find efficiencies and improve data robustness across their data platform, which included information about:
Healthcare and insurance: beneficiary enrollment, subsidies, demographics, plans/contracts
Providers and care: contact information, medical and pharmaceutical claims, preventative services
Web activity: user metrics, account logins, email engagement
At the start of this initiative, Inroads had already worked closely with our client on a variety of projects relating to beneficiary outreach, data analytics/modeling, and web tooling. We were well-positioned to play a strategic role — providing insights on data sources, business logic, and practical use cases.
The Inroads team built workflows with automated orchestration tools, ensuring that data and reports are refreshed at appropriate cadences in a no-touch manner. In turn, we minimized personnel time needed for semi-regular tasks that require manual triggering.
We further incorporated several methods to improve the database and codebase for our client:
Staging models: cleaned and standardized data to align with existing datasets and standards
Intermediate and analytics models: combined and enriched data for business application across the suite of datasets in the client platform, enabling strategic analysis and decision making
Custom functions and change logs: minimized the size of our codebase and the volume of tables necessary in the database, while maintaining coverage of end-user needs
API continuity: our team enabled continued access to the platform’s API to make personalized data accessible to improve the beneficiary experience.
Our rearchitecture work brought together many disparate data sources and made them available to an even broader set of stakeholders. They can now leverage the platform for needs ranging from web design and marketing to cost efficiency and beneficiary health decision-making.
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