The most up-to-date, comprehensive, and accurate source of data. Your organization can access profiles of every active provider in the U.S.—over 6 million.
See how we’ve helped leading healthcare organizations achieve significant cost savings, improve data accuracy, and enhance patient care. Here, you will find our results, research, reports, and everything else our scientists are testing in the Veda Lab – no lab coat required.
At Veda we understand that every data point is an opportunity to improve the healthcare experience. And we can see the potential when data is no longer a barrier.
If we can speed up end-point to end-point connections of the healthcare lifecycle and remove additional steps in the clerical process, it will result in improved patient experiences.
However, simplifying workflows isn’t enough unless the data driving the information is accurate and timely.
In the fast-paced world of healthcare, sluggish provider data is a liability, not a luxury. Backlogged rosters pile up, decisions stall, and resources drain away. But what if provider data moved faster?
How Veda’s Speed Redefines Provider Data Management
Imagine what is possible when automation delivers provider rosters at unprecedented speeds. That’s the power of Veda. We’re not just automating data; we’re redefining it. In the future of provider data, speed isn’t just a goal – it’s how we connect health systems and payers to solve complex healthcare data challenges.
Manual provider data approaches are specifically troublesome when handling large provider rosters, some containing hundreds of rows. Handling the volume of data created in healthcare every day is unfeasible without AI.
Where AI Comes In
One of the main benefits of AI is the ability to quickly wrap up tasks—especially when compared to manual methods and reduced processing times free up resources for other meaningful tasks.
Large, unruly provider rosters or atypical formats? Not a problem with robust and reliable (and patented) AI. When data quality is maintained by automation, it also means rosters don’t need addressing or fixing again later.
AI also delivers on what we call “synthetic attestation.” This is an attestation that occurs with no provider intervention or effort. While this is important in all specialties, it’s especially impactful for behavioral health when providers do not have precious moments available to pick up the phone and self-attest. Synthetic attestation uses the data providers are already creating in their day-to-day workflows.
Faster Data, Faster Care
With accurate data that quickly gets to where it needs to be, providers are displayed correctly, decision-making is improved, and patients have faster access to care.
Digital Health Transformers podcast host Gregory Cave is joined by Meghan Gaffney, CEO and Co-Founder of Veda. She discusses the importance of accurate provider data, the challenges patients face in accessing specialized care, and how AI is transforming healthcare operations. Meghan explains how Veda automates the transfer of provider data to health plans, helping patients find in-network providers quickly and efficiently.
Meghan covers the impact Veda’s technology has had on healthcare organizations-read about our automation that saves money and improves member experience.
Deepfakes Can Damage Businesses—Here’s How To Fight Back
Deepfakes—AI-generated synthetic media in which visuals or audio are manipulated to create deceptively realistic content—are often discussed in terms of their impact on the public’s perception of current events, but they pose a growing threat to businesses as well. Created and leveraged by unscrupulous actors, deepfakes can enable fraud, perpetuate misinformation and cause lasting brand damage.
Whether they take the form of a fabricated video, cloned voice or contrived image, deepfakes can erode trust and disrupt operations in ways many companies aren’t prepared for. Members of Forbes Technology Council discuss some of the specific ways deepfakes could be used to hurt a company and what leaders can do to defend their organizations (or respond when a deepfake succeeds).
Regularly Review Employee LinkedIn Profiles
“We’ve noticed LinkedIn profiles for people who claim to work at our company but who don’t or never have. Such deepfake profiles damage our company because our people, our reputation and our brand are being abused. Leaders can respond to this specific use of deepfakes by periodically reviewing all “employees” of your company. Look for surprises and flag the frauds for review by LinkedIn.” – Robert Lindner, Veda
Meghan: When we founded Veda, we set out to create lasting infrastructure in the healthcare industry that allows accurate data to flow automatically between payers and providers. That meant inventing new ways of processing data that were both secure and accurate, and then publishing our work through the patent process. Ten years later, we are staying true to those objectives— we’ve built AI tools to modernize healthcare and we’ve shared our discoveries through the patent process so our solutions can fuel further innovation.
Bob: We needed to bring a fresh perspective to the problems surrounding provider data that have remained stagnant for over four decades. By creating wholly new approaches to the trillion-dollar data administration problem in healthcare, we knew that our solutions were innovative and unique. So we began early in our company’s history with the patenting of Veda’s technology—protecting our inventions in the short term, while also benefitting all of us in the long run.
Veda’s patents protect our entity resolution engine, AI modeling engine, ML training data process & platform, and web-scale data collection.
How else has Veda committed to AI development?
Bob: I’m an astrophysicist and I built AI tools in radio astronomy before founding Veda. Scientists have been building innovative AI tools for decades and have a cultural rigor that drives them to test and publish their findings.
We’ve recruited a team of PhD scientists—from physics to molecular genetics and astronomy—who help build and test Veda’s in-house LLM technology, train our machine learning models, and develop the infrastructure that is the foundation for Veda’s patented systems.
What makes your AI systems different from others in the industry?
Bob: Our AI is trained on Veda’s proprietary training data, which is ethically sourced and high quality. Our training data is used to fine-tune Veda’s models and help solve critical healthcare-specific tasks with the highest possible performance.
Plus, Veda’s AI models are entirely owned by Veda with no external dependencies. Our application of AI differentiates us from others in the industry because it leverages LLMs and contextual understanding but does not produce hallucinations. We allow the model to select correct answers, not to invent free-form text.
Meghan: Our company is founded on scientific rigor and was built specifically for healthcare from Day 1. We have over 80 combined years of AI expertise, and our commitment to science and data integrity compels us to approach problems differently. It hasn’t always been easy. We did the hard work upfront. We threw out the rule book and asked ourselves, “How do I ensure I can access care?”
Putting ourselves in the patients’ shoes is how we began to turn these challenges on their heads and look at them differently—we’ve calibrated our success to the patient’s ability to use the data to access care. What does that mean technologically? It means our AI systems must provide hallucination-free, predictable, and measurable results because that is what our customers expect and it is what patients deserve.
Bob: It was essential we build the system in a new way. The blend of patents is what makes our AI systems so unique. The patented technology works together, in parallel, to accomplish complex data curation challenges with speed and accuracy that was previously thought impossible.
Which provider data problem is Veda’s AI solving?
Bob: All of them. But the one I’m particularly excited about, and that our most recently granted patent underscores, is our ability to automate intake at scale.
Meghan: Veda’s technology isn’t just a single model. It offers many capabilities working in tandem towards one comprehensible function. There are several foundational data challenges that our technology solves. One of the unique benefits of our patented technology is that it can be assembled in different ways to address many kinds of healthcare industry problems.
Bob: For example, our patented entity resolution system efficiently matches the identity of healthcare providers. The special challenge in this problem is that healthcare providers change lots of their information over the course of their careers, so the system needs to connect their identities while allowing for a normal amount of drift in some fields over time.
Why do you need AI to solve provider data problems?
Meghan: Veda’s AI can cut through data barriers and ensure that people can access care when they need it the most. That’s why we founded Veda—because everyone deserves access to accurate, up-to-date information that empowers them to get the care they need.
What are the risks of using AI in healthcare and how can they be mitigated?
Meghan: While everyone is looking to AI and automation for solutions, in healthcare the AI isn’t living up to the hype. In a race to reduce costs, many have lost sight of the problem they are trying to solve and have left out foundational components of professional services, actual results, and rigorous testing. In fact, I think the irresponsible development of some AI tools could negatively impact the companies that are taking a transparent and tested path.
For instance, imagine a business trying a new product for the first time, and it doesn’t go well. It breaks, it’s costly, and leaves a negative impression. After that bad experience, you might be reluctant to try another product in that category. This can happen with AI too—if one company delivers poor results, people might dismiss AI solutions altogether and revert to outdated methods, which ultimately hurts innovation.
Bob: We succeed with AI when it is effective, robust, and focused on responsibly making an impact. While there is a risk posed by poorly designed and underperforming tools, I see an opportunity for Veda to prove our integrity to the industry. We’re proud to showcase our patented AI and machine learning solutions, which were developed and tested with an unwavering commitment to scientific rigor and ethical, security-forward principles.
Ready for Veda’s provider data solutions? Contact us.
Veda Announces Tenth AI and Machine Learning Patent
Proprietary Technology Leads the Health Data Industry
MADISON, February 6, 2025 – Veda Data Solutions, Inc. (Veda), a healthcare technology company solving complex provider data challenges, announced its tenth patent has been granted by the United States Patent and Trademark Office, with four patents secured in the last four months.
“Our provider data solution is the only one of its kind,” said Veda Chief Science & Technology Officer and patent author Dr. Bob Lindner. “The 10 patents work in tandem to deliver automation, speed, and provider data accuracy that others can’t match. Our IP portfolio spans the entire operational pipeline from web-scale data collection, entity resolution, automatic semantic recognition and transformation, accuracy modeling, and human-in-the-loop interactivity.”
Why did Veda patent its AI technology?
Veda is committed to building responsible and transparent AI. The patent process is rigorous and ensures inventors are both creating technology with unique value while also openly sharing their research to fuel an innovation ecosystem.
What does Veda’s patented AI technology do?
Veda’s patented technology definitively solves provider data problems plaguing the healthcare industry.
Veda offers the optimal solution for automatic mass-scale demographic information management along with automatic roster ingestion, directory accuracy, network construction, and network adequacy optimization.
Veda’s best-in-class product leaves behind flawed, biased, and outdated notions of “sources of truth” and attestation, instead leaning on artificial intelligence and sound scientific design to produce reliable and reproducible results.
Is Veda’s AI secure?
Veda’s AI systems are HITRUST-certified and built entirely in-house. Veda’s implementation of its patented technology is bias and hallucination-free with all customer data and services fire-walled within the United States for maximum security.
At Veda, provider data is treated with the same reverence for security and privacy that is required for patient data.
What is next for Veda’s proprietary innovations?
With 14 more pending patents, Veda continues innovating to remain the optimal solution for provider data roster automation and data accuracy scoring.
“Veda’s technology isn’t only patented, it’s powerful. Innovated precisely for healthcare organizations and their unique data problems, our patents are essential to the delivery of fast and accurate data to Veda’s customers,” said Veda CEO Meghan Gaffney. “Veda was the first to tackle the provider roster data problem successfully and continues to develop innovative solutions in healthcare data today. With our patented approach, organizations can dramatically reduce operating costs by automating complex business rules for data extraction, transformation, and loading.”
About Veda Veda blends science and imagination to solve healthcare’s most complex data issues. Using AI, machine learning, and human-in-the-loop automation, our solutions dramatically increase productivity, enable compliance, and empower healthcare businesses to focus on delivering care. Veda’s platforms are simple to use and require no technical skills or drastic system changes because we envision a future for healthcare where data isn’t a barrier—it’s an opportunity. To learn more about Veda, visit vedadata.com and follow us on LinkedIn.
In this episode, #MillenniumLive is joined by Dr. Bob Lindner, Chief Science & Technology Officer and Co-Founder at Veda, for a deep dive into the fascinating world of artificial intelligence (AI). Bob shares his insights on what excites him most about AI development, exploring the balance between innovation and responsibility. Tune in as Bob discusses the differences between supervised and unsupervised learning, the critical role of data science in AI modeling, and why modeling is essential to delivering impactful results.
We’ll look at the future of healthcare data and the challenges it faces, and how Veda is positioned to lead the charge in transforming the industry. Whether you’re an AI enthusiast or just curious about the technology shaping our future, this episode is packed with knowledge, thought-provoking discussions, and practical advice for businesses exploring AI solutions.
August 13, 2024 – Veda Data Solutions, healthcare’s leading AI provider data platform, was named No. 417 on the 2024 Inc. 5000 list revealed today.
Among software companies, Veda was ranked 47th and the Madison, Wis.-based company was the 4th highest-ranked company on the list from Wisconsin. This is Veda’s second consecutive year on the Inc. 5000 list.
“At Veda, we are committed to improving the healthcare experience by creating the most accurate, curated provider data on the market and partnering with health plans and provider organizations to ensure their members have seamless access to appropriate care,” said Meghan Gaffney, CEO and co-founder of Veda. “Being named in the top 10 percent of high growth companies validates our solution and reflects the value our customers place on member satisfaction, patient access to care, and their commitment to delivering on Medicaid and Medicare requirements.”
The Inc. 5000 class of 2024 represents companies that have driven rapid revenue growth while navigating inflationary pressure, the rising costs of capital, and seemingly intractable hiring challenges. Among this year’s top 500 companies, the average median three-year revenue growth rate is 1,637 percent. In all, this year’s Inc. 5000 companies have added 874,458 jobs to the economy over the past three years.
“Veda is committed to Health Equity, and creating the most accurate provider data is how we make good on that promise,” said Gaffney. “I am so proud of our customers and team members who ensure members have access to timely, high-quality care.”
For complete results of the Inc. 5000, including company profiles and an interactive database that can be sorted by industry, location, and other criteria, go to www.inc.com/inc5000. All 5,000 companies are featured on Inc.com starting Tuesday, August 13, and the top 500 appear in the new issue of Inc. magazine, available on newsstands beginning Tuesday, August 20.
“One of the greatest joys of my job is going through the Inc. 5000 list,” says Mike Hofman, who recently joined Inc. as editor-in-chief. “To see all of the intriguing and surprising ways that companies are transforming sectors, from health care and AI to apparel and pet food, is fascinating for me as a journalist and storyteller. Congratulations to this year’s honorees, as well, for growing their businesses fast despite the economic disruption we all faced over the past three years, from supply chain woes to inflation to changes in the workforce.”
About Veda
Veda blends science and imagination to solve healthcare’s most complex data issues. Through human-in-the-loop Smart Automation, our solutions dramatically increase productivity, enable compliance, and empower healthcare businesses to focus on delivering care. Veda is simple to use and requires no technical skills or drastic system changes because we envision a future for healthcare where data isn’t a barrier—it’s an opportunity. To learn more about Veda, follow us on LinkedIn.
More about Inc. and the Inc. 5000
Methodology
Companies on the 2024 Inc. 5000 are ranked according to percentage revenue growth from 2020 to 2023. To qualify, companies must have been founded and generating revenue by March 31, 2020. They must be U.S.-based, privately held, for-profit, and independent—not subsidiaries or divisions of other companies—as of December 31, 2023. (Since then, some on the list may have gone public or been acquired.) The minimum revenue required for 2020 is $100,000; the minimum for 2023 is $2 million. As always, Inc. reserves the right to decline applicants for subjective reasons. Growth rates used to determine company rankings were calculated to four decimal places.
About Inc.
Inc. Business Media is the leading multimedia brand for entrepreneurs. Through its journalism, Inc. aims to inform, educate, and elevate the profile of our community: the risk-takers, the innovators, and the ultra-driven go-getters who are creating our future. Inc.’s award-winning work achieves a monthly brand footprint of more than 40 million across a variety of channels, including events, print, digital, video, podcasts, newsletters, and social media. Its proprietary Inc. 5000 list, produced every year since its launch as the Inc. 100 in 1982, analyzes company data to rank the fastest-growing privately held businesses in the United States. The recognition that comes with inclusion on this and other prestigious Inc. lists, such as Female Founders and Power Partners, gives the founders of top businesses the opportunity to engage with an exclusive community of their peers, and credibility that helps them drive sales and recruit talent. For more information, visit www.inc.com.
MAY 6, 2024 – MADISON, WI – Veda, a leading health technology company specializing in provider data solutions, announced today that it has achieved third-party validation from the prestigious Erdős Institute, an independent organization of university PhDs advancing the fields of Data Science and Machine Learning.
Following a blind independent review of Veda’s AI-powered data curation engine—the backbone of its product stack—Erdős Institute researchers found highly accurate provider directory data with certain accuracy scores exceeding 90 percent for critical information like addresses, locations, and phone numbers.
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.
Increased pressure on state and federal lawmakers to protect seniors from surprise medical bills and improve access to mental health treatment has spurred a wave of bipartisan legislation seeking to hold commercial and Medicare Advantage plans accountable.
In a crowded marketplace of payer solutions, the independent validation from Erdős sets a new benchmark for accuracy and compliance.
“Our blinded study found that Veda’s data-driven automation is capable of producing accurate provider data quickly and efficiently,” said Roman Holowinsky, PhD, Managing Director of the Erdős Institute. “Automated, real-time provider datasets like Veda’s can greatly benefit the market and save users a lot of time over manual attestation or intervention.”
The Erdős Institute is a multi-university collaboration focused on helping PhDs get jobs they love at every stage of their career. Founded in 2017, the Institute helps train and place a diverse pool of PhDs through boot camps, workshops, mini-courses, consulting opportunities, and direct employer connections. For more information, visit www.erdosinstitute.org.
About Veda:Veda blends science and imagination to solve healthcare’s most complex data issues. Through human-in-the-loop Smart Automation, our solutions dramatically increase productivity, enable compliance and empower healthcare businesses to focus on delivering care. Veda is simple to use and requires no technical skills or drastic system changes, because we envision a future for healthcare where data isn’t a barrier—it’s an opportunity. To learn more about Veda, visit vedadata.com.
Provider Data Accuracy Verified By Third-Party Audit
Independent Audit Says Veda’s AI Precision Exceeds 90%, Solving Ghost Networks and Payer Network Attestation Challenges
Veda is paving the way for improved data quality, transparency, and accountability in healthcare through continuous validation and testing of our AI-powered solutions. Our scientific rigor commits us to continuously testing our methodology and we believe all AI-powered data solutions should undergo an unbiased independent validation of their data. Learn about our approach to healthcare data challenges and the third-party study performed by Erdős Institute proving our data accuracy.
Provider data management is inherently flawed. The information found in provider directories is often manually updated, the scope of required information keeps expanding and information changes often; practices move, physicians change practices, and contracts between practices and health plans expire. Multiple industry reports state between 20% and 30% of directory information changes annually.
With provider data accuracy rates of 90+%, the study highlights the potential of automation and machine learning in achieving high levels of data accuracy
Reinforcing Commitment to Accountable AI-Powered Solutions in Healthcare
To prove our products and approach to provider directory accuracy are best-in-class, the Erdős Institute conducted a blind third-party audit of Veda’s Vectyr product.
Vectyr is Veda’s curated dataset that powers all our provider data products. With Vectyr’s continuously monitored and validated data, Veda customers can quickly find correct provider information with the confidence of Veda’s optimal accuracy. Many back-office workflows—like directory management, credentialing, and claims—need access to accurate, up-to-date provider information. Our Vectyr product curates data from over 300,000 sources including NPPES, DEA, and state licensing organizations.
Why validate with impartial analysis?
As a leader in the industry, armed with proprietary solutions, we understand the key to solving complex healthcare problems lies in innovative technology. We’re taking the lead again, this time via rigorous third-party validation. By subjecting our technology to impartial analysis we’re taking a step forward in the evolution towards greater transparency.
Employing rigorous methodologies and independent sampling techniques, ensuring an unbiased assessment of provider directory accuracy? Sounds like our scientific approach to everything we do. Additionally, having an independent institute like Erdős conduct the validation study safeguards against potential conflicts of interest and ensures the credibility and integrity of the findings.
Erdõs’ Method for Proving Accuracy
To gauge provider data accuracy, Erdős and Veda simulated a Centers for Medicare & Medicaid Services “secret shopper” audit. Callers attempted to make an appointment on behalf of a patient and collect information that would be necessary to do so: the phone number and location of the provider (which are typically major areas of inaccuracies). The audit provided a measure for the main appointment information and manual research was used to measure more detailed provider information.
To reduce bias in the CMS simulation, Erdős created a sample of NPIs for the measurement. The selection of the sample was aimed to be representative with respect to geographic categories of rural and urban and focused on stratified sampling by specialty. In the total sample, 184 NPIs were called. Of these 184 NPIs, 92 phone numbers and 118 locations could be assessed.
The call-based outcomes found phone and address accuracy are consistent at 90%. Moreover, Erdős found that additional Vectyr fields were also highly accurate. For example, the Vectyr database demonstrated accuracy levels of 99% for fields such as languages spoken. With provider data accuracy rates of 90+%, the study highlights the potential of automation and machine learning in achieving high levels of data accuracy.
Reinforcing Commitment to Accountable AI-Powered Solutions in Healthcare
With a commitment to accountable AI-powered solutions and five approved patents in the industry, we believe all healthcare data vendors need to think more rigorously about what “correct” data means. Attestation does not create quality data. Patients don’t need attested data, they need correct data. Therefore, data vendors must measure performance the same way patients do: making an appointment on the first try, with the correct information. We promise accuracy, and we deliver.
Why It Took Language Processing For AI To Go Mainstream
Scientists and technologists have been using AI for decades. We’ve used it to do complicated calculations and run algorithms and equations that we couldn’t previously conceive of. Your favorite streaming services have been using it for years to recommend shows and movies. But looking at media coverage of the past year, you’d think that AI was just developed. Why is mainstream AI language processing now taking off?
In late 2022, did AI experience an onslaught of media attention that made it seem like it was a new functionality? Why are legislators and regulators now racing to regulate something that has been in existence for about the same length of time as the color TV?
Learning To Learn
Tools powered by AI have essentially learned to learn. The language models we’re all seeing now train themselves with two primary algorithms. First, they can look at any sentence in any context and try to predict the next one.
The other way that language models try to learn is by guessing words in a sentence if some words are randomly removed. These are examples of implicit supervised training, and it’s made possible because these tools use the entire corpus of the internet as training data. This is the actual breakthrough.The other way that language models try to learn is by guessing words in a sentence if some words are randomly removed. These are examples of implicit supervised training, and it’s made possible because these tools use the entire corpus of the internet as training data. This is the actual breakthrough.
Veda’s provider data solutions help healthcare organizations reduce manual work, meet compliance requirements, and improve member experience through accurate provider directories. Select your path to accurate data.
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Standardize and verify unstructured data with unprecedented speed and accuracy.
