The most up-to-date, comprehensive, and accurate source of data. Your organization can access profiles of every active provider in the U.S.—over 3.5 million.
See how we’ve helped leading healthcare organizations achieve significant cost savings, improve data accuracy, and enhance patient care. Here, you will find our results, research, reports, and everything else our scientists are testing in the Veda Lab – no lab coat required.
At Veda we understand that every data point is an opportunity to improve the healthcare experience. And we can see the potential when data is no longer a barrier.
Healthcare Business Today: Congress, Bad Data, and Ghost Networks
The Senate Finance Committee has advanced legislation that aims to eradicate ghost networks, a goal that will benefit payers, providers, and patients alike.
As the legislation advances through the halls of Congress, all stakeholders must have a clear understanding of why the bill is necessary and what’s behind all those ghosts anyway.
Ghost networks are provider networks that appear robust and full of available providers but actually contain inaccurate data and, in reality, have limited availability and unreachable providers. These “ghosts” are no longer practicing, not accepting new patients, are not in-network, or have errors in their contact information.
MedCity News: Healthcare Doesn’t Need More Big Tech
Healthcare Doesn’t Need More Big Tech; It Needs Specialized Tech. Byline by Dr. Bob Lindner in MedCity News.
It’s easy to oversimplify and say, “These big tech companies are now doing healthcare and they’re going to solve everything.” But the reality is that often, the solutions are not going to come from big tech.
Just like clinicians who specialize in an area of medicine, healthcare’s tech problems need specialized solutions. That’s because the industry doesn’t have a single general issue to solve, healthcare has many discrete issues to address.
To further complicate things, healthcare is not one industry but many industries under the same umbrella. Clinical care, devices, diagnostics, pharmaceuticals, hospitals, payers, and more each has its own unique challenges and opportunities that need to be addressed with unique solutions.
It’s easy to oversimplify and say, “These big tech companies are now doing healthcare and they’re going to solve everything.” But the reality is that often, the solutions are not going to come from big tech.
These individual problems are being addressed by legions of innovative people working in smaller, more focused organizations where they are experimenting, iterating, pivoting, and getting closer and closer to solutions to the issue they’re addressing. These teams are focusing on singular issues and solutions in a way that bigger, more general tech doesn’t.
To compound the issue, healthcare is an ever-changing industry and requires solution providers to be agile in order to keep up with emerging trends, new discoveries, new regulations, and shifts in patient and provider preferences. These smaller more specialized companies may not have the resources of large tech enterprises; however, they are inherently more adept at quickly iterating solutions, responding to changes, and adapting to evolving needs.
This is why specialized solutions and specialized tech providers are ultimately going to be the problem solvers.
Does this mean that big tech doesn’t have a place? Of course not. Big tech can do what big tech does best: identify, vet, and foster some of these solutions and ultimately scale the right ones.
But what about the funding? These entrepreneurial companies who are developing innovative tools are often start-ups and frequently raising capital at the same time they are building the solution.
A recent Pitchbook report covered by MedCity News included a mixed bag of news for these entrepreneurial companies in the medtech space. The report noted that venture capital funding to medtech appears to have bottomed out in the first quarter of this year and has been ticking slightly upward. That’s the good news. The troubling news is that this year’s medtech funding total may not reach the 2022 funding total of $13.5 billion and certainly won’t even approach the 2021 funding total of more than $19 billion.
In healthcare the stakes are high, and any tech solution needs to operate as a “mission-critical” part of the equation. Think NASA or car safety where there are no margins for error or experimentation like there are if we were building a ridesharing or shopping app. We’re dealing with people’s health and lives on a daily basis. The stakes should be treated as life or death because they are. And the solutions we deploy need to be more than adequate. They need to be infallible.
Is Your Data Partner Ready for New AI Regulations?
How to Find the Right Provider Data Vendor Partnership
It seems that almost every week we see new vendor offerings within the provider data management ecosphere—each claiming to offer a revolutionary way of visualizing your data and making impactful improvements for healthcare. The provider data accuracy challenges that health plans and provider organizations face are vast, so it’s not surprising that new health tech companies are quick to capitalize. Caution: all health tech companies may not be keeping new and developing AI regulations in mind when developing their technology.
All health tech companies may not be keeping new and developing AI regulations in mind when developing their technology.
Looking at the Bigger Provider Data Picture
Quality provider data can help the healthcare industry tackle some of the biggest challenges health plans and provider networks face, like onboarding, credentialing, roster creation, and referrals.
A commitment to improving your provider data can have great ROI potential and long-term impacts on your business and patient populations. But, with all the buzzwords and capability promises from the masses, how do you find the best vendor fit for your business needs?
Necessary Topics to Cover When Vetting Provider Data Vendors
To set you up for partnership success, here are discussion ideas for data vendor conversations:
Find out the vendor’s practices for responsible data collection, storage, and validation. For example, does the vendor keep their data in the U.S.? Veda’s Take: You want to ensure any vendor interacting with critical data is not utilizing offshore, 3rd party data processing centers that can unnecessarily expose data and therefore, providers’ information. Consider this: If patient data is protected from offshore processing vendors via HIPAA and other regulations, shouldn’t the same protections be afforded to provider data? Veda offers comprehensive security and data protection and is HITRUST-certified.
Understand the vendor’s business policies regarding AI. For example, how does an AI vendor utilize Language Learning Models? Veda’s Take: Avoid risk by future-proofing your AI policies. Machine Learning can be a powerful tool, but should be approached thoughtfully and aligned with current and future U.S. legislative requirements such as President Biden’s executive order for the Development and Use of Artificial Intelligence. Warning: fewer companies comply with the proposed regulatory requirements than you may think.
Ask about their reporting and measurement. How does the vendor define accurate data and measure it during and after delivery? Veda’s Take:Data can be compliant but inaccurate and unusable for health plan members who depend on the data to get care. Be prepared to discuss your business goals and thresholds for accuracy—consider going beyond meeting regulatory guidelines. We think healthcare data should truly be considered “accurate” if it meets a member’s needs when accessing care.
Discuss what is necessary for your business success. Can the vendor offer the necessary tools to reach your goals and eliminate what is unproven? Veda’s Take: Don’t be distracted by tools that may not deliver results or provide value for your goals. For example, APIs are often necessary to save time on automation. Therefore, many businesses focus on the ability to have an API connection and the API integration above even the results the product delivers. Or, in another example, the UX and the interface of a product can become a focal point above the actual functionality of the product. If you can’t trust the data and know how to interpret and use it, then connectivity and appearances don’t matter.
Determine what happens first. Can a vendor partner prioritize your specific business requirements? Veda’s Take: All businesses have different objectives and these goals greatly impact priorities. Your vendor should clearly articulate what needs to happen first, upon implementation of the product, to realize immediate value and reach success. There is no one-size-fits-all solution when working with a provider data vendor. Before integration and during the initial conversations is the perfect time to establish an approach to prioritization within business rules.
Get familiar with the training process. What does the implementation, training, and delivery process look like for your AI data vendor? Veda’s Take: How a provider data vendor plans to work with you, and how they plan to train others in your organization, is key to partnership success. Beyond day-to-day use of the tools, how does the vendor recommend using the data and applying the findings? Who should be trained on what tasks? Clear and concise preparation will ready everyone in your organization.
Ensure any vendor interacting with critical data is not utilizing offshore, 3rd party data processing centers that can unnecessarily expose data and therefore, providers’ information.
Once you have a solid understanding of how a potential partner tackles the above objectives, only then can you capitalize on a business case for building a collaborative partnership with a provider data vendor.
A ghost network is a provider network that appears robust and full of available providers but actually contains bad data and thus, much more limited availability and unreachable providers. These “ghosts” are no longer practicing, not accepting new patients, are not in-network, or have errors in their contact information.
Imagine using a “Find A Doctor” online tool to pick an in-network doctor in the specialty you seek, but when you call to book an appointment, you discover the provider is no longer practicing. That’s a ghost.
“ [Ghost networks are a] pervasive issue in the American health care system. A three-phase study of the accuracy of Medicare Advantage directories, which included over 15,000 providers, found that between forty-five and fifty-two percent of provider directory listings had errors, with some individual plans having error rates as high as ninety-eight percent.”
While behavioral health networks are often cited for inaccuracy and the mental health crisis in America has brought adequate network issues to light, inaccurate directories are a systemic issue prevalent across all provider types. All directories have “ghosts.”
How do ghost networks inhibit care?
Besides adding to patient frustration, ghost networks prevent patients from accessing the care they need. Impactful stories, like this one from the Yale Law & Policy Review, shed light on the millions of people dealing with the repercussions of inaccurate provider directories each year:
“KC, who manages her brother’s mental health care, gave up on trying to find an in-network psychiatrist because calling potential providers was taking up so much of her time that it was more cost effective to pay out-of-network rates. “Ironically, I can’t imagine my brother or others in his situation being organized and effective enough to be able to make all these calls and keep track.” [Yale Law & Policy Review]
Additionally, ghost networks have inhibited care by leading to unexpected medical bills. “When Americans are purchasing and using their health insurance, they have the right to know whether their doctors are covered by that plan,” said Ron Wyden (D-Ore). “Too often, seniors and families get health care whiplash when they sign up for a plan only to find out that their preferred doctor is out-of-network, or it’s impossible to find a covered mental health care provider.”
How does the REAL Health Providers Act tackle the issue of ghost networks?
The legislation aims to combat the ghost network problem by, among other things, requiring Medicare Advantage (MA) health plans (beginning with plan year 2026) to verify their provider directory data every 90 days and, if necessary, update that information.
If a health plan cannot verify the data, the plan must indicate in its directory that the information may not be up to date.
A health plan must also remove a provider within 5 business days if the provider is no longer participating in the plan’s network.
If a patient obtains care from an out-of-network provider that a health plan’s directory indicated was in-network at the time the appointment was made, the plan may only charge that patient in-network prices.
The legislation also requires MA health plans to analyze the accuracy of their provider data on an annual basis and submit a report to HHS/CMS with the results of that analysis. HHS/CMS will use this information to publish accuracy scores for each plan’s provider directory.
How does Veda banish ghost networks?
Veda has been at the forefront of efforts to eradicate “ghost networks” in the U.S. for years. Veda understands that progress on this front is essential to connect patients to the critical care they need and ensure that individuals are not burdened with unexpected healthcare costs.
Veda takes a one-two-punch approach to eradicating ghost networks. Veda leverages its patented technology and innovative solutions to first identify where the “ghosts” reside in provider directories and then fills in the gaps left behind once the ghosts have been removed. Using a mix of AI technology, smart automation, and machine learning, Veda’s provider data is proven accurate.
Find the Ghosts
Veda’s Quantym platform identifies the errors in a provider directory
High-volume audit solution that delivers comprehensive, real-time scoring of provider data quality to identify bad data and significantly improve provider directory accuracy
Fill the Gaps with Accurate Data
Veda’s Vectyr tool supplies accurate data to replace the bad data
The most up-to-date, comprehensive, and accurate source of data on healthcare providers, groups, and facilities on the market
Veda understands that ghost networks will not be eliminated by the manual attestation methods of the past. Manual verification is labor-intensive, expensive, subject to human error and time and again, it’s proven ineffective. The fact is, “attested” data is frequently not “correct” or “accurate.”
Veda’s technology takes provider attestation out of the equation and, in doing so, can more accurately identify where ghost networks exist (by pointing out inaccuracies in the health plans’ provider directory data) and provide solutions to the ghost network problem (by filling in the gaps that are created when the bad data is removed).
It’s time to let technology like Veda’s solve the ghost network problem once and for all. Give your members the accurate information they need to make an appointment for care on their first try. Contact Veda for a free data assessment.
In this episode of Leaders of B2B, Meghan Gaffney, CEO and co-founder of Veda, offers an in-depth perspective on the evolving realm of artificial intelligence in healthcare. With her extensive experience, Meghan underscores the transformative impact of implementing AI solutions in medical diagnostics and patient care pathways.
Tune in to learn why a diagnostic approach is essential for effective data management across industries, to identify and address critical issues systematically.
Veda Brings AI Data Solutions to Provider Group Organizations
Accurate data simplifies referral management to address outcome gaps
October 9, 2023, MADISON, Wis. – Veda, a health technology provider specializing in accurate, curated provider data, is introducing new features to support healthcare provider organizations making specialty referrals. In an effort to get patients the care they need and help improve outcomes, Veda’s solutions remove barriers for both referring providers and patients.
With our accurate provider data and human-in-the-loop technology, we can positively impact the legions of patients who are not accessing necessary specialty care.
Meghan Gaffney, CEO and co-founder, veda
In one survey 1, as many as one-third of patients don’t follow through on a referral to a specialist from their primary care provider. Provider groups who can deliver accurate referrals can improve the chances of a patient pursuing specialty care.
1. Becker’s Payer Issues: Only two out of every three patients actually receive the care that they need when a referral is made.
“With our accurate provider data and human-in-the-loop technology, we can positively impact the legions of patients who are not accessing necessary specialty care,” said Meghan Gaffney, CEO and co-founder of Veda. “In addition, our technology can help capture potential lost revenue for provider organizations by managing referral leakage to providers outside of the organization.”
Health plans across the country currently benefit from Veda’s suite of AI-powered products. With this expansion, Veda is delivering solutions to health systems and provider groups for the first time with the product Vectyr.
Vectyr curates data from more than 100,000 unique sources, optimizing results for each provider every 24 hours. Rigorous scientific validation methodology ensures that users have the most up-to-date data for every provider in the country, on-demand, every day. The database provides records for physicians, nurses, allied health, behavioral health, pharmacists, and dental providers.
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’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. Follow Veda on LinkedIn.
Supervised vs. Unsupervised Machine Learning: Using The Right Tool For The Job
It’s Veda’s philosophy that any new technology tools we utilize are not meant to wholly replace human engagement. We believe that technology should help elevate humanity. With a focus on performing meaningful work, people can achieve their highest value. Technology should help people help people.
As a Data Science as a Service (DSaaS) company, Veda leverages scientific principles within our unique AI and machine learning systems to perpetually clean, correct, and monitor evolving provider and facility data. A lot of companies claim to offer accurate provider data, but none are as committed to using science to solve deeply entrenched provider data problems.
Dr. Bob Lindner describes supervised vs. semi-supervised learning
At Veda, we use AI systems including natural language processing (NLP), supervised, and unsupervised learning components that can be leveraged to solve a wide array of payer data challenges depending on what tool is right for the job. No matter what, we start by understanding the problem—not by applying a method.
A lot of companies claim to offer accurate provider data, but none are as committed to using science to solve deeply entrenched provider data problems.
The Roles of Supervised and Unsupervised Learning
Supervised Learning
Veda utilizes supervised learning because it doesn’t require “perfect” sources of data— it can make use of the good parts of any data source and knows how to ignore errors. With supervised learning, a data scientist is watching and helping train a model with all the healthcare nuances and industry-specific language, etc. to make the model a good one.
We’ve measured individual sources of data for years—including attestation— and haven’t found any at 90% or above yet. Supervised learning is incredibly accurate with the data we have access to today. The benefit is the highest accuracy which is flexible and not dependent on a single data source.
Unsupervised Learning
We also use unsupervised learning for offline data exploration and research to learn more about a dataset, to help design better machine learning features for supervised learning systems. That’s because unsupervised learning separates big collections of data into groups on its own. The benefit of unsupervised learning is its ability to pick out patterns in the data.
Unsupervised learning separates big collections of data into groups on its own. Some will claim that unsupervised learning, alone, is superior to supervised learning because of the lack of human intervention. However, most algorithms require the user to specify upfront how many groups they want the data separated into. So no matter what data is being grouped, one would have to delineate exactly how many groups are wanted ahead of time and the exact number of groups the data is sorted into regardless of whether the groups match up well with the data. This requires the user to have upfront knowledge of exactly what labels and groups they need.
But the biggest pitfall of unsupervised learning is that there’s no labeled training data, which means there’s no actual measurement of how well it’s working and placing the items into the correct groups. And with no way of knowing how well it’s working, it’s impossible to depend on unsupervised learning as a primary method for accuracy.
The Right Tool For the Job
Supervised and unsupervised learning are tools, and just as you wouldn’t remodel your kitchen and only use a saw, you shouldn’t only use one kind of machine learning model.
Veda’s technology and approach to data challenges are fundamentally different from other provider data technology companies in that we focus on fully automating both the static information and the more challenging temporal information about a provider—data that changes at varied rates over time, like practice address, phone, and group affiliation. Our patented systems do not require manual outreach to providers, rather they rely on data created by providers throughout their established workflows. This increases data accuracy by reducing human error while also decreasing provider abrasion. Validating millions of temporal data elements in real-time requires Veda’s full automation system and could not be solved manually.
Above all, we believe that AI and machine learning are the best ways to solve the provider data quality problem because:
These techniques do not require us to know how accurate our sources of data are ahead of time—the machine figures out how to tell good data from bad
AI makes the most of imperfect and changing data
They do not require provider participation—we use data they already create in their day-to-day workflows, so no need to persuade providers to take additional action
It works—we have scientifically tested attestation along with “source of truth” modeling, and Veda’s approach has the highest measured accuracy of any approach in the industry.
Artificial Intelligence, ChatGPT, and the Relationship Between Humans and Machines
By: Dr. Bob Lindner, Chief Science & Technology Officer, Co-Founder
If the explosive launch of ChatGPT has taught us anything, it’s that there is a growing appetite for engaging with AI. According to a recent UBS study, the chatbot from OpenAI reached 100 million monthly active users in January— only two months after its launch. By comparison, it took TikTok about nine months to reach that milestone and Instagram two-and-a-half years.
While ChatGPT and the generative AI that powers it represent the latest advancements in AI and machine learning, the fact is that organizations and individuals have been trying to harness the power of AI for years. Some see it as the wave of the future. Others are scared of what it portends for the complicated relationships between humans and machines.
Many people are so afraid of being displaced by the automation that artificial intelligence brings that they overlook the benefits of this amazing technology. But the fear of “robots replacing humans” isn’t the only thing that gives people pause. There’s also concern that machines will make unacceptable errors. Of course, when people make the occasional mistake, we’re used to giving them the benefit of the doubt, but we struggle to do the same for machines because we don’t know how to contextualize their errors.
Why do we react so emotionally to AI? How can we shift our perspectives? And how can we actually score recommendations in AI systems? The hope is that with greater understanding, we can apply AI to more business settings and drive greater success.
Digging deeper into our fears and hesitations
Behaviorally, people tend to fear things we don’t understand or that seem out of our control. When it comes to risk, specifically, we struggle to comprehend how to assess it in an objective—rather than emotional—way.
For example, think about self-driving cars. The thought of a car without a driver makes many of us uneasy. Even though more than 75% of us will be in at least one major car accident during our driving lifetime, we’re afraid to put autonomous cars with this type of driving record on the road. While the probability of an accident is likely not higher than for a human driving a car, the combination of not knowing the exact percentage of risk and not being in control makes it harder to accept. We’re just not used to making our decisions based on probability; we are used to listening to our gut.
In order to process the data with a probabilistic AI system, we have to score it and set a threshold for “good” data; anything with a score below our threshold is discarded and anything higher is deemed an acceptable level of risk and included in the data set.
In my experience, the best way to get comfortable with objective assessment of risk is practice. Over time, it becomes more natural to look at the numbers as opposed to looking at our emotional response. Of course, understanding exactly how AI works helps too.
Understanding how to assess risk associated with AI
AI acts on two types of systems: deterministic and probabilistic. With a deterministic system, an outcome can be determined with relative certainty. This includes apps like Amazon, Doordash, and Venmo, which generate predictable types of data within a confined system. These are usually not considered “mission-critical,” and as a result, we’re willing to tolerate some level of inaccuracy in their algorithms. For example, when Netflix recommends a movie that doesn’t actually interest us, we don’t cancel our subscription to the service. We just look at the next recommendation in the queue or scan the top 10 titles of the week. We’re forgiving.
Probabilistic systems have built-in uncertainty. The exact output is not known. Think about the difficulty of forecasting the weather. It’s hard for us to understand the uncertainty of probabilistic systems and the stakes get even higher when we’re dealing with “mission critical” data, like we are in healthcare technology. In order to process the data with a probabilistic AI system, we have to score it and set a threshold for “good” data; anything with a score below our threshold is discarded and anything higher is deemed an acceptable level of risk and included in the data set.
The first step is to understand how these systems work, and the second is to set thresholds to score data that matches your risk tolerance.
Take a risk
With machine learning models, we are training a system to learn and adapt in order to improve—so it’s necessary to make assessments on an ongoing basis, rather than measuring an automation system’s performance once and only once. Because of that, it’s essential to have patience, as data can and will change, depending on many factors.
While risk makes people feel uncomfortable regardless of the setting, it’s time to address those fears and reluctance to move forward. Once we have tangible examples and parallels we often relate and tolerate it better.
As for ChatGPT and its generative AI brethren, the key will be for each person who engages with these tools to determine what level of risk they are willing to take. For most of us, a simple chat about something mundane or unimportant is likely acceptable. For some, the exchange of critical data or asking it to perform an important function will be a bridge too far. For now.
CEO Meghan Gaffney Selected for EY Entrepreneurial Winning Women™ North America Class of 2023
Veda’s Meghan Gaffney Selected for EY Entrepreneurial Winning Women™ North America Class of 2023
Ernst & Young LLP (EY) is proud to announce that Meghan Gaffney, CEO of Veda, a health technology provider specializing in accurate, curated provider data, is one of the 23 women founders from 20 companies selected for the EY Entrepreneurial Winning Women™ North America (Winning Women) Class of 2023.
Now in its 16th year, the program identifies talented entrepreneurs with scalable companies in the United States and Canada and connects them with the networks and resources they need to accelerate growth and scale their businesses.
Participants receive customized executive education, introductions, and access to the Winning Women community around the world, as well as the entirety of the EY global entrepreneurial ecosystem, including members of the Entrepreneur Of The Year® and EY Entrepreneurs Access Network (EAN) programs.
“Women founders contribute trillions to the US economy, and studies have shown that when women are empowered, the economy grows,” said EY Americas Industry and Solutions Leader Cheryl Grise, who also serves as the EY Entrepreneurial Winning Women North America Program Executive Sponsor. “At EY, we believe that a rising tide lifts all boats, so the success of women impacts the success of every business,” said Grise. “Over the last 16 years, the Winning Women program has intentionally addressed societal gender-based challenges that often confront women entrepreneurs by providing these phenomenally talented businesspeople with greater access, guidance and knowledge, which are the tools they need to continue to break the mold, inspire innovation and be shamelessly ambitious. I welcome these women to the fold and look forward to seeing them do even bigger and greater things.”
Cheryl Grise
Members of the Winning Women Class of 2023 have ambition, creativity and a desire to build a better world in common. They are tackling problems from inclusivity, to offering healthier products and food, to solving for complex health care issues. Others are bringing to the table innovative solutions in supply chain, data management, marketing and more. The founders selected for the program display unparalleled ingenuity, business prowess, ambition in crafting solutions and a formidable can-do attitude that allowed them to break from the pack of their peers to stand out.
“2023 has been filled with many economic ups and downs – from geopolitical unrest, to interrupted supply chains, to inflation – there has been plenty to make consumers tighten their belts” said Maranda Bruckner, EY Entrepreneurial Winning Women North America Program Leader. “I applaud these business leaders for not only surviving these challenges, but exceeding growth and profit expectations when others did not. They are outstanding examples of being unstoppable and shifting entire industries. We are excited to have them in the program, and deeply congratulate them on this recognition.”
The EY Entrepreneurial Winning Women North America program serves women business owners who are founding CEOs of any US or Canadian privately held company. Company revenues typically range from at least $2m to $30m annually. The EY Entrepreneurial Winning Women program participants become part of a global peer community, which includes more than 900 entrepreneurs in 55 countries and on every continent.
“Every year, I am so pleased to welcome the newest class of the EY Entrepreneurial Winning Women North America program, who are not only incredible leaders in their organizations but also in their communities,” said Lee Henderson, Americas EY Private Leader. “It is an honor to provide these best-in-class founders with resources and access to EY’s vast entrepreneurial ecosystem to help them scale, attract talent and disrupt industries. I am always excited to see where these entrepreneurs go next. I already know it’s only up from here.”
The Class of 2023 will be officially recognized in November 2023 at the Strategic Growth Forum®, one of the nation’s most prestigious events for ambitious, high-growth, market-leading business leaders.
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 and follow us on LinkedIn.
About EY EY exists to build a better working world, helping create long-term value for clients, people and society and build trust in the capital markets.
Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate.
Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.
EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. Information about how EY collects and uses personal data and a description of the rights individuals have under data protection legislation are available via ey.com/privacy. EY member firms do not practice law where prohibited by local laws. For more information about our organization, please visit ey.com.
Ernst & Young LLP is a client-serving member firm of Ernst & Young Global Limited operating in the US.
About EY Entrepreneurial Winning Women™ The EY organization is committed to seeing women lead. EY Entrepreneurial Winning Women™ is a global program for successful entrepreneurs whose successful businesses show more potential to scale. Through access to global EY networks throughout the entrepreneurial ecosystem, pioneering founders on every continent secure the resources, advice and connections they need to scale their businesses sustainably. This one-of-a-kind community of founders is rewriting rules and remaking markets. Visit ey.com/us/winningwomen.
About EY Private As Advisors to the ambitious™, EY Private professionals possess the experience and passion to support private businesses and their owners in unlocking the full potential of their ambitions. EY Private teams offer distinct insights born from the long EY history of working with business owners and entrepreneurs. These teams support the full spectrum of private enterprises, including private capital managers and investors and the portfolio businesses they fund, business owners, family businesses, family offices and entrepreneurs. Visit ey.com/us/private.
At Veda, a healthcare data solutions company headquartered in Madison, we’ve been using AI technologies like supervised machine learning for years, adapting our policies as we go, to stay ahead of a rapidly changing tech landscape. Recently we’ve tackled the use of LLMs such as ChatGPT, Google Bard, GitHub, and others by employees on our office systems. In doing so we learned a bit along the way that may help other businesses working on similar policies.
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.
