The Client
When millions of Americans think about health insurance, our client is one of the first payors on their mind. The organization has a reputation for high-quality and affordable coverage, accepted by an extensive array of general health and wellness providers and specialists nationwide. Their commitment to the ongoing improvement and expansion of their products and services offers their members personalized coverage that continues to evolve, improving their members’ experience, and increasing overall health and peace of mind.
The Challenge
The client had plans to enhance their members’ experience, but lacked the necessary understanding and cohesion across their data to pinpoint their greatest areas of opportunity. In its current state, data was decentralized, spread across multiple sources without any uniformity or verified accuracy. This prevented the flow of critical data into internal analytics and limited groundbreaking collaborations with external partners.
Consider, for example, the client’s planned partnership with an online pharmacy. Together, the two organizations would supply the client’s members with prescriptions straight to their homes. But first, they needed to integrate relevant data sets between the two organizations. The barrier created by their segregated data threatened to reduce the speed and accuracy of their proposed full-service prescription program.
Rather than allow this or other data-driven initiatives to flounder, the client engaged w3r for help. Their challenges required the expertise of a data integration and engineering partner familiar with HIPAA compliance, the healthcare industry, and their organizational best practices. For that reason, they chose w3r Consulting.
The Solution
Before the start of our data integration, our team set out to define the requirements. We began by conducting a needs assessment with various stakeholders across both organizations to define the scope, business requirements, and technical requirements. We then identified all of the relevant data sources, established data management practices, defined how much time it would take to process the data sets, and agreed to a project timeline. As a result, accurate expectations and standards were locked in long before the final deliverable.
Using this information, the w3r project team proceeded to create a data pipeline solution.
- We extracted the data from the source systems to compare alongside client data sets.
- We evaluated external and internal data, transforming it from the source schema in instances when it failed to comply with the client’s established standards.
- We loaded the cleansed and standardized data into a HIPAA compliant yet accessible data warehouse, mapping the data to match fields with the client’s member data.
After loading relevant data sets from internal and external sources, our team created an automated framework to implement a seamless pipeline. The established production system gathers data under the guidelines of a specific business logic and automatically converts the source data into our client’s specified format.
Results
With their automated pipeline in place, our client is able to process, load, and transform data in ways that are secure and compatible with target destinations. On an organizational level, this will allow them to better navigate the complex data exchange between other healthcare organizations, government agencies, and authorized vendors.
More immediately, they’ve been able to efficiently verify and process prescription requests from their pharmaceutical partner. On a daily basis, the automated production system reviews requests, confirming that inquiries for the pharmaceutical home-delivery service are from current members who qualify for the requested prescription. Our work operates in the background without any maintenance, allowing our clients to focus on bringing their members the best experience possible.
Can data pipeline solutions enhance your member experience, operational capabilities, and more? Reach out to our team to discuss how you can unlock the full potential of your data.
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