The most up-to-date, comprehensive, and accurate source of data. Your organization can access profiles of every active provider in the U.S.—over 6 million.
See how we’ve helped leading healthcare organizations achieve significant cost savings, improve data accuracy, and enhance patient care. Here, you will find our results, research, reports, and everything else our scientists are testing in the Veda Lab – no lab coat required.
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 organizations today face an immense challenge: ensuring data accuracy and accessibility in a complex, often fragmented industry. Meghan Gaffney, CEO of Veda Data Solutions, is tackling these healthcare data challenges head-on.
In a recent episode of the Healthcare Strategy Podcast, Gaffney, whose unique journey spans nearly 15 years in healthcare policy to tech entrepreneurship, discussed how AI-driven healthcare solutions can bridge critical gaps.
Meghan covers the impact Veda’s technology has had on healthcare organizations. Please read about our automation, which saves money and improves the member experience.
Dr. Bob Lindner is the Chief Science and Technology Officer at Veda, a company addressing provider directory data challenges.
It’s no surprise to anyone who works with data—it’s messy. In every industry and every business, there are data anomalies and issues that can impact the story data tells. If we have any hope of improving data practices and making collected data truly actionable, we first have to acknowledge its limitations and then explore modern solutions for improving it.
Bad Data Is The Norm
With the new federal administration exploring cost-cutting measures and releasing data nearly daily, a specific example caught my eye—it was a Social Security disbursements by age graph, with the data suggesting 210 year olds are receiving Social Security entitlements. As a data scientist who has been working with healthcare data for over 10 years, this graph wasn’t shocking to me.
I recently saw one dermatologist who was practicing at 20 different variations of one address; imagine the extra legwork required by a patient to find out where you are booking an appointment. Or how about two providers with the exact same name but one is a veterinarian on the West Coast and the other is a physician in New York? There is state licensing info for both of them, but the only one with a federal National Provider Identifier (NPI) is the veterinarian. These are complex data problems occurring every day.
Data engineers know that a lot of data in every industry is collected manually, and this often introduces errors that are quickly propagated and magnified throughout downstream processes. In fact, most data systems in the modern economy, all around the globe, have shockingly out-of-date practices. With a spotlight on data issues right now, it’s important to dig deeper and examine data processes to have any hope of modernizing databases and making data functional.
Meghan Gaffney: 5 Reasons Why Women Build Great Companies
Read CEO and Co-Founder of Veda Meghan Gaffney’s Medium.com Interview
Medium Authority Magazine — Women-led businesses are on the rise, and the data shows they often outperform their peers. From fostering strong company cultures to driving innovation and long-term success, women bring unique strengths to entrepreneurship and leadership. What are the key reasons behind their success? Read Meghan Gaffney‘s interview with Authority Magazine below.
Thank you so much for doing this with us! Before we dig in, our readers would like to get to know you a bit more. Can you tell us a bit about your “backstory”? What led you to this particular career path?
I worked on Capitol Hill during the development of the Affordable Care Act and had the unique opportunity to hear perspectives on the future of healthcare from hospital executives, insurers, and patient advocates. Data was at the center of many challenges patients faced when accessing care, but it wasn’t the focus of any proposed solutions. I learned most patients simply want the basics: the ability to find a provider who they can easily and quickly book an appointment with. Quite simply, I knew there had to be a better way for people to access a provider.
Can you share the most interesting story that happened to you since you began your career
What I’ve found over and over again is how bipartisan healthcare is. From working on Capitol Hill to CEO of a healthcare technology company, healthcare’s impact is universal. It’s a rare piece of common ground for both sides of the aisle.
Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lesson you learned from that?
One of the mistakes I made in the early fundraising days was trying to contort our business into something that it wasn’t to appeal to a broader audience. We were offering technology that was fundamentally different from what everyone else was doing in Silicon Valley and initially, instead of leaning into that, I tried to appeal to everyone. Even down to my slide deck framework, I made it look like what the market was used to seeing.
I learned broad visions that address all areas of a market can muddy the water on what your next steps are. It’s difficult to find out where your product will fit if it fits everywhere. I quickly realized an authentic voice and viewpoint was far more valuable than doing what everyone else was doing.
Digital Health Transformers podcast host Gregory Cave is joined by Meghan Gaffney, CEO and Co-Founder of Veda. She discusses the importance of accurate provider data, the challenges patients face in accessing specialized care, and how AI is transforming healthcare operations. Meghan explains how Veda automates the transfer of provider data to health plans, helping patients find in-network providers quickly and efficiently.
Meghan covers the impact Veda’s technology has had on healthcare organizations-read about our automation that saves money and improves member experience.
Deepfakes Can Damage Businesses—Here’s How To Fight Back
Deepfakes—AI-generated synthetic media in which visuals or audio are manipulated to create deceptively realistic content—are often discussed in terms of their impact on the public’s perception of current events, but they pose a growing threat to businesses as well. Created and leveraged by unscrupulous actors, deepfakes can enable fraud, perpetuate misinformation and cause lasting brand damage.
Whether they take the form of a fabricated video, cloned voice or contrived image, deepfakes can erode trust and disrupt operations in ways many companies aren’t prepared for. Members of Forbes Technology Council discuss some of the specific ways deepfakes could be used to hurt a company and what leaders can do to defend their organizations (or respond when a deepfake succeeds).
Regularly Review Employee LinkedIn Profiles
“We’ve noticed LinkedIn profiles for people who claim to work at our company but who don’t or never have. Such deepfake profiles damage our company because our people, our reputation and our brand are being abused. Leaders can respond to this specific use of deepfakes by periodically reviewing all “employees” of your company. Look for surprises and flag the frauds for review by LinkedIn.” – Robert Lindner, Veda
In this episode, #MillenniumLive is joined by Dr. Bob Lindner, Chief Science & Technology Officer and Co-Founder at Veda, for a deep dive into the fascinating world of artificial intelligence (AI). Bob shares his insights on what excites him most about AI development, exploring the balance between innovation and responsibility. Tune in as Bob discusses the differences between supervised and unsupervised learning, the critical role of data science in AI modeling, and why modeling is essential to delivering impactful results.
We’ll look at the future of healthcare data and the challenges it faces, and how Veda is positioned to lead the charge in transforming the industry. Whether you’re an AI enthusiast or just curious about the technology shaping our future, this episode is packed with knowledge, thought-provoking discussions, and practical advice for businesses exploring AI solutions.
Meghan Gaffney Named a Women in Health IT to Know 2024
Women are working to shape and enhance the future of healthcare through health IT. These strong female leaders are modernizing administrative healthcare processes, cutting back on inefficiencies, standardizing workflows and more.
Meghan Gaffney. CEO and Co-founder of Veda (Madison, Wis.). As CEO and co-founder, Ms. Gaffney is instrumental in shaping Veda into a pioneering force in the data automation sector and a key player in the provider data management market. She oversees the strategic direction of the a Series B company, steering it through significant growth phases and forming partnerships with commercial plans and industry giants like Humana. Her leadership has also contributed to the company’s recent achievement of third-party validation for its AI technology. With over 15 years of experience in healthcare policy, Ms. Gaffney is adept at navigating complex regulatory environments and leveraging technology opportunities to address pressing issues such as “ghost networks” in healthcare provider directories. A 2023 EY Entrepreneurial Winning Women, Ms. Gaffney also contributes to the Entrepreneur Leadership Network and shares her insights on venture capital dynamics within the digital health startup ecosystem.
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.
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.
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.
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.
