Roster Automation Meets Data Quality
Transformational Roster Automation with an Accuracy-First Perspective
The Roster Automation Problem
Health plans are making tremendous investments in the accuracy of their provider directories. Meanwhile, provider data arriving via rosters is largely flawed. In the best-case scenario, the rosters are 70% accurate.
Many solutions on the market automate roster intake, without considering data quality, and wipe out provider directory accuracy gains.
How To Preserve An Investment in Data Quality
Roster automation can be transformational for a health plan but only when it’s done from an accuracy-first perspective. It’s vital to use a provider directory solution with automation and data quality accuracy filters to achieve data quality. Veda is the only solution on the market that offers both speed and accuracy for provider rosters.
Veda uses a variety of AI and machine learning techniques, developed and tested with scientific rigor, to identify provider and facility data, enrich and validate that information, and produce outputs that transform manual workflows and increase data accuracy.
Other products in the industry focus on using automation to confirm static information about a provider—that is, the pieces of information that do not change over time, like medical school graduation date, birthdate, and residency. These technologies are not fully automated, however, and require, by design, the manual validation of information by humans using a web interface.
Veda’s technology is fundamentally different. We focus on fully automating both the static information and the more challenging temporal information about a provider– data that changes at varied rates over time, like practice address, phone, and group affiliation.
Our patented systems do not require manual outreach to providers, rather they rely on data created by providers throughout their established workflows. This increases data accuracy by removing human error while also decreasing provider abrasion. Validating millions of temporal data elements in real-time requires Veda’s full automation system and could not be solved manually.
How can Veda’s AI machine learning model identify accurate provider data, when information directly from the provider is often inaccurate?
