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.
AI accountability in healthcare for business success
Chief Healthcare Executive – Many in the public are leery of AI. By committing to transparency and accountability, health organizations can emerge as leaders in innovative and responsible AI implementation.
AI is becoming integral to healthcare, revolutionizing everything from clinical outcomes to operational efficiencies. Stakeholders across the industry—payers, providers, and pharmaceutical companies—are leveraging AI technologies like machine learning, generative AI, natural language processing, and large language models to streamline processes and close gaps in care. These innovations are transforming aspects like image analysis and claims processing through data standardization and workflow automation.
However, integrating AI into healthcare is not without its hurdles. Public trust in AI has plummeted, dropping globally from 61 percent in 2019 to just 53 percent in 2024, with many skeptical about its application.
Certifying outcomes from AI-driven practices remains an unregulated territory and transparency around how algorithms impact health data practices and decision-making is lacking. For example, AI models designed for real-time automation can quickly process flawed data, leading to erroneous outcomes. AI transparency and ethical practices must evolve towards greater accountability and compliance to advance the industry.
However, for healthcare executives, establishing and showcasing ethical and transparent AI practices goes beyond following existing guidelines. By committing to transparency and accountability, organizations can position themselves as leaders in innovative and responsible AI implementation.
To effectively demonstrate these principles, healthcare business leaders should consider the following:
Implement rigorous validation protocols: Ensure that your organization’s AI algorithms undergo thorough and unbiased third-party validation. This step is crucial for verifying the accuracy, reliability, and safety of AI outputs. Validation helps to mitigate risks and ensures that AI systems operate as intended.
Promote transparency: Be transparent about how your AI models work and how they impact data processes. This includes disclosing the use of AI to patients, payers, and providers, and providing clear explanations of the AI’s role in decision-making processes. Transparency builds trust and helps stakeholders understand the value and limitations of AI technologies.
Commit to ethical standards: Adhere to ethical guidelines and best practices in AI development and deployment. This includes addressing potential biases, ensuring data privacy, and prioritizing patient safety. Ethical AI practices foster a culture of accountability and integrity within your organization.
Engage with stakeholders: Actively involve stakeholders in the development and implementation of AI systems. Gather feedback, address concerns, and make adjustments based on input from patients, providers, and others. Engaging with both internal and external stakeholders helps to build trust and ensures that AI solutions meet needs and expectations.
Stay ahead, informed, and compliant: Keep abreast of evolving regulations and guidelines related to AI in healthcare. Ensure that your AI systems comply with all relevant regulatory requirements. Staying informed and compliant helps to mitigate legal risks and demonstrates a commitment to responsible AI use.
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.
Veda doesn’t use payers’ directories as inputs in its AI and data training models. Why not?
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.
Can you give us an analogy to describe how problematic this really is?
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.
Why It Took Language Processing For AI To Go Mainstream
Scientists and technologists have been using AI for decades. We’ve used it to do complicated calculations and run algorithms and equations that we couldn’t previously conceive of. Your favorite streaming services have been using it for years to recommend shows and movies. But looking at media coverage of the past year, you’d think that AI was just developed. Why is mainstream AI language processing now taking off?
In late 2022, did AI experience an onslaught of media attention that made it seem like it was a new functionality? Why are legislators and regulators now racing to regulate something that has been in existence for about the same length of time as the color TV?
Learning To Learn
Tools powered by AI have essentially learned to learn. The language models we’re all seeing now train themselves with two primary algorithms. First, they can look at any sentence in any context and try to predict the next one.
The other way that language models try to learn is by guessing words in a sentence if some words are randomly removed. These are examples of implicit supervised training, and it’s made possible because these tools use the entire corpus of the internet as training data. This is the actual breakthrough.The other way that language models try to learn is by guessing words in a sentence if some words are randomly removed. These are examples of implicit supervised training, and it’s made possible because these tools use the entire corpus of the internet as training data. This is the actual breakthrough.
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.
HEALTHCARE BUSINESS TODAY — Specialty and subspecialty healthcare services are less likely to be available in rural areas and are less likely to include highly sophisticated or high-intensity care. This exacerbates problems for rural patients seeking specialized care who must travel significant distances for treatment.
It comes as no surprise a 2019 policy brief from the University of Minnesota Rural Health Research Center found that 64% of surveyed rural health clinic staff members reported difficulties finding specialists for patient referral.
A functioning rural health system relies on legions of specialty care doctors doing outreach visits across a wide geography. In theory, that’s an effective way to ensure that rural patients have access to specialty care without traveling to a major metro area. But, bad data is keeping us from achieving complete access to specialty care in rural areas and experts across industries are weighing in on the issue.
Health tech companies have attempted to solve provider data inaccuracy problems with a number of products, platforms, and integrations. No solutions have been able to offer members the ability to easily book an appointment armed with accurate information.
While many solutions focus on gathering all data sources available to identify providers, most don’t have the ability to effectively clean up those databases. That’s where we comes in. Veda is leading efforts to eradicate rural healthcare data challenges in the U.S. Discover how our technology connects patients to the critical care they need while ensuring that individuals are not burdened with unexpectedhealthcare costs.
AI for Amateurs: Questions Answered by Veda’s AI Experts
Everything You Always Wanted to Know About AI But Were Afraid to Ask
Feeling overwhelmed by AI? You’re not alone. At Veda, we take complicated data and we make it simple. Now we’re here to explain the basics of the complicated topic of artificial intelligence. If you’re feeling like everyone is diving into the deep end with AI knowledge but you’re still in the kiddy pool, this is for you.
Impossible to miss, 2023 is synonymous with the year AI debuted to the masses. AI capabilities have brought up questions in every industry, including healthcare. Your organization will likely find itself navigating the risks and rewards associated with healthcare AI in the coming year.
But, let’s start with a question you’re too afraid to ask at the company meeting: What is AI? Like, really. We’ve found a lot of false information out there and we’re here as a trustworthy source you can pull information from.
Why is Veda a Trusted Source?
As pioneers who have used AI technology since our founding, we’re passionate advocates for AI and want to ensure everyone else feels comfortable with it too.
Want our credentials? Our technology and data science team has 80 years of collective AI experience. Veda co-founder and Chief Science and Technology Officer, Bob Lindner, is the author of five technology patents on AI, entity resolution, and machine learning. Bob also has over 16 years of experience writing and publishing scientific and academic papers in the artificial intelligence field.
Backed by extensive experience and science, we’re the AI experts.
What is AI?
OFFICIAL ANSWER: Artificial intelligence Is a field of study that focuses on how machines can solve complex problems that usually involve human intelligence.
AI is not one specific tool. It is a field of study. With AI’s computing power, computers can make decisions and predictions, and take actions. An algorithm recommending which movie you should watch next is an AI action.
VEDA’S TAKE: So why does this matter? Why is AI important? By freeing up human resources, AI can reduce manual and often error-prone tasks. Freeing up people so they have the time to do the things they do best, that’s the power of AI.
What is machine learning?
OFFICIAL ANSWER: Machine learning is a sub-field of AI and focuses on algorithms that train models to make predictions on new data without being explicitly programmed. Meaning, the machine learns the way humans do, with experience.
Note: In recent years, some organizations have begun using the terms artificial intelligence and machine learning interchangeably.
Instead of learning step by step, computers using machine learning can learn through trial and error and lots of practice. What does machine learning practice on? Lots and lots of data. The data can be things like images, video, audio, and text. When fed loads of data, machine learning will recognize patterns and make predictions based on these patterns.
AI is not one specific tool. It is a field of study. With AI’s computing power, computers can make decisions and predictions, and take actions.
VEDA’S TAKE:Veda uses machine learning, and therefore, AI for the healthcare industry, every day. For what exactly? To power our provider information. Veda uses machine learning to:
Determine correct addresses and phone numbers
Transform provider rosters from one format to another
Simulate an experience a member may have when booking an appointment
With a patented training data approach, our machine learning can make predictions on a wide variety of new data (that it has never seen before in the training set).
Feeling good about AI and machine learning? Further your AI understanding with these blogs:
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.
Healthcare provider data can be riddled with inaccuracies—just ask anyone who uses network directories to find an in-network specialist or view clinics in a 10-mile radius. The Centers for Medicare & Medicaid Services (CMS) Medicare Advantage (MA) online provider directory reviews between September 2016 and August 2017 found that 52.2% of the provider directory locations listed had at least one inaccuracy.
Health tech companies have attempted to solve provider data inaccuracy problems with a number of products, platforms, and integrations. No solutions have been able to ultimately offer a better experience for members where it matters: the ability to easily book an appointment armed with accurate information.
Many solutions in the market focus on gathering all data sources available to identify providers but don’t have the ability to clean up those databases so they have only current and accurate information. A patient might find a doctor in a directory but if the location and coverage information was wrong, they still can’t make an appointment.
Enter Veda’s latest offering: Vectyr Data Curation. Vectyr offers the most up-to-date, comprehensive, and accurate source of provider data on the market. Vectyr’s database uses more than 100,000 unique sources to create an optimal collection of provider information. The data is continuously monitored, validated daily, and backed by our accuracy guarantees.
Prove It
How does Veda back up claims of accuracy and completeness? For one, our team of data scientists behind the development of Vectyr has the clout and expertise needed for intensive data modeling. From creating ground-breaking machine learning code to researching at the largest particle physics laboratory in the world, the best in science and technology are found at Veda. Here is how Veda employs a different approach than other data companies on the market:
Automation: Veda fully automates static and temporal data, boosting accuracy and reducing provider barriers. This validation process is automated in real-time, a fundamental advantage for healthcare companies seeking effective data structure.
Performance Measurement: Veda’s team of scientists carefully monitors the data’s success rate, creating statistical models, sample sizes, and methodologies to consistently guarantee accuracy. This process ensures specialty and data demands are evaluated and performing at the highest level.
Data Reconciliation: As temporal data evolves, Veda’s entity resolution process follows. Our technology accounts for data drifts over time, so our entity resolution is calibrated to recognize correct data from the abundant sources available today. New data is always cleansed and standardized, then consolidated within a database to eliminate duplicates.
Test Outcomes: Even with 95%+ accuracy, Veda doesn’t rely on automation to do all the work. The Veda team inspects all aspects of delivered data, including quality, delivery methods, bugs, and errors with a continuous monitoring process. By continually auditing and testing our data fields to confirm they are the competitive, current, and optimal quality we know reasonable coverage is reached.
Coverage, Precision, and Recall are numbers reported and recorded by the science team.
Coverage: What is the fraction of the data that isn’t blank?
Precision: When we do have an answer, how often is it right?
Recall: If we should have an answer, how often do we actually have it?
“Anyone can make an API. They are flashy, they can help operations, they can automate processes. But if your API is pulling in duplicative, inaccurate, and just plain bad information it’s useless,” says Dr. Robert Lindner, Chief Science & Technology Officer at Veda. “With our science backing, Veda’s data is guaranteed accurate and with flexible query so data delivery is where, when, and how users need it.”
Veda’s data is currently being used by top health plans for the correction and cleansing of their directories. Now, customers, new prospects, and new channel partners have direct access to Veda’s best-in-class provider information based on their nuanced business use case.
Vectyr has profiles on more than 3.5 million providers who have an NPI 1 number—including MDs, DOs, RNs, social workers, DDS, and pharmacists.
What can health plans do with Veda’s data? Staying atop changing information ensures provider directories are always accurate. This is no small feat as 20-30% of all provider directory information changes annually. With Vectyr, health plans can offer a better experience for members and providers by:
Expanding network offerings: Members need both provider options and location access to get the care they need. Using Veda’s data can help health plans identify providers they aren’t currently contracted with and fill geographic or provider gaps in their network.
Sourcing correct providers for referrals: Providing accurate and on-the-spot information for in-network referrals relieves administrative burdens and eliminates frustrating hours spent searching for answers.
Quick credentialing: Credential providers faster and deliver faster onboarding and credentialing support with data that’s updated every 24 hours and guaranteed accurate.
What’s possible with optimal provider data? There are immediate benefits to using Veda’s data. Health plan members will no longer wonder if their doctor of choice accepts their insurance or where the closest allergist to their home is. Hours of phone calls and administrative burdens are eliminated for both the member and the health plan. And, most importantly, health plans can trust Veda’s rigorous scientific validation methodology to ensure they have the optimal data for every provider in the country, on-demand, every day.
When health plans have access to optimal data, it means members have access to optimal data and that results in a markedly better customer experience.
Automation, Machine Learning, and the Universe: Q&A with Veda’s Chief Science & Technology Officer and Co-founder, Dr. Bob Lindner
Veda’s science department is dedicated to solving complex data issues with creativity and imagination. Learn more about the head of the department, Dr. Bob Lindner, Veda’s Chief Science & Technology Officer, and his journey to co-founding Veda in the Q&A below.
“With automation, we’re not focusing on moving work from humans to machines, rather, how to amplify the power of humans to be more capable in what they’re doing.”
Describe your science background. How did you get interested in astrophysics?
I grew up in Rome, Wisconsin, a small town where you can see a lot of stars and the Milky Way. I wondered about the stars a lot. I was interested in Star Wars and science fiction things. In school, physics seemed like the thing for me.
I wasn’t sure what the jobs were in physics, but I knew there were jobs out there in that field and I found it fun to study. Physics led me into astrophysics and then in grad school, I got involved in observational astrophysics.
For those of us not in the science world, what kind of work are astrophysicists performing?
In my world, I was collecting data from telescopes. This was a lot of fun, hectic, and chaotic because I got to travel around, collect the data, run the telescopes, and analyze the data. One thing was always true: The data is always a mess.
Scientific observers are like the front lines of the science world. The crazy uncalibrated data from the brand-new telescopes lands on their desk.
I spent a lot of years handling this kind of data and making it easier for scientists to work on it. I released the machine learning code Gausspy in 2017 which automates and accelerates the ability for scientists to analyze data from next-generation telescopes. With Gausspy, scientists can test theories using the increased data from bigger telescopes to find out why stars form, why they age and die, and get much closer to understanding the most fundamental question of why we are here.
Automation seems like a natural progression; how did that lead to healthcare data?
When I was a postdoc, I got interested in where else this automation could happen. Of course, there are challenges in science with data standardization, but other sectors experience this too. I got more interested in the process of handling the data, rather than whose data it was. Even in science, I hopped between a lot of subfields of science like radio, infrared, submillimeter, and x-ray and that’s because a lot of times, the data processing challenges are what guided me and not a single science question.
Then I got connected with Meghan to analyze data in the political world. It became clear healthcare data was a more complex and necessary problem that needed tackling, leading to the creation of Veda.
You’re seeing complexity and data problems in many industries.
Yes, Veda has a lot of commonalities that span all industries. The patterns to handle problems within data are stunningly similar. Lots of fields have data that is a numerical value. Perhaps the data is missing, or corrupted, or has an outlier. The way to fix it is largely the same. For example, take text categories and classifications. Galaxies have text classifications like spiral, elliptical, merger. Similarly, doctors have text classifications; these would be specialties like pediatrics and internal medicine.
The way to handle how to classify something into its correct text phrase has a lot of commonalities. It’s important to really understand the domain of the data you’re analyzing but that’s the final flavor for a lot of techniques that span industries.
Another example is time series data. This is one value changing over time. Whether that’s the value of the intensity inside a telescope receiver, or it’s the current stock price of a U.S. security, or it’s the present location of a healthcare provider, tracing it across time has a lot of commonalities. Seeing the patterns of how data behaves across industries has been a lot of fun. It summarizes my background because it explains why it’s in so many different places.
With healthcare data analysis, it wasn’t a huge pivot. It’s really doing the same thing for a different industry.
“Messy data that you want to extract reliable conclusions from spans every topic and institution in the world.”
We have astrophysicists working at Veda, how does that background align with the work the science department is performing?
Astrophysics is a great crash course in what to do with an enormous amount of inaccurate data. It’s the norm and everyday life in that field. The telescopes of the modern era produce terabytes of data every day, you need to get used to having low expectations of how high quality the raw data is going to be.
With healthcare data, people look closely at their databases and find it way lower quality than they are expecting. It can be a stumbling block or even a brick wall to analyze it unless you’re staffed up with folks who have a high fortitude for getting started in such suboptimal conditions.
How does this group of problem solvers get past complex problems?
The project has to move on and so you need to find ways to mitigate, manage, handle, and circumvent all these data suboptimalities. Plus, these elements are not all equal.
It sounds simple but you must take the important things seriously and move past the things that are less important. The important trick here is deciding what effects are the most important ones and which ones can you come back to later. That comes with the process of measurement—making accurate measurements of what the impact of different effects is going to be. If you can do that, it becomes manageable. If you have 15 problems in your data set and you can rank those in order of magnitude, then you don’t have to tackle all 15 at once. You can tackle the first two and make huge gains and you may not come back to the lower ones because you’ve moved on to another priority. That’s really the scientific method. It’s saying “I know there are problems, but I don’t know what to do next. Let’s measure it and see what the data says.” Using those measurements will guide what we do next.
What is your big-picture goal at Veda?
At its highest level, I’m focusing on making sure our technology helps people help people. With automation, we’re not focusing on moving work from humans to machines, but rather, how to amplify the power of humans to be more capable in what they’re doing. We want to empower the users with the power of automation.
Automation and AI frequently get a negative reputation from the public–taking away jobs, and being emotionless. For the most part, machines are actually really narrow in what they can do well. Humans are unmatched at solving problems when a wrench is thrown into the system. Something you didn’t expect that does not conform to rules that the system was wanting to do, a left-field problem. In this area, humans have a problem-solving ability that can never be removed from our philosophy of how to solve problems.
Solving a lot of the messier and more important problems in the world requires end-to-end attention. You can’t cut out the creative power of humans. You need to have them close by in your process.
Tell me about a day in the life of Dr. Bob. What are you doing today?
I make sure I’ve had plenty of caffeine then I do morning Zoom meetings and check in with the team, looking at plots of various kinds, writing code, querying databases, and sketching on paper.
Paper? That sounds pretty analog.
I have a tin of actual pencils with a sharper. When you really need to sketch something out creatively, you can’t be limited to the digital world. You need to put the lines on the paper and you can add structure as you go forward.
Outside of Veda, what things are intriguing to you in science right now?
The pictures of the universe coming out of the James Webb Space Telescope are capturing my attention. The universe is huge and we’re still tiny and that is immensely interesting to me.
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.
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