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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

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.

Connect with Dr. Bob Lindner on Twitter and 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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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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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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