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Q&A with Veda’s Co-Founders: Patented AI Approach for Provider Data

Veda recently announced that, with its tenth patent granted by the United States Patent and Trademark Office, it holds the most AI and machine learning patents in the healthcare data industry. Below is a Q&A with Veda’s Co-Founders, Meghan Gaffney, CEO, and Dr. Bob Lindner, Chief Science & Technology Officer, about Veda’s patented AI technology.

Why did you patent your AI?

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 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 be able 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.

veda patents

Why do you need AI to solve provider data problems?

Bob: We believe only AI can solve the complexities of the provider data problem in the U.S. If manual solutions could successfully process provider data, it would have worked by now. We wouldn’t have legislation, lawsuits, and increasing amounts of member dissatisfaction across the healthcare industry.

Meghan: Veda’s AI can cut through data barriers and ensure that people can access care when they need it the most. That’s why we founded Veda—because everyone deserves access to accurate, up-to-date information that empowers them to get the care they need.

What are the risks of using AI in healthcare and how can they be mitigated?

Meghan: While everyone is looking to AI and automation for solutions, in healthcare the AI isn’t living up to the hype. In a race to reduce costs, many have lost sight of the problem they are trying to solve and have left out foundational components of professional services, actual results, and rigorous testing. In fact, I think the irresponsible development of some AI tools could negatively impact the companies that are taking a transparent and tested path. 

For instance, imagine a business trying a new product for the first time, and it doesn’t go well. It breaks, it’s costly, and leaves a negative impression. After that bad experience, you might be reluctant to try another product in that category. This can happen with AI too—if one company delivers poor results, people might dismiss AI solutions altogether and revert to outdated methods, which ultimately hurts innovation.

Bob: We succeed with AI when it is effective, robust, and focused on responsibly making an impact. While there is a risk posed by poorly designed and underperforming tools, I see an opportunity for Veda to prove our integrity to the industry. We’re proud to showcase our patented AI and machine learning solutions, which were developed and tested with an unwavering commitment to scientific rigor and ethical, security-forward principles.


Ready for Veda’s provider data solutions? Contact us.

Veda Announces Tenth AI and Machine Learning Patent

Proprietary Technology Leads the Health Data Industry

MADISON, February 6, 2025 – Veda Data Solutions, Inc. (Veda), a healthcare technology company solving complex provider data challenges, announced its tenth patent has been granted by the United States Patent and Trademark Office, with four patents secured in the last four months.

“Our provider data solution is the only one of its kind,” said Veda Chief Science & Technology Officer and patent author Dr. Bob Lindner. “The 10 patents work in tandem to deliver automation, speed, and provider data accuracy that others can’t match. Our IP portfolio spans the entire operational pipeline from web-scale data collection, entity resolution, automatic semantic recognition and transformation, accuracy modeling, and human-in-the-loop interactivity.”

Why did Veda patent its AI technology?

Veda is committed to building responsible and transparent AI. The patent process is rigorous and ensures inventors are both creating technology with unique value while also openly sharing their research to fuel an innovation ecosystem.

What does Veda’s patented AI technology do?

Veda’s patented technology definitively solves provider data problems plaguing the healthcare industry.

Veda offers the optimal solution for automatic mass-scale demographic information management along with automatic roster ingestion, directory accuracy, network construction, and network adequacy optimization.

Veda’s best-in-class product leaves behind flawed, biased, and outdated notions of “sources of truth” and attestation, instead leaning on artificial intelligence and sound scientific design to produce reliable and reproducible results. 

Is Veda’s AI secure?

Veda’s AI systems are HITRUST-certified and built entirely in-house. Veda’s implementation of its patented technology is bias and hallucination-free with all customer data and services fire-walled within the United States for maximum security.

At Veda, provider data is treated with the same reverence for security and privacy that is required for patient data.

What is next for Veda’s proprietary innovations?

With 14 more pending patents, Veda continues innovating to remain the optimal solution for provider data roster automation and data accuracy scoring. 

“Veda’s technology isn’t only patented, it’s powerful. Innovated precisely for healthcare organizations and their unique data problems, our patents are essential to the delivery of fast and accurate data to Veda’s customers,” said Veda CEO Meghan Gaffney. “Veda was the first to tackle the provider roster data problem successfully and continues to develop innovative solutions in healthcare data today. With our patented approach, organizations can dramatically reduce operating costs by automating complex business rules for data extraction, transformation, and loading.”


About Veda
Veda blends science and imagination to solve healthcare’s most complex data issues. Using AI, machine learning, and human-in-the-loop automation, our solutions dramatically increase productivity, enable compliance, and empower healthcare businesses to focus on delivering care. Veda’s platforms are simple to use and require no technical skills or drastic system changes because we envision a future for healthcare where data isn’t a barrier—it’s an opportunity. To learn more about Veda, visit vedadata.com and follow us on LinkedIn.

veda patents

New in 2025: CMS Standards for Initial Appointment Wait Times

How to achieve compliance with Centers for Medicare & Medicaid Services (CMS) wait time standards

The healthcare landscape just got more demanding. Starting January 1, 2025, Qualified Health Plan (QHP) issuers on the federal exchanges must meet strict new standards for initial appointment wait times. This means proving that 90% of the time, new patients can schedule primary care and behavioral health appointments within 15 and 10 days, respectively. Fail to comply? You’ll need to expand your network.

CMS wait times standard appointment times grid

Decoding the New Appointment Wait Time Standards

CMS is tackling the growing problem of long wait times head-on. The new standards, which must be assessed by a third party unaffiliated with the health plan (more on that below), require QHPs to demonstrate timely access to care. Here’s a breakdown of the standards:

  • Primary Care: Appointments within 15 days
  • Behavioral Health: Appointments within 10 days
  • 90% Compliance Target: Health plans must meet this target with a confidence level of +/- 5% or face mandatory network expansion.

Specialists will be surveyed in future years and that standard will be 30 days.

The Stakes Are High: Why CMS is Prioritizing Wait Times

Long wait times create barriers to care, frustrate patients, and can have serious consequences for health outcomes. As the media has reported, in some cases, patients are not able to schedule an appointment for 6-12 months from the first time they reach out for care.

CMS is “particularly concerned with the ability of new patients to schedule appointments with in-network providers” and secret shopper calls, from independent third-party entities, must take place from January to May of this year.

CMS is taking action to address this issue, recognizing the urgent need for timely access to both primary care and behavioral health services.

The CMS wait time requirements will be assessed during annual secret shopper surveys conducted by independent third-party entities hired by the health plans. The standards are detailed in CMS’ Appointment Wait Time Secret Shopper Survey Technical Guidance for Qualified Health Plan (QHP) Issuers in the Federally-facilitated Exchanges (FFEs).

The completed surveys must be submitted to CMS with compliance rates, percentage of non-responsive providers, and contracts with third-party entities. Submissions are due in mid-June.

Veda: Your Partner in Achieving and Exceeding CMS Compliance

Veda’s proprietary provider data technology can help QHPs meet and exceed the wait time standards issued by CMS.

The first step in ensuring you can deliver on wait time requirements is auditing your directories for accurate provider-at-location data and keeping those records current.

Then, Veda can help you identify and strategically fill gaps in your network for known provider needs (from an adequacy perspective), particularly PCPs and Telehealth. This will ensure adequate access to care across all specialties and service areas.

Veda’s Dashboard: Your CMS Audit Command Center

Veda’s intuitive dashboard provides a clear, real-time view of your provider data accuracy. View your performance through a simulated CMS audit score, identify areas for improvement, and take proactive steps to ensure compliance.

Offering profiles on providers and roster automation, Veda offers true directory accuracy for providers, facilities, and groups. Veda’s solutions can help you not only meet the new CMS wait time standards but exceed them, all while enhancing your member satisfaction and solidifying your position in the market.

Don’t wait for secret shopper surveys to reveal gaps in your network. Request a demo from Veda today and ensure you are ready for this new era of provider data accuracy. By identifying and addressing gaps in your network with Veda’s powerful analytics, you are ensuring adequate access to care across all specialties and service areas.

Perfecting Provider Directory AI Modeling

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.

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.

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.

Health Tech Solution Veda Ranks No. 417 on the 2024 Inc. 5000

With Three-Year Revenue Growth of 1,066 Percent, Veda Ranks No. 417 Among America’s Fastest-Growing Private Companies

August 13, 2024 – Veda Data Solutions, healthcare’s leading AI provider data platform, was named No. 417 on the 2024 Inc. 5000 list revealed today.

Among software companies, Veda was ranked 47th and the Madison, Wis.-based company was the 4th highest-ranked company on the list from Wisconsin. This is Veda’s second consecutive year on the Inc. 5000 list. 

“At Veda, we are committed to improving the healthcare experience by creating the most accurate, curated provider data on the market and partnering with health plans and provider organizations to ensure their members have seamless access to appropriate care,” said Meghan Gaffney, CEO and co-founder of Veda. “Being named in the top 10 percent of high growth companies validates our solution and reflects the value our customers place on member satisfaction, patient access to care, and their commitment to delivering on Medicaid and Medicare requirements.”

The Inc. 5000 class of 2024 represents companies that have driven rapid revenue growth while navigating inflationary pressure, the rising costs of capital, and seemingly intractable hiring challenges. Among this year’s top 500 companies, the average median three-year revenue growth rate is 1,637 percent. In all, this year’s Inc. 5000 companies have added 874,458 jobs to the economy over the past three years. 

“Veda is committed to Health Equity, and creating the most accurate provider data is how we make good on that promise,” said Gaffney. “I am so proud of our customers and team members who ensure members have access to timely, high-quality care.”

For complete results of the Inc. 5000, including company profiles and an interactive database that can be sorted by industry, location, and other criteria, go to www.inc.com/inc5000. All 5,000 companies are featured on Inc.com starting Tuesday, August 13, and the top 500 appear in the new issue of Inc. magazine, available on newsstands beginning Tuesday, August 20. 

“One of the greatest joys of my job is going through the Inc. 5000 list,” says Mike Hofman, who recently joined Inc. as editor-in-chief. “To see all of the intriguing and surprising ways that companies are transforming sectors, from health care and AI to apparel and pet food, is fascinating for me as a journalist and storyteller. Congratulations to this year’s honorees, as well, for growing their businesses fast despite the economic disruption we all faced over the past three years, from supply chain woes to inflation to changes in the workforce.” 

About Veda

Veda blends science and imagination to solve healthcare’s most complex data issues. Through human-in-the-loop Smart Automation, our solutions dramatically increase productivity, enable compliance, and empower healthcare businesses to focus on delivering care. Veda is simple to use and requires no technical skills or drastic system changes because we envision a future for healthcare where data isn’t a barrier—it’s an opportunity. To learn more about Veda, follow us on LinkedIn.

More about Inc. and the Inc. 5000 

Methodology 

Companies on the 2024 Inc. 5000 are ranked according to percentage revenue growth from 2020 to 2023. To qualify, companies must have been founded and generating revenue by March 31, 2020. They must be U.S.-based, privately held, for-profit, and independent—not subsidiaries or divisions of other companies—as of December 31, 2023. (Since then, some on the list may have gone public or been acquired.) The minimum revenue required for 2020 is $100,000; the minimum for 2023 is $2 million. As always, Inc. reserves the right to decline applicants for subjective reasons. Growth rates used to determine company rankings were calculated to four decimal places. 

About Inc. 

Inc. Business Media is the leading multimedia brand for entrepreneurs. Through its journalism, Inc. aims to inform, educate, and elevate the profile of our community: the risk-takers, the innovators, and the ultra-driven go-getters who are creating our future. Inc.’s award-winning work achieves a monthly brand footprint of more than 40 million across a variety of channels, including events, print, digital, video, podcasts, newsletters, and social media. Its proprietary Inc. 5000 list, produced every year since its launch as the Inc. 100 in 1982, analyzes company data to rank the fastest-growing privately held businesses in the United States. The recognition that comes with inclusion on this and other prestigious Inc. lists, such as Female Founders and Power Partners, gives the founders of top businesses the opportunity to engage with an exclusive community of their peers, and credibility that helps them drive sales and recruit talent. For more information, visit www.inc.com. 

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 

Building upon CMS’s recent addition of a new benefit category for mental health counselors (MHCs) and marriage and family therapists (MFTs)—and recognizing that many MHCs and MFTs practice in outpatient behavioral health facilities—CMS has expanded its network adequacy requirements to include a new category called “Outpatient Behavioral Health.”

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).

Skill Sets of Certain Behavioral Providers Must Be Verified 

“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.

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.

 

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.

Provider Data Accuracy Verified By Third-Party Audit

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

Veda is paving the way for improved data quality, transparency, and accountability in healthcare through continuous validation and testing of our AI-powered solutions. Our scientific rigor commits us to continuously testing our methodology and we believe all AI-powered data solutions should undergo an unbiased independent validation of their data. Learn about our approach to healthcare data challenges and the third-party study performed by Erdős Institute proving our data accuracy.

Provider Data 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.

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.


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

You know your business. We know data.

One Simplified Platform

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.

Vectyr
PROFILE
SEARCH

The most up-to-date, comprehensive, and accurate data source of healthcare providers, groups, and facilities on the market.

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DIRECTORY ANALYSIS

Review and refresh your network directory to identify areas that affect your quality metrics.

Resources & Insights

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Q&A with Veda’s Co-Founders: Patented AI Approach for Provider Data
February 6, 2025
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Veda Announces Tenth AI and Machine Learning Patent
February 6, 2025
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9 Questions Leaders Should Ask Themselves to Help End Employee Burnout
January 28, 2025
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