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.
Dr. Bob Lindner is the Chief Science and Technology Officer at Veda, a company addressing provider directory data challenges.
It’s no surprise to anyone who works with data—it’s messy. In every industry and every business, there are data anomalies and issues that can impact the story data tells. If we have any hope of improving data practices and making collected data truly actionable, we first have to acknowledge its limitations and then explore modern solutions for improving it.
Bad Data Is The Norm
With the new federal administration exploring cost-cutting measures and releasing data nearly daily, a specific example caught my eye—it was a Social Security disbursements by age graph, with the data suggesting 210 year olds are receiving Social Security entitlements. As a data scientist who has been working with healthcare data for over 10 years, this graph wasn’t shocking to me.
I recently saw one dermatologist who was practicing at 20 different variations of one address; imagine the extra legwork required by a patient to find out where you are booking an appointment. Or how about two providers with the exact same name but one is a veterinarian on the West Coast and the other is a physician in New York? There is state licensing info for both of them, but the only one with a federal National Provider Identifier (NPI) is the veterinarian. These are complex data problems occurring every day.
Data engineers know that a lot of data in every industry is collected manually, and this often introduces errors that are quickly propagated and magnified throughout downstream processes. In fact, most data systems in the modern economy, all around the globe, have shockingly out-of-date practices. With a spotlight on data issues right now, it’s important to dig deeper and examine data processes to have any hope of modernizing databases and making data functional.
Simple, Clear Provider Data For Fast Decision-Making
Reliable provider data enables confident goal attainment in your healthcare business, streamlining operations and facilitating the pursuit of new strategic ventures.
Reliable Provider Data for Digital Health Organizations
In this scenario, your digital health organization aims to connect patients to behavioral health specialists within 24 hours of contact. How can you accomplish this goal? By finding and partnering with plenty of providers who perform online appointments and having reliable data to refer patients quickly.
Watch this example of how Vectyr Profile Search identifies specific providers who conduct telehealth appointments:
Then, you can compare your search findings with your current provider database to easily contract with those you’re not currently working with. The best part? Peace of mind that the information is up to date and accurate—no more making several dead-end calls when one will do.
Pro Tip: Provider profiles are detail rich. Look for providers that are accepting many different types of insurance if that is a plus for your organization.
Reliable Provider Data for Health Plans
Let’s say you want to recruit more pediatric cardiologists for your health plan in Illinois to carve out a competitive advantage and meet your members’ needs.
Use Vectyr Profile Search to curate an exportable list of providers to contact:
The best part? Addresses are practice locations and not just P.O. boxes, so you can be sure your network reflects where providers are actually practicing.
Pro Tip: Check provider profiles for languages, cultural competencies, licenses, and more. By aligning patients with the right providers, you’ll enhance care quality and drive better retention.
Reliable Provider Data for Provider Organizations
What about when you’re onboarding new providers and want to enroll and credential them faster? You can streamline the onboarding process with the initial data entry of provider information, ensuring accuracy from the start.
Watch as the Vectyr Profile Search serves up-to-date and complete information—no more costly and time-consuming manual searches and verification of credentials:
Pro Tip: With Vectyr’s detail-rich profiles, you can spot or bulk-check credentials to ensure enrollment is completed correctly the first time without relying on self-reported data.
Try Our Provider Data
Veda’s Vectyr Profile Search features a comprehensive and constantly growing dataset that drives value for many healthcare businesses. Our data offering is always expanding to ensure that no matter how deep the use case, we have the data to support it.
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.
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.
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.
Q&A with Bob Lindner on why sustainably-fed AI models are the path forward
As an AI company powered by our proprietary data training AI models, the article, “When A.I.’s Output Is a Threat to A.I. Itself,” in the New York Times caught our eye. Illustrating exactly what happens when you make a copy of a copy, the article lays out the problems that arise when AI-created inputs generate AI-created outputs and repeat…and repeat.
Veda focuses on having the right sources and the right training data to solve provider data challenges. A data processing system is only as good as the data it’s trained on; if the training data becomes stale—or, is a copy of a copy—inaccurate outputs will likely result.
We asked Veda’s Chief Science & Technology Officer, Bob Lindner, PhD, for his thoughts on AI-model training, AI inputs, and what happens if you rely too heavily on one source.
Veda doesn’t use payers’ directories as inputs in its AI and data training models. Why not?
At Veda, we use what we call “sustainably-fed models.” This means we use hundreds of thousands of input sources to feed our provider directory models. However, there is one kind of source we don’t use: payer-provided directories.
Provider directories are made by health plans that are spending millions of dollars of effort to make them. By lifting that data directly into Veda’s AI learning model, we would permanently depend on ongoing spending from the payers.
We aim to build accurate provider directories that allow the payers to stop expensive administrative efforts. A system that depends on payer-collected data isn’t useful in the long term as that data will go away.
The models will begin ingesting data that was generated by models and you will experience quality decay just like the New York Times article describes. We use sustainably sourced inputs that won’t be contaminated or affected by the model outputs.
Veda does the work and collects first party sources that stand independently without requiring the payer directories as inputs.
Beyond the data integrity problems, if you are using payers’ directories to power directory cleaning for other payers, you are effectively lifting the hard work from payer 1 and using it to help payer 2, potentially running into data sharing agreement problems. This is another risk of cavalier machine learning applications—unauthorized use of the data powering them.
Can you give us an analogy to describe how problematic this really is?
Imagine we make chocolate and we are telling Hershey that they should just sell our chocolate because it’s way better than their own. We tell them, “You could save a lot of money by not making it yourselves anymore.”
However, we make our chocolate by buying a ton of Hershey’s chocolate, remelting it with some new ingredients, and casting it into a different shape.
In the beginning, everything is fine. Hershey loves the new bar and they’re saving money because we’re doing the manufacturing. Eventually, they turn off their own production. Now, with the production turned off, we can’t make our chocolate either. The model falls apart and in the end, no one has any chocolate. A real recipe for disaster.
CMS 2025 Final Rule: New Behavioral Health Requirements for MA Plans
Mental Health Awareness Month and Summary of New CMS Final Rule
Fitting for Mental Health Awareness Month, the Centers for Medicare & Medicaid Services (CMS) recently released its 2025 Final Rule that, among other things, aims to improve access to behavioral health providers for Medicare Advantage members.
Ready to learn about the CMS 2025 Final Rule and Veda’s strategic approach to its behavioral health network requirements?
The CMS 2025 Final Rule significantly expands the behavioral health network requirements for Medicare Advantage (MA) health plans. As reported by Fierce Healthcare, all Medicare Advantage plans will likely see increased administrative burdens due to the behavioral health network expansion requirements.
Not only is Veda a proven and trusted partner for achieving compliance with CMS requirements, Veda’s solutions are unrivaled in their ability to help health plans verify, expand, improve, and map their behavioral health networks.
Here are the behavioral health requirements covered in the Contract Year 2025 Medicare Advantage and Part D Final Rule and Veda’s approach:
New “Outpatient Behavioral Health” Category Added to Network Adequacy Evaluations
Veda’s curated dataset includes accurate information for even the hardest-to-validate specialties, credentials, and facility types— requiring no additional burden on health plans and health systems.
Wide Range of Specialists Included in “Outpatient Behavioral Health” Category
More specialties and outpatient care classifications were added to solve behavioral health provider shortages. The specialists in the new “Outpatient Behavioral Health” category include MHCs, MFTs, Opioid Treatment Program providers, Community Mental Health Centers, addiction medicine physicians, nurse practitioners (NPs), physician assistants (PAs), and clinical nurse specialists (CNSs).
What Can Veda Do?
Veda offers current and complete outpatient behavioral health provider profiles, on each of the specialties above, containing the necessary details to contract with providers and meet adequacy requirements.
Skill Sets of Certain Behavioral Providers Must Be Verified
To address concerns that NPs, PAs, and CNSs have the requisite skills and training to address the behavioral health needs of plan members, MA plans must independently verify that the providers they are adding to their network have furnished (or will furnish) behavioral health services to at least 20 patients within a 12-month period using information such as (i) MA plan’s claims data, (ii) prescription drug claims data, (iii) EHRs, or (iv) similarly reliable data.
What Can Veda Do?
Veda partners with customers to develop effective ways to complete the requisite independent verifications, utilizing the best data available in the industry. Our proprietary Vectyr database is updated 24/7 to include the most up-to-date, complete, and accurate provider data in the behavioral health space. This data can help MA plans verify behavioral health providers and comply with CMS’s requirements.
“Outpatient Behavioral Health” Facility Added to Time & Distance Standards and Telehealth Specialty Requirements
CMS now includes the “Outpatient Behavioral Health” facility specialty in the list of specialty types that will receive a 10% credit toward meeting time and distance standards. Additionally, MA plan’s networks must include at least one telehealth provider within the Outpatient Behavioral Health specialty.
What Can Veda Do?
Veda’s nationwide database can identify telehealth providers, flag whether a provider accepts patients at their practice location, and help source providers to meet the new behavioral health requirements. We can also help MA plans ensure compliance with CMS’s time and distance standards.
Veda is Equipped to Meet The Needs Introduced By The 2025 CMS Rule Changes
Veda excels at helping health plans and systems connect members with behavioral health services, treatment facilities, and telehealth providers. The 2025 MA rule changes are an opportunity for health plans and health systems to explore how Veda can help them expand, improve, and map their behavioral health networks and verify the claims data for behavioral health providers.
Veda’s solutions can help connect members to quality behavioral health services more quickly, efficiently, and at less cost than the traditional methods relied on in the past. Armed with the most accurate provider data available, Veda’s solutions contribute to positive member experiences while helping people find the right care for their behavioral health needs.
Rural healthcare challenges: How bad data deepens disparities
In rural healthcare, timely access to crucial mental healthcare and other specialized services presents a significant challenge. Over the last decade, numerous rural hospitals have shuttered, with more at risk of closure due to staffing shortages, declining reimbursement rates, diminished patient volume, and challenges attracting talent. The answer to the challenges in rural healthcare is to get more data.
With very few options for specialty and subspecialty providers, rural patients often endure long journeys for necessary care. According to a Pew Research Center report, the average drive to a hospital in a rural community is approximately 17 minutes, nearly 65 percent longer than the average drive time in urban areas. Such systemic failures not only exacerbate disparities but also challenge the very foundation of patient care.
A functioning rural health system relies on legions of specialty care doctors conducting outreach visits across vast geographic areas. In principle, this approach presents an efficient means to provide rural patients with access to specialty care, eliminating the need for extensive travel to major urban centers. However, the persistence of inaccurate data poses a significant barrier to achieving comprehensive access to specialty care in rural regions.
Discover Bob Lindner’s take on how bad data exacerbates rural healthcare challenges and impacts patients on Chief Healthcare Executive.
CMS Directory Accuracy Audits and Sanctions: Achieving True Directory Accuracy
The Centers for Medicare & Medicaid Services (CMS) regularly audits health plan programs and provider directories. All health plans providing services to Medicare and Medicare Advantage members are nearly guaranteed to be audited by CMS. By definition, the CMS directory accuracy audits aim to improve patient access and experience. Additionally, many standards for provider directories and network adequacy are developed based on CMS regulations.
Veda works with health plans to prepare for CMS audits and then interpret and address their audit results.
Unfortunately, health plans’ directory accuracy claims may not match with CMS’s findings—in the case of lower accuracy discovered, the plans may receive CMS sanctions and fines. Why are the directory accuracy rates differing and what can be done to reconcile the accuracy rates?
Why do accuracy rates determined by CMS and health insurance providers differ?
Many factors determine accuracy rates in provider directories. CMS zeroes in on specific fields (such as name, address, and phone number) for determining accuracy while insurance providers may go further in determining accuracy (such as specialty fields). Here are the reasons why updating directories while maintaining high accuracy levels—is an uphill battle:
Many systems rely on heavily manual workflows, causing delays in data updates. Human error degrades data quality
Provider abrasion and long turnaround times are present when constantly attesting to information
Phone calls, even when used for verification, have a 20% variability rate. Meaning, if your call center has two people call the same provider twice in one day, you’ll get a different answer 20% of the time
Why Does CMS Audit Provider Directories?
A few years ago, a CMS Online Provider Directory Review Report looked at Medicare Advantage directories and found that 52% had at least one inaccuracy. The areas of deficiency included such errors as:
The provider was not at the location listed,
The phone number was incorrect, or
The provider was not accepting new patients when the directory indicated they were.
And, despite provisions in the 2021 No Surprises Act legislation, new research has shown that directories remain inconsistent, one study citing “of the almost 450,000 doctors found in more than one directory, just 19% had consistent address and specialty information.” (Let alone complete accurate information including phone numbers.) The audits continually find inaccuracies as the years go on.
How Do Health Plans Prepare for CMS Audits?
Traditional approaches to audit preparation include phone calls and mock audits.
Phone Calls
Pricey and oftentimes inconsistent, call campaigns amount to hundreds of thousands of phone calls being made every day to check data.
Mock Audits
Mimics the audit experience with sample sets of small amounts of data but are not reflective of the overall directory.
These approaches are not sufficient for achieving successful audit results.
What Is CMS Looking for in Audits of Directories?
Not all information is equally important during an audit. The scoring algorithm assigns different weights for fields so if you’re starting somewhere, Veda recommends starting with the key areas of focus: Name, Address, Phone, Speciality, and Accepting Patients.
Addressing the most important data elements with quality validated data will move a health plan towards audit success.
How Veda’s Solutions Interpret and Address CMS Audit Results
CMS performs audits to advocate for members and better outcomes so interpreting audit results is the perfect place to get started with directory updates. Our research shows that when it comes to what members care about it is pretty simple: Choice, Accuracy, and Accessibility —meaning the ability to schedule, with their preferred provider, easily and quickly. On the first try.
Where to Start For CMS Audit Success
Many health plans are realizing that achieving directory accuracy and audit success is not a one-and-done. An ongoing surveillance approach is needed to confidently prepare and ultimately, achieve success in an audit.
Veda’s approach consistently evaluates the directory to provide ongoing insights. For example, we leverage technology to identify and prioritize providers for updates who haven’t attested recently, to ensure they have a data trail that supports their current status and information in a directory. By prioritizing bad data, this audit strategy is efficient and effective.
AI for Amateurs: Questions Answered by Veda’s AI Experts
Everything You Always Wanted to Know About AI But Were Afraid to Ask
Feeling overwhelmed by AI? You’re not alone. At Veda, we take complicated data and we make it simple. Now we’re here to explain the basics of the complicated topic of artificial intelligence. If you’re feeling like everyone is diving into the deep end with AI knowledge but you’re still in the kiddy pool, this is for you.
Impossible to miss, 2023 is synonymous with the year AI debuted to the masses. AI capabilities have brought up questions in every industry, including healthcare. Your organization will likely find itself navigating the risks and rewards associated with healthcare AI in the coming year.
But, let’s start with a question you’re too afraid to ask at the company meeting: What is AI? Like, really. We’ve found a lot of false information out there and we’re here as a trustworthy source you can pull information from.
Why is Veda a Trusted Source?
As pioneers who have used AI technology since our founding, we’re passionate advocates for AI and want to ensure everyone else feels comfortable with it too.
Want our credentials? Our technology and data science team has 80 years of collective AI experience. Veda co-founder and Chief Science and Technology Officer, Bob Lindner, is the author of five technology patents on AI, entity resolution, and machine learning. Bob also has over 16 years of experience writing and publishing scientific and academic papers in the artificial intelligence field.
Backed by extensive experience and science, we’re the AI experts.
What is AI?
OFFICIAL ANSWER: Artificial intelligence Is a field of study that focuses on how machines can solve complex problems that usually involve human intelligence.
AI is not one specific tool. It is a field of study. With AI’s computing power, computers can make decisions and predictions, and take actions. An algorithm recommending which movie you should watch next is an AI action.
VEDA’S TAKE: So why does this matter? Why is AI important? By freeing up human resources, AI can reduce manual and often error-prone tasks. Freeing up people so they have the time to do the things they do best, that’s the power of AI.
What is machine learning?
OFFICIAL ANSWER: Machine learning is a sub-field of AI and focuses on algorithms that train models to make predictions on new data without being explicitly programmed. Meaning, the machine learns the way humans do, with experience.
Note: In recent years, some organizations have begun using the terms artificial intelligence and machine learning interchangeably.
Instead of learning step by step, computers using machine learning can learn through trial and error and lots of practice. What does machine learning practice on? Lots and lots of data. The data can be things like images, video, audio, and text. When fed loads of data, machine learning will recognize patterns and make predictions based on these patterns.
AI is not one specific tool. It is a field of study. With AI’s computing power, computers can make decisions and predictions, and take actions.
VEDA’S TAKE:Veda uses machine learning, and therefore, AI for the healthcare industry, every day. For what exactly? To power our provider information. Veda uses machine learning to:
Determine correct addresses and phone numbers
Transform provider rosters from one format to another
Simulate an experience a member may have when booking an appointment
With a patented training data approach, our machine learning can make predictions on a wide variety of new data (that it has never seen before in the training set).
Feeling good about AI and machine learning? Further your AI understanding with these blogs:
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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