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Veda First to Achieve Third-Party Data Validation from Erdős Institute, Reinforcing Commitment to Accountable AI-Powered Solutions in Healthcare

Independent Audit Says Veda’s AI Precision Exceeds 90%, Solving Ghost Networks and Payer Network Attestation Challenges

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 “While outdated methods for maintaining accuracy and compliance continue to fail, Veda is proof positive that automation is an effective and necessary approach to supporting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.”

To access a copy of the whitepaper, please visit vedadata.com/case-studies/erdos-white-paper-vedas-ai-precision-exceeds-90.

About Erdős Institute:

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.

We promise accuracy, and we deliver.

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OVERVIEW

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.

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Overview

Provider Data Inaccuracy

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

Inaccurate provider directories and networks full of “ghosts” (or unavailable providers) aren’t just frustrating to patients making appointments, they’re making waves among policymakers. The bipartisan Requiring Enhanced & Accurate Lists of (REAL) Health Providers Act, introduced by U.S. Senators and Representatives calls to eradicate ghost networks that are impacting patients nationwide and states across the U.S. have followed suit with their own proposed regulations.

Vectyr Curated Dataset

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.

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OVERVIEW

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.

Read Chief Science & Technology Officier Dr. Bob Lindner’s entire article on Mainstream AI Language Processing.

Veda Technology

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.

Connect with Dr. Bob Lindner on LinkedIn. Read more from Bob with Automation, Machine Learning, and the Universe: Q&A with Veda’s Chief Science and Technology Officer and Co-founder, Dr. Bob Lindner.

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:

In anecdotes shared by those in the industry, 20–30% of providers are unresponsive during attestation requests. Attestation is not a sufficient data-collection tool and does not result in data quality.
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.

Diagnose your provider directory and fix critical data errors ahead of CMS audits with Veda.

Addressing Challenges in Rural Healthcare Data

HEALTHCARE BUSINESS TODAY — Specialty and subspecialty healthcare services are less likely to be available in rural areas and are less likely to include highly sophisticated or high-intensity care. This exacerbates problems for rural patients seeking specialized care who must travel significant distances for treatment.

It comes as no surprise a 2019 policy brief from the University of Minnesota Rural Health Research Center found that 64% of surveyed rural health clinic staff members reported difficulties finding specialists for patient referral.

A functioning rural health system relies on legions of specialty care doctors doing outreach visits across a wide geography. In theory, that’s an effective way to ensure that rural patients have access to specialty care without traveling to a major metro area. But, bad data is keeping us from achieving complete access to specialty care in rural areas and experts across industries are weighing in on the issue.

Read Chief Science & Technology Officier Dr. Bob Lindner’s entire article addressing rural healthcare data challenges.

Progress in healthcare data quality

Health tech companies have attempted to solve provider data inaccuracy problems with a number of products, platforms, and integrations. No solutions have been able to offer members the ability to easily book an appointment armed with accurate information.

While many solutions focus on gathering all data sources available to identify providers, most don’t have the ability to effectively clean up those databases. That’s where we comes in. Veda is leading efforts to eradicate rural healthcare data challenges in the U.S. Discover how our technology connects patients to the critical care they need while ensuring that individuals are not burdened with unexpected healthcare costs.

Veda Technology

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.

Read Veda CEO Meghan Gaffney’s entire article in Healthcare Business Today.

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.

READ FULL MEDCITY NEWS ARTICLE

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.

Our healthcare system is built on a series of complex requirements and regulations that conventional technology solutions aren’t built for. Patient data privacy, regulatory compliance, interoperability, and the sensitivity of medical information call for a specialized set of solutions. A solution for a payment issue isn’t the same as a solution for patient records or network construction, telehealth, provider data, or a condition-specific issue.

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.

Need more ideas on what to ask a potential provider data vendor? See Veda’s Six Questions to Ask Your Provider Data Vendor.

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.

Stay up-to-date on industry trends and discover a wealth of resources on provider data management, roster automation, and AI.

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.

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What We Offer

Veda First to Achieve Third-Party Data Validation from Erdős Institute, Reinforcing Commitment to Accountable AI-Powered Solutions in Healthcare

Independent Audit Says Veda’s AI Precision Exceeds 90%, Solving Ghost Networks and Payer Network Attestation Challenges

We promise accuracy, and we deliver.

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

Provider Data Inaccuracy

Reinforcing Commitment to Accountable AI-Powered Solutions in Healthcare

Vectyr Curated Dataset

Why validate with impartial analysis?

Erdõs’ Method for Proving Accuracy

Reinforcing Commitment to Accountable AI-Powered Solutions in Healthcare

Why It Took Language Processing For AI To Go Mainstream

Learning To Learn

Rural healthcare challenges: How bad data deepens disparities

CMS Directory Accuracy Audits and Sanctions: Achieving True Directory Accuracy

Why do accuracy rates determined by CMS and health insurance providers differ?

Why Does CMS Audit Provider Directories?

How Do Health Plans Prepare for CMS Audits?

What Is CMS Looking for in Audits of Directories?

How Veda’s Solutions Interpret and Address CMS Audit Results

Where to Start For CMS Audit Success

Addressing Challenges in Rural Healthcare Data

Progress in healthcare data quality

AI for Amateurs: Questions Answered by Veda’s AI Experts

Everything You Always Wanted to Know About AI But Were Afraid to Ask

Why is Veda a Trusted Source?

What is AI?

What is machine learning?

Healthcare Business Today: Congress, Bad Data, and Ghost Networks

MedCity News: Healthcare Doesn’t Need More Big Tech

Is Your Data Partner Ready for New AI Regulations?

How to Find the Right Provider Data Vendor Partnership

Looking at the Bigger Provider Data Picture

Necessary Topics to Cover When Vetting Provider Data Vendors

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DIRECTORY ANALYSIS

Resources & Insights

The Strategy of Health Podcast: Access & Accuracy – Healthcare’s Data Challenge

Provider Directory Regulation Alert

Bad Data Exists. What Can AI Do About It?

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