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Optimal and Proven Provider Data from Veda

What makes Veda’s data so great?

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.

More about Vectyr Data Curation

People, Patterns, and Physics: Q&A with Veda Data Science Manager, Rishi Patel

Veda’s science department is dedicated to solving complex data issues with creativity and imagination. Learn more about Veda Data Science Manager, Rishi Patel, and his scientific approach (and improv background) in the Q&A below.

rishi gif

You have a history with Veda Co-founder, Dr. Bob Lindner, how did you meet?

I did my graduate degree at Rutgers with Bob. Within the first week of school, Bob and I were in the library looking for the same book. We started discussing and working on assignments together. We’ve kept in touch and encouraged each other in different pursuits in the academic and scientific fields. Bob encouraged me to step out of the lab and get into the world to attack more pertinent problems with the same skill set we used in academia.

For example, during the early start of the pandemic, there was a giant data set of all the COVID-19 literature coming out, including things from the past. Like an article from the 1800s about early pulmonary diseases. It was in PDF and you couldn’t mine it. Usually, all my data is quantitative but this data was text. I’m used to looking at pictures and in this, I was looking at text and breaking it into keywords like particles. If you have enough of certain keywords, you can say “maybe this is really important,” but let me make that decision later and filter out the important keywords.

I was able to take half a year’s worth of COVID-19 literature and combine it into a couple of paragraphs. It proved Bob’s point–that if I tried to take what I learned in the lab and look at a dataset that I have no expertise in, something good would still happen in the end. Basically, we could generate knowledge through experimentation the same way we were doing in our research. I still get messages and questions about this research today.

Talk about your role in Veda’s science department, how are different members of the team approaching science?

When Bob and Carlos Vera Ciro (Veda architect) were focused on the biggest and largest aspects of the universe, I worked on creating small versions of the big bang in a controlled environment. Putting it another way, while Bob and Carlos may see a huge picture, I see little wisps of energy in a particle detector. 

What’s powerful about our comparisons, the methods that they use, and the methods that I use–for what should appear to be very disparate and strangely different–in analyzing data, it’s roughly the same. We approach things in the same way and we have the same divisions: I’m experimentation, Bob is observation, and Carlos is theory. My work requires me to know a little bit about theory and the constraints that come from what we’re trying to prove. To solve provider data problems, you have to have a good hypothesis and that’s likely going to come from seeing a solution in an intuitive way from the perspective of our clients.

Why do you think a health tech start-up even needs an entire science department?

I love that Veda has a whole team dedicated to the scientific process. We want to know “what is true and how do we test it?” That’s a strong competitive edge compared to other companies.

“Having an entire science department, all verifying something is true in collaboration with each other, is more than just the icing on the cake, it is the full entree.”

The incredibly challenging data we work with gets me jazzed. We have to ask, “how do we present this in the simplest fashion for the end user?” That is a powerful engine in data science, not just finding the data and guiding strategy, but creating a picture or a product where there are a-ha moments all day long. Being able to build products that provide those moments makes me super excited. 

A big part of science culture is the ability to break things down so that science is sharable. This is also what keeps me motivated.

Tackling a big challenge is like climbing a mountain, there are highs and lows and points at which you want to give up. It is satisfying to finally make it to the top and learn something about what you are capable of. But then taking a picture from the top and sharing it with the world, that’s the moment when you feel victorious.
 

rishi hula hoop

How did you become interested in science as a career path?

In high school, I hated math and science. I thought it was super boring and I even remember doing an abysmally low amount of math homework compared to any other subject. I thought that I would never major in a hard science or a quantitative field. I thought I’d be a writer and I went to NYU planning to major in philosophy and writing. 

But the real hook for me was how different math was taught in college compared to high school. The math started with the bigger question and then you start to build out answers in a sequential way. It felt like reading a mystery novel– you guess what the right answer might be, test it, and maybe you’re wrong, maybe you’re right. That really pivoted my enthusiasm for math and science. Physics felt real and tangible. You could look at the sun and imagine what’s going on there. Really, you could look at anything in the world, big or small, and try to solve it like a puzzle.

“In science, seeing sparks a kind of wonder, and plants seeds of questions centered around the “how” and the “why.” When the seeds become a forest, the crux of being a scientist is being able to say “I have seen this before, I know how to find my way.”

You previously did research at CERN, the largest particle physics laboratory in the world. How did you end up in the healthcare field?

When you start off as a data scientist, there are so many sectors and you have to find what you’re interested in. There isn’t a perfect fit for going from particle physics data to anything else. I realized healthcare data is very complex because it’s people and patterns.

“People and patterns are way more complex than particles and patterns because all particles behave the same while people of course do not. When you start to look at different patterns in similar contexts, it really challenges your ability to recognize patterns.”

Describe your role at Veda. If someone asks “what do you do at work,” what do you tell them?

I am a data science manager for the data science team. This means, I ask the key scientific questions to try and poke at the data scientists’ arguments and check to make sure that what they’re developing is aligned with the truth. Everything we’re working on must align with both our intuition and the patterns found in the data.

It’s my job to take the problem and break it down into actionable items and testable output. We go from big questions to small actionable questions and test the hypothesis to see if we can continually make improvements in the model.

What data process do you use at Veda?

I am focused on three letters: ETL (Extract, Transform, and Load). This data process is what I’m living every day.

  • Extract. Can we collect the data to inform our hypothesis? What truth can we collect? What is available out there from public records? Could we make phone calls and verify what our models are saying? Is it still up to date? Is there an inefficiency in our model that a human could catch when they ask questions to another human?

  • Transform. This is where the core skills of data science come in. We get a bunch of data but we want to make it useable. We want an answer to give somebody for what they should do based on the data and the patterns we believe are true. For example, for a care location we do a bunch of checks to be sure an address is really valid and give it an accuracy score. We want to make sure our clients know what to do with that score. The transformation always connects to the customer vision and our pattern must be intuitive enough to interpret.

  • Load. Here’s the customer vision. We go back to the model and see it from a customer’s lens. Is the row really valid? What can we do to justify it? For example, does this provider have a state board address and do they have prescribing power? We see doubts from the client’s perspective and make an argument to see how to best prove the decision we are recommending.

When you get stuck on a problem, what is a resource you use to get past it?

I’m part of a start-up collab Slack channel called On Deck where we meet regularly to discuss general data science problems. We get to see what challenges we’re all facing. For instance, how do we communicate really technical things to those who need brief and actionable answers? Or how much time should we invest in this issue? We work through those questions and agree on steps. It’s magical to me that data scientists can come from different backgrounds but when you start to compare notes on a given problem, there are pretty strong steps for how to solve it. It’s reassuring that we walk down similar paths to find a solution, so there is some standardization in the process of being a data science manager.

Say a student comes to you and says they want to be a data scientist. What advice do you have for them?

Can you let things go? The ability to ruthlessly prioritize and reach a more balanced focus is a key part of data science management. If there’s one thing that really irritates you, and you want to get it right, and there are so many other priorities that are more important, you have to let that irritating thing go and solve it later. You can dream up a new idea every day but that might not answer the question you’re trying to solve.

You work at Veda so you’re statistically likely to have a pet. Tell me about yours. 

I have a cat named Luna who has lived in three different countries. She was an Italian farm cat, who lived with an Australian family, then we adopted her in Switzerland. We love her broken tail–there’s always beauty in imperfection!

luna cat
Luna

You did improv and acting while you lived in Switzerland. I can’t imagine there are many data scientists sharing a stage with you. How does this relate to science?

I was always interested in improv and acting. As a scientist you’re always taught to look and judge but in improv, you have to invert that process. You have to accept what’s given to you and do the “yes and.” It’s exciting to see where an improv scene is going to go and how you’re going to respond. That degree of chaos is exciting.

I see science as creativity with constraint. That’s what makes it a cousin of art. You have to think out of the box to solve problems but the constraints are heavy. Improv removes the constraints and it’s fun to build a new world without constraints. 

Connect with Rishi on LinkedIn.

Star Systems Meet Star Ratings: Using Science and Imagination to Solve Healthcare’s Most Complex Data Problems

What the heck does astrophysics have to do with provider data quality?

With an entire science department dedicated to solving complex data issues, science is at the very core of Veda’s existence. After all, our Chief Science & Technology Officer and Co-founder, Dr. Bob Lindner, began his career in astrophysics.

After taking a leap from the academic world and into political data analysis, Bob and co-founder Meghan Gaffney realized the potential of provider data automation. [READ Q&A WITH DR. BOB LINDNER]

The commitment to the scientific method and investment in science is what sets Veda apart from other data and healthcare tech companies—and what led to a robust science department with an impressive five IP and automation patents.

But you might be thinking: what exactly does this background in galaxy-staring, particle-measuring, and the expansive universe have to do with ensuring health plans’ provider directories are accurate?

The answer lies in wholeheartedly embracing the scientific method and Veda’s mission: We blend science and imagination to arrive at solutions for our customers. In fact, Bob argues one would not be able to tackle provider data problems accurately and reliably without a science department.

In the healthcare industry, data changes rapidly, some sources of data claim to be sources of truth but may in fact not be accurate, and data can be a heavily manual process. The only way to uncover the truth is with a careful and accurate measurement process.

Science meets imagination with Veda’s Science Team

Here is an expert from Dr. Lindner on problem-solving at Veda:

There are two kinds of main prediction problems. One where the answer to any given problem is self-evident. You can look at it and immediately know what the answer is. You give this problem a fast feedback loop and design your system to get the right answer based on immediate feedback from engineers. Outside of healthcare, an example is image classification. Is there a smiling person in this picture or not? You can look at an image and immediately tell.

A different kind of problem that we’re faced with every day at Veda is if the answer you’re trying to predict is not self-evident by a trained user in the field. For example, does this provider work at this address? It may look like a reasonable address and provider name but you don’t know if it’s accurate just by looking at it.

The only way to know if the system is working is to be very disciplined with the art of measurement and calibration. You must have a good set of test data that you trust that was collected in a way that was very tightly controlled. And you have to trust you are training your models on the data in a way that’s not overfitting because when your system gets used in production you don’t know—aside from that measurement in comparison to your training data set—if it’s working or not. You have to trust in science fully because if you do that part wrong, by using a biased training set or too narrow of a sample, there are errors that are invisible until you actually try to use the data. It can be a devastating effect. If you have a 10-digit number that says it’s the phone number of a provider, and you can’t call every phone number, how do you know it’s correct? You must have faith in the process. And the process to have faith in is the scientific method.

Dr. Robert Lindner

Provider data is complex and vast just like data in the field of astrophysics. However, provider data is nuanced and complicated in ways that even monitoring billions of stars is not.

The challenges with provider data are more complex than say, finding the largest thing in the universe, because the information included in directories changes often and the scope of required information keeps expanding. Practices move locations, physicians change practices, and contracts between practices and health plans expire. Multiple industry reports state between 20% and 30% of directory information changes annually.

Yet, no single party is the exclusive keeper of this information. Some of the information is governed and controlled by the practice, such as contact information and the roster of clinicians who practice there. Other data, such as whether a clinician is accepting new patients under a specific plan, can be owned by the practice, the health plan, or in some instances, shared by both parties.

Veda’s Science Team

Having different authoritative sources depending on the data contributes to the difficulty for health plans and practices in keeping information accurate.

So yes, provider data is more complicated to monitor than the stars but the Veda science department, using the scientific method day in and day out, can solve complex provider data problems faster and more accurately than anyone else. We start by understanding problems deeply before pairing them with an appropriate model and AI technology.

Before you select a healthcare data vendor, ask yourself, why don’t they have a science department backed by patented IP?



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Resources & Insights

Provider Data Solution Veda Automates Over 59 Million Hours of Administrative Healthcare Tasks Since 2019
October 21, 2024
HealthX Ventures Blog: How Veda Is Aiming to Fix Healthcare’s Broken Provider Directories
October 17, 2024
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