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 3.5 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.
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:
Healthcare Business Today: Congress, Bad Data, and Ghost Networks
The Senate Finance Committee has advanced legislation that aims to eradicate ghost networks, a goal that will benefit payers, providers, and patients alike.
As the legislation advances through the halls of Congress, all stakeholders must have a clear understanding of why the bill is necessary and what’s behind all those ghosts anyway.
Ghost networks are provider networks that appear robust and full of available providers but actually contain inaccurate data and, in reality, have limited availability and unreachable providers. These “ghosts” are no longer practicing, not accepting new patients, are not in-network, or have errors in their contact information.
MedCity News: Healthcare Doesn’t Need More Big Tech
Healthcare Doesn’t Need More Big Tech; It Needs Specialized Tech. Byline by Dr. Bob Lindner in MedCity News.
It’s easy to oversimplify and say, “These big tech companies are now doing healthcare and they’re going to solve everything.” But the reality is that often, the solutions are not going to come from big tech.
Just like clinicians who specialize in an area of medicine, healthcare’s tech problems need specialized solutions. That’s because the industry doesn’t have a single general issue to solve, healthcare has many discrete issues to address.
To further complicate things, healthcare is not one industry but many industries under the same umbrella. Clinical care, devices, diagnostics, pharmaceuticals, hospitals, payers, and more each has its own unique challenges and opportunities that need to be addressed with unique solutions.
It’s easy to oversimplify and say, “These big tech companies are now doing healthcare and they’re going to solve everything.” But the reality is that often, the solutions are not going to come from big tech.
These individual problems are being addressed by legions of innovative people working in smaller, more focused organizations where they are experimenting, iterating, pivoting, and getting closer and closer to solutions to the issue they’re addressing. These teams are focusing on singular issues and solutions in a way that bigger, more general tech doesn’t.
To compound the issue, healthcare is an ever-changing industry and requires solution providers to be agile in order to keep up with emerging trends, new discoveries, new regulations, and shifts in patient and provider preferences. These smaller more specialized companies may not have the resources of large tech enterprises; however, they are inherently more adept at quickly iterating solutions, responding to changes, and adapting to evolving needs.
This is why specialized solutions and specialized tech providers are ultimately going to be the problem solvers.
Does this mean that big tech doesn’t have a place? Of course not. Big tech can do what big tech does best: identify, vet, and foster some of these solutions and ultimately scale the right ones.
But what about the funding? These entrepreneurial companies who are developing innovative tools are often start-ups and frequently raising capital at the same time they are building the solution.
A recent Pitchbook report covered by MedCity News included a mixed bag of news for these entrepreneurial companies in the medtech space. The report noted that venture capital funding to medtech appears to have bottomed out in the first quarter of this year and has been ticking slightly upward. That’s the good news. The troubling news is that this year’s medtech funding total may not reach the 2022 funding total of $13.5 billion and certainly won’t even approach the 2021 funding total of more than $19 billion.
In healthcare the stakes are high, and any tech solution needs to operate as a “mission-critical” part of the equation. Think NASA or car safety where there are no margins for error or experimentation like there are if we were building a ridesharing or shopping app. We’re dealing with people’s health and lives on a daily basis. The stakes should be treated as life or death because they are. And the solutions we deploy need to be more than adequate. They need to be infallible.
Is Your Data Partner Ready for New AI Regulations?
How to Find the Right Provider Data Vendor Partnership
It seems that almost every week we see new vendor offerings within the provider data management ecosphere—each claiming to offer a revolutionary way of visualizing your data and making impactful improvements for healthcare. The provider data accuracy challenges that health plans and provider organizations face are vast, so it’s not surprising that new health tech companies are quick to capitalize. Caution: all health tech companies may not be keeping new and developing AI regulations in mind when developing their technology.
All health tech companies may not be keeping new and developing AI regulations in mind when developing their technology.
Looking at the Bigger Provider Data Picture
Quality provider data can help the healthcare industry tackle some of the biggest challenges health plans and provider networks face, like onboarding, credentialing, roster creation, and referrals.
A commitment to improving your provider data can have great ROI potential and long-term impacts on your business and patient populations. But, with all the buzzwords and capability promises from the masses, how do you find the best vendor fit for your business needs?
Necessary Topics to Cover When Vetting Provider Data Vendors
To set you up for partnership success, here are discussion ideas for data vendor conversations:
Find out the vendor’s practices for responsible data collection, storage, and validation. For example, does the vendor keep their data in the U.S.? Veda’s Take: You want to ensure any vendor interacting with critical data is not utilizing offshore, 3rd party data processing centers that can unnecessarily expose data and therefore, providers’ information. Consider this: If patient data is protected from offshore processing vendors via HIPAA and other regulations, shouldn’t the same protections be afforded to provider data? Veda offers comprehensive security and data protection and is HITRUST-certified.
Understand the vendor’s business policies regarding AI. For example, how does an AI vendor utilize Language Learning Models? Veda’s Take: Avoid risk by future-proofing your AI policies. Machine Learning can be a powerful tool, but should be approached thoughtfully and aligned with current and future U.S. legislative requirements such as President Biden’s executive order for the Development and Use of Artificial Intelligence. Warning: fewer companies comply with the proposed regulatory requirements than you may think.
Ask about their reporting and measurement. How does the vendor define accurate data and measure it during and after delivery? Veda’s Take:Data can be compliant but inaccurate and unusable for health plan members who depend on the data to get care. Be prepared to discuss your business goals and thresholds for accuracy—consider going beyond meeting regulatory guidelines. We think healthcare data should truly be considered “accurate” if it meets a member’s needs when accessing care.
Discuss what is necessary for your business success. Can the vendor offer the necessary tools to reach your goals and eliminate what is unproven? Veda’s Take: Don’t be distracted by tools that may not deliver results or provide value for your goals. For example, APIs are often necessary to save time on automation. Therefore, many businesses focus on the ability to have an API connection and the API integration above even the results the product delivers. Or, in another example, the UX and the interface of a product can become a focal point above the actual functionality of the product. If you can’t trust the data and know how to interpret and use it, then connectivity and appearances don’t matter.
Determine what happens first. Can a vendor partner prioritize your specific business requirements? Veda’s Take: All businesses have different objectives and these goals greatly impact priorities. Your vendor should clearly articulate what needs to happen first, upon implementation of the product, to realize immediate value and reach success. There is no one-size-fits-all solution when working with a provider data vendor. Before integration and during the initial conversations is the perfect time to establish an approach to prioritization within business rules.
Get familiar with the training process. What does the implementation, training, and delivery process look like for your AI data vendor? Veda’s Take: How a provider data vendor plans to work with you, and how they plan to train others in your organization, is key to partnership success. Beyond day-to-day use of the tools, how does the vendor recommend using the data and applying the findings? Who should be trained on what tasks? Clear and concise preparation will ready everyone in your organization.
Ensure any vendor interacting with critical data is not utilizing offshore, 3rd party data processing centers that can unnecessarily expose data and therefore, providers’ information.
Once you have a solid understanding of how a potential partner tackles the above objectives, only then can you capitalize on a business case for building a collaborative partnership with a provider data vendor.
A ghost network is a provider network that appears robust and full of available providers but actually contains bad data and thus, much more limited availability and unreachable providers. These “ghosts” are no longer practicing, not accepting new patients, are not in-network, or have errors in their contact information.
Imagine using a “Find A Doctor” online tool to pick an in-network doctor in the specialty you seek, but when you call to book an appointment, you discover the provider is no longer practicing. That’s a ghost.
“ [Ghost networks are a] pervasive issue in the American health care system. A three-phase study of the accuracy of Medicare Advantage directories, which included over 15,000 providers, found that 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.”
While behavioral health networks are often cited for inaccuracy and the mental health crisis in America has brought adequate network issues to light, inaccurate directories are a systemic issue prevalent across all provider types. All directories have “ghosts.”
How do ghost networks inhibit care?
Besides adding to patient frustration, ghost networks prevent patients from accessing the care they need. Impactful stories, like this one from the Yale Law & Policy Review, shed light on the millions of people dealing with the repercussions of inaccurate provider directories each year:
“KC, who manages her brother’s mental health care, gave up on trying to find an in-network psychiatrist because calling potential providers was taking up so much of her time that it was more cost effective to pay out-of-network rates. “Ironically, I can’t imagine my brother or others in his situation being organized and effective enough to be able to make all these calls and keep track.” [Yale Law & Policy Review]
Additionally, ghost networks have inhibited care by leading to unexpected medical bills. “When Americans are purchasing and using their health insurance, they have the right to know whether their doctors are covered by that plan,” said Ron Wyden (D-Ore). “Too often, seniors and families get health care whiplash when they sign up for a plan only to find out that their preferred doctor is out-of-network, or it’s impossible to find a covered mental health care provider.”
How does the REAL Health Providers Act tackle the issue of ghost networks?
The legislation aims to combat the ghost network problem by, among other things, requiring Medicare Advantage (MA) health plans (beginning with plan year 2026) to verify their provider directory data every 90 days and, if necessary, update that information.
If a health plan cannot verify the data, the plan must indicate in its directory that the information may not be up to date.
A health plan must also remove a provider within 5 business days if the provider is no longer participating in the plan’s network.
If a patient obtains care from an out-of-network provider that a health plan’s directory indicated was in-network at the time the appointment was made, the plan may only charge that patient in-network prices.
The legislation also requires MA health plans to analyze the accuracy of their provider data on an annual basis and submit a report to HHS/CMS with the results of that analysis. HHS/CMS will use this information to publish accuracy scores for each plan’s provider directory.
How does Veda banish ghost networks?
Veda has been at the forefront of efforts to eradicate “ghost networks” in the U.S. for years. Veda understands that progress on this front is essential to connect patients to the critical care they need and ensure that individuals are not burdened with unexpected healthcare costs.
Veda takes a one-two-punch approach to eradicating ghost networks. Veda leverages its patented technology and innovative solutions to first identify where the “ghosts” reside in provider directories and then fills in the gaps left behind once the ghosts have been removed. Using a mix of AI technology, smart automation, and machine learning, Veda’s provider data is proven accurate.
Find the Ghosts
Veda’s Quantym platform identifies the errors in a provider directory
High-volume audit solution that delivers comprehensive, real-time scoring of provider data quality to identify bad data and significantly improve provider directory accuracy
Fill the Gaps with Accurate Data
Veda’s Vectyr tool supplies accurate data to replace the bad data
The most up-to-date, comprehensive, and accurate source of data on healthcare providers, groups, and facilities on the market
Veda understands that ghost networks will not be eliminated by the manual attestation methods of the past. Manual verification is labor-intensive, expensive, subject to human error and time and again, it’s proven ineffective. The fact is, “attested” data is frequently not “correct” or “accurate.”
Veda’s technology takes provider attestation out of the equation and, in doing so, can more accurately identify where ghost networks exist (by pointing out inaccuracies in the health plans’ provider directory data) and provide solutions to the ghost network problem (by filling in the gaps that are created when the bad data is removed).
It’s time to let technology like Veda’s solve the ghost network problem once and for all. Give your members the accurate information they need to make an appointment for care on their first try. Contact Veda for a free data assessment.
In this episode of Leaders of B2B, Meghan Gaffney, CEO and co-founder of Veda, offers an in-depth perspective on the evolving realm of artificial intelligence in healthcare. With her extensive experience, Meghan underscores the transformative impact of implementing AI solutions in medical diagnostics and patient care pathways.
Tune in to learn why a diagnostic approach is essential for effective data management across industries, to identify and address critical issues systematically.
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.
Velocity
ROSTER AUTOMATION
Standardize and verify unstructured data with unprecedented speed and accuracy.
