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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.

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.

HealthX Ventures Blog: How Veda Is Aiming to Fix Healthcare’s Broken Provider Directories

Q&A with Veda CEO Meghan Gaffney and HealthX Ventures on how Veda leverages automated data science and machine learning solutions to provide healthcare payers and networks with the most accurate and comprehensive data available.

When you or someone you love needs care, it would make sense to seek help from a directory of providers who are in-network and available to help. Unfortunately, this is one of the most painful parts of America’s broken healthcare system. One of the most shocking and significant barriers for patients seeking care in today’s healthcare landscape is the vast amount of inaccurate provider directory data. This inaccurate data creates frustration, delays, and inefficiencies that negatively impact both the patient experience in finding care, and also contributes to negative overall patient outcomes. Now, imagine loved ones dealing with memory loss, a behavioral health crisis, or long Covid—and finding the right provider becomes an even greater challenge.

Veda, co-founded by Meghan Gaffney, addresses this critical issue by leveraging automated data science and machine learning solutions to provide healthcare payers and networks with the most accurate and comprehensive data available. By curating data from over 300,000 unique sources, Veda offers real-time, precise profiles of more than 3.5 million healthcare providers, facilitating better decision-making and improved patient experiences.

Imagine this: you use your health insurance company’s online directory to find a doctor at your preferred location and needed specialty, only to be met with incorrect phone numbers, wrong addresses, or no information at all. You’re stuck making phone calls that lead to nowhere, and wasting precious hours navigating bad information while experiencing delays in actually getting the care you need most. After years of diving deep to the complexities around this problem, Veda is at the forefront of clean data and better access to care. Veda’s tools correct inaccurate data and make it possible for members to get what they truly need: the ability to easily and quickly make an appointment with a healthcare provider.

One of Veda’s earliest supporters was HealthX Ventures. With a mission to improve access to care, HealthX found Veda working at the forefront of health equity and contributing to better outcomes for all.


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.

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.

 

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.

Veda at ViVE 2024

Panels, partnerships, and busting ghost networks

ViVE 2024 brought together health tech innovators, vendors, investors, and media. What did Veda bring? Engaging discussions around Medicare Advantage, AI in healthcare, ghost networks, and rural health. Not to mention some heavy media coverage announcing our Humana partnership. Plus, a little fun with a certain famous car to showcase Veda’s ghost network-busting abilities. (We were in Hollywood after all.)

Rural Healthcare and Data

“The way for rural healthcare to succeed is to make it easy to find a doctor, to be a doctor and to pay a doctor,” said Dr. Bob Lindner, Chief Science & Technology officer at Veda during a panel on the challenging rural healthcare landscape. “All of these three things have challenges that can be traced back to the data.”

Bob presented data as a tool that rural health systems can utilize to access accurate information on traveling doctors, a unique offering in rural areas. As rural health faces more hits and challenges, data has the answers.

Accurate provider data leads to better care

The belief that accurate provider data leads to better care is exactly what led to the Humana and Veda partnership (and created plenty of buzz at ViVE). Fierce Healthcare proclaimed “Humana taps data automation startup Veda to polish up provider directories.” MedCity News listed the partnership in their “9 ViVE Announcements You Don’t Want to Miss.”

Veda will use its patented automation technology to analyze, verify, and standardize Humana’s data to ensure the information is accurate and comprehensive, along with real-time scoring of data quality.

Veda is ready for the mainstream adoption of AI and committed to advancing the technology to offer members true access to care.

Busting Ghost Networks

Whether you were building a mini-fig or entering to win an ECTO-1 replica, plenty of fun was found at the Veda booth. With a page taken from a beloved movie franchise, Veda showed off its ‘ghost-busting’ abilities and talked ghost networks.

Ghost networks are provider networks that appear robust and full of available providers but actually contain 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.

Veda’s accurate data eliminates ghost networks, improves member satisfaction, and stays ahead of emerging regulations.

See you next year at ViVE!

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?

ai regulation questions

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.

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

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


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:

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:

  1. 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.
  2. 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.
  3. 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. 
  4. 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.
  5. 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. 
  6. 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

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.

Quantym
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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