Veda’s AI Precision Exceeds 90%
Independent Audit Validates Provider Data Accuracy
AI-powered solutions solve ghost networks and attestation challenges
Whitepaper paves the way for improved data quality, transparency, and accountability in healthcare
Veda’s AI Precision Exceeds 90%
Independent Audit Validates Provider Data Accuracy
AI-powered solutions solve ghost networks and attestation challenges
Whitepaper paves the way for improved data quality, transparency, and accountability in healthcare
Unbiased Data Validation
Inaccurate provider data is a significant obstacle to widespread and equitable access to
healthcare in the United States. Innovative technologies can solve healthcare data challenges but how do you know that technology is performing as well as it claims to?
By subjecting our data to impartial analysis we’re holding our proprietary solutions accountable and providing transparency in the industry.
The Erdős Institute performed a blind independent review of Veda's AI-powered data curation engine, Vectyr, and found highly accurate provider directory data with certain accuracy scores exceeding 90 percent for critical information like addresses, locations, and phone numbers.
How does Veda provide such accurate data?
Veda’s team of data scientists uses supervised learning systems to create optimal provider data profiles and unsupervised learning for grouping data.
The white paper proves: Automation is an effective and necessary approach to supporting health plan members who rely on provider directories to find care.
Reinforcing Commitment to Accountable AI-Powered Solutions in Healthcare
By facilitating accurate provider directory data, as mandated by the No Surprises Act, validation of Veda's proprietary curation and machine learning methodologies represents a pivotal milestone in Veda's journey towards fostering greater transparency and accountability in payer data solutions.
As skepticism surrounds AI tools in healthcare, validation from the Erdős Institute underscores Veda's commitment to leading the market with ethical and reliable solutions," said Meghan Gaffney, Co-Founder and CEO of Veda. While outdated methods for maintaining accuracy and compliance continue to fail, Veda is proof positive that automation is an effective and necessary approach to supporting health plan members who rely on provider directories to find care.
A rigorous analysis by Erdős not only demonstrates Veda's commitment to AI excellence but also sheds light on the prevalent issue of 'ghost networks,' or inaccurate provider directories. Yale Law and Policy Review found between forty-five and fifty-two percent of provider directory listings had errors, with some individual plans having error rates as high as ninety-eight percent.
Reinforcing Commitment to Accountable AI-Powered Solutions in Healthcare
By facilitating accurate provider directory data, as mandated by the No Surprises Act, validation of Veda's proprietary curation and machine learning methodologies represents a pivotal milestone in Veda's journey towards fostering greater transparency and accountability in payer data solutions.
As skepticism surrounds AI tools in healthcare, validation from the Erdős Institute underscores Veda's commitment to leading the market with ethical and reliable solutions," said Meghan Gaffney, Co-Founder and CEO of Veda. While outdated methods for maintaining accuracy and compliance continue to fail, Veda is proof positive that automation is an effective and necessary approach to supporting health plan members who rely on provider directories to find care.
A rigorous analysis by Erdős not only demonstrates Veda's commitment to AI excellence but also sheds light on the prevalent issue of 'ghost networks,' or inaccurate provider directories. Yale Law and Policy Review found between forty-five and fifty-two percent of provider directory listings had errors, with some individual plans having error rates as high as ninety-eight percent.
The Erdős study conclusively shows how, using highly accurate curated datasets like Vectyr, the provider data accuracy problem can be improved greatly without the need for time-consuming, manual attestation methods of the past.
The Erdős study conclusively shows how, using highly accurate curated datasets like Vectyr, the provider data accuracy problem can be improved greatly without the need for time-consuming, manual attestation methods of the past.
Provider Phone Number Accuracy
Provider Address Accuracy
Other Field Accuracy
WHAT WE DO
Dataset Curation
VEDA VECTYR
Build a high-quality network. Credential providers faster. Process claims without error. Make referrals seamlessly. Discover How
Data Quality Scoring
VEDA QUANTYM
Automate provider network updates with real-time scoring of data quality within 24 hours. Discover How
Process Automation
VEDA VELOCITY
Automatically apply predetermined business rules to your unstructured and disorganized files to quickly compare, validate, and address critical missing data elements. Discover How
WHAT WE DO
Dataset Curation
VEDA VECTYR
Build a high-quality network. Credential providers faster. Process claims without error. Make referrals seamlessly. Discover How
Data Quality Scoring
VEDA QUANTYM
Automate provider network updates with real-time scoring of data quality within 24 hours. Discover How
Process Automation
VEDA VELOCITY
Automatically apply predetermined business rules to your unstructured and disorganized files to quickly compare, validate, and address critical missing data elements. Discover How
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