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Why Attestation Isn’t Sufficient for Quality Provider Data

Veda solves attestation problems by harnessing the power of AI and machine learning to automate manual data-gathering and validation processes

Attestation is necessary for compliance, but it fails to deliver quality provider data. At Veda, we’ve spent years measuring and monitoring the accuracy of attested data and its impact on quality—it just falls short. Attestation isn’t sufficient to achieve quality provider data.

Attested data sources are updated slowly through manual workflows that are susceptible to human error, and some providers never update information at all. It doesn’t work well and requires providers to act outside their busy days just to attest. It’s abrasive and providers dislike the process.

The risk of error, and patient dissatisfaction, is high when attested data is the source. Take one recent “secret shopper” example from a senator in Oregon. His staff made over 100 calls to make an appointment with a mental health provider for a family member with depression at 12 Medicare Advantage insurance plans in six states. The callers could only get an appointment only 18% of the time. That means more than eight in 10 mental health providers listed in provider directories were inaccurate or weren’t taking appointments.

Attesting is so burdensome that smaller or private practices—like many in the psychiatric workforce—do not participate in health plan networks because of the administrative burden.

At Veda, we work to achieve member satisfaction and ease the administrative burden as our definition of accuracy is the same as health plan members—”Can I easily find the phone number to call and make an appointment with [X Doctor] at [X Location]?”

A Better Way to Source Quality Provider Data

There is enough existing data to solve provider data accuracy problems, within current workflows, without relying on doctors to attest. We use the data providers generate every day, curated from over 100,000 unique sources, optimizing results for each provider, every 24 hours.

At Veda, we work to achieve member satisfaction and ease administrative burden as our definition of accuracy is the same as health plan members—”Can I easily find the phone number to call and make an appointment with [X Doctor] at [X Location]?”

Veda’s solutions are unique and proprietary. We employ rigorous scientific validation methodology to ensure we have optimal data for every provider in the U.S. on-demand, every day. Our comprehensive data set includes over 50 key data elements including demographic information; specialty & credentialing details; practice locations & group affiliation information; as well as contact information appropriate for making appointments. All without the attestation that isn’t sufficient for quality provider data.

Access To Comprehensive and Accurate Provider Data

We offer three unique products to address provider data challenges.

Velocity Process Automation automates the manual effort of provider roster updates. Velocity applies predetermined business rules to unstructured and disorganized roster files to quickly compare incoming data to an existing directory, validate it against external data sources, and enhance it with critical missing data elements
Quantym Data Quality Scoring analyzes entire provider directories, addressing the most at-risk data fields and identifying areas that may affect overall quality metrics.
Vectyr Data Curation offers access to ready-to-query data to help manage overall provider and directory accuracy by filling gaps in missing or incorrect information with complete provider profiles. We provide these profiles for providers of multiple types, including physicians, nurses, allied health professionals, behavioral health specialists, pharmacists & dental providers.

The Impact of Bad Healthcare Data

The information included in provider directories changes often and the scope of required information keeps expanding. Practices move, physicians change practices, and contracts between practices and health plans expire. According to a report from CAQH and AMA, 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.

Only when data is accurate, timely and contextually relevant, can we make actionable decisions that positively impact patients.

In the health plan space, we saw that bad data was driving claims fallout, bad patient interactions, and sanctions. It was also impacting members of health plans who weren’t able to find the right doctor to access care, like in the case of the secret shopper experiment above.

Compliance is table stakes, which is why Veda doesn’t stop at getting the data right for the sake of CMS audits. Only when data is accurate, timely, and contextually relevant, can we make actionable decisions that positively impact patients.

In October 2022, CMS asked for public input in creating a national directory; a system in which it would collect information from providers and compile it into a single directory maintained by CMS. While an important undertaking, officials note there are many unanswered questions such as managing information for Medicare and private payers.

Luckily, provider directory problems are being solved right now by Veda’s innovative technology. Veda’s offerings are ushering in a new day where data is not a burden to doctors, nor an obstacle to patients. Innovative solutions already exist to connect individuals to the healthcare they so desperately need. All without the need for taxpayer dollars or the use of valuable CMS resources that could be dedicated to other deserving initiatives.

Our solution can mitigate the manual lift from multiple sources, and streamline the workflow with guaranteed accuracy and turnaround.

Having different authoritative sources depending on the data contributes to the difficulty of health plans and practices in keeping information accurate. Our solution can mitigate the manual lift from multiple sources, and streamline the workflow with guaranteed accuracy and turnaround.

The Veda Approach to Provider Data Quality

Attestation-free: We don’t ask doctors to use portals or rely on attestation to validate.
Evidence-Based Data: We utilize doctors’ current data usage to build evidence where they practice. The result? No human error and real-time updates.
Higher-standard for Accuracy: Our definition of accuracy is the one members care about—”can you actually see this provider at this location?”
Proven Methodology: Our roots are in science. We leverage the scientific method to understand and optimize performance.
Unique, Patented Technology: Proprietary solution backed by five existing patents and more pending.
Performance Amplification: Option to layer in your existing data—claims + live call audits—to optimize platform processing.

Guaranteed Outcomes

Speed and accuracy outcomes are defined in our SLAs and brand-defining for Veda. We stand by our data, unlike any others in the market.

Ready for high-quality provider data that is attestation-free? Request a free data assessment from Veda.

The Impact of Provider Data on Star Ratings

Medicare Advantage plans saw the largest-ever decline in Star Ratings in 2023. How can provider directory accuracy boost your Star Ratings?

As Open Enrollment began last fall, Medicare Advantage payers saw a big hit to their 2023 Star Ratings. While some plans managed to weather the ratings storm, some saw drastic changes. In 2022, 68% of Medicare Advantage plans who offer drug coverage had a Star Rating of four or more. That metric dropped 25% for the 2023 ratings with only 51% of Medicare Advantage plans boasting a four or more.

“It’s estimated a Medicare Advantage plan with 100,000 members could lose $15 million in revenue with that lost .5 star.”

With 38 measures to assess the quality of care delivered to Medicare Advantage with prescription drug coverage members, the Star Rating System is administered by the Center for Medicare and Medicaid Services (CMS) and serves as a benchmark for the industry. Maintaining a high Star Rating is essential for health plans to reach enrollment goals as members use the ratings to compare and choose between plans in a competitive marketplace. The highest-rated five Star plans are able to market and sell their Medicare Advantage plans outside the standard enrollment, giving them an advantage when obtaining new members. Not to mention, the ratings also determine the size of the bonuses plans can receive from CMS—any plan rated four Stars or above receives a 5% quality bonus from CMS and has their payment benchmark increased.

What happened with the 2023 Star Ratings?

The steep decline in Medicare Advantage Star Ratings in 2023 was attributed to many factors including fading pandemic flexibilities and members feeling dissatisfied with their plan.

The value of member experience in Star measurement significantly increased in 2023 as member satisfaction played a much more dominant role than they have in previous years. Just how important is member satisfaction when the CMS Medicare Current Beneficiary Survey is also looking at details like cost of care, health disparities, and the number of tests performed? A Gartner study stated member experience metrics represent 57% of an individual health contract’s overall Medicare Advantage Star Rating.

Business impacts for loss in Stars

Projected earnings for insurers take a big hit when Star Ratings drop. Lower-rated plans hope to avoid a drop in enrollment as members compare plans and look for the highest Stars. The competition can be fierce for plans that are looking to hang on to their current members. We know members are comfortable making a change as 22% of those who select a Medicare Advantage plan switch health insurance plans in the next year. Losing members, and enrolling new ones, is costly to plans.

“A Gartner study stated member experience metrics represent 57% of an individual health contract’s overall Medicare Advantage Star Rating.”

Another significant source of income for Medicare Advantage plans is the quality bonuses paid by CMS. MA plans will receive an estimated $10 billion in bonus payments in 2022, according to an analysis by the Kaiser Family Foundation. Losing out on those bonuses will lower overall earnings and projections for the following years.

An uphill Star Ratings battle on tap for 2024

The outlook for next year’s ratings doesn’t look much better for payers. Maintaining and improving Star Ratings is set to become more difficult with the Tukey Outlier Deletion methodology that begins with the 2024 Star Ratings. According to CMS modeling, about 16% of plans could lose at least a half of a star in 2024. It’s estimated a Medicare Advantage plan with 100,000 members could lose $15 million in revenue with that lost .5 star.

How can Veda help health plans boost their Star Ratings?

If your health plan saw your Star Rating drop or are looking to hang onto your current rating, now is the time to get prepared for October’s open enrollment and future ratings. Veda has found accurate provider data is tied to high Star Ratings and existing customers have seen a lift due to their improvements in the directory quality and roster processing.

Veda can directly address two sections of the CMS Medicare Current Beneficiary Survey with our platform’s Smart Automation and access to our national provider database. Accurate provider data from Veda impacts key aspects of the survey, such as:

1. Ease of access to care: How simple is it for your members to find the information they need to obtain?

a. In the last 6 months, how often did you get an appointment for a check-up or routine care as soon as you needed?

b. In the last 6 months, when you needed care right away, how often did you get care as soon as you needed?

2. Quality of member experience: Inability to find a doctor leads to poor member experience.

a. In the last 6 months, how often did you get an appointment to see a specialist as soon as you needed?

b. In the last 6 months, how often was it easy to get the care, tests or treatment you needed?

“If your health plan saw your Star Rating drop or are looking to hang onto your current rating, now is the time to get prepared for October’s open enrollment and future ratings.”

Put yourself in the member’s shoes, imagine a scenario where you are searching for a specialist that is in-network, within 20 minutes of your home, and is accepting new patients. Inaccurate address information in a directory makes the provider you choose seem closer than they actually are. When you finally get an appointment, you find it’s two hours away. Maybe on this journey, you dialed incorrect phone numbers or weren’t even given an updated list of doctors as a new specialist started last month and hadn’t been put into the directory yet.

Put simply: Quality provider directory information means easy appointment booking for members. No more endless phone calls searching for a provider nearby and no more wondering if a surprise bill will arrive due to an out-of-network provider visit. When provider directory challenges are addressed, members and health plans both win.

What else can Veda’s provider data do?

Veda’s technology helps ensure payers meet or exceed CMS compliance benchmarks. Health plans can keep enrollment and Star Ratings in good standing, all while reducing data processing time by 98% and improving data accuracy to 95% or higher.

Prepare for future Star Ratings with a free data assessment from Veda.

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

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.

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?

Get your provider data assessed by Veda.

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.

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.

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!

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.

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.

How to Thrive and Find Belonging in a Virtual Workplace

Staying connected 3,000 miles from headquarters

By: Katie Titus, Veda Customer Success Manager

I live 3,330 miles from Veda’s Madison, Wisconsin headquarters in the small city of Fairbanks, Alaska. I’m a people person and an extrovert who gets energy from sunlight and social interaction. Then why you might ask, do I live in interior Alaska and work remotely?

I joined Veda upon finishing my graduate degree in public health. Since my husband is in the military, I knew we’d be moving around and that I needed a remote job. Luckily, due to the pandemic, many companies were offering remote work at the time. While scrolling LinkedIn, Veda’s deep purple brand logo, unique founder story, and mission to make our health system more efficient immediately piqued my interest.

“After interviewing with my now-colleagues, who made me feel comfortable, confident, and welcomed, I knew it was the perfect fit.”

Shortly after becoming a Vedan I learned that my husband and I would be moving to Fairbanks, Alaska sooner than expected. In the middle of November, after having worked at Veda for only one month, we started the 100+ hour drive from Columbus, Georgia to Fairbanks, Alaska – all with Veda’s full support. Veda is a fully work-from-home company but I would now have the distinction of being the most remote remote worker.

It all happened very fast: New company. New place. New darkness. New version of cold I wasn’t accustomed to. New house. New car. And having only been married seven months, a relatively new husband.

It didn’t take me long to realize I needed to find purpose and belonging in order to survive in all the newness.

In a matter of three months, I learned. I learned to shovel myself out of a snowstorm. I learned to put heating oil into my home’s oil tank. I learned to bundle up and run despite -40 degree temperatures (yes, that’s a negative sign in front of that 40). I learned I could run toward a moose and it wouldn’t charge me. And one of the most rewarding things, I learned to find belonging at work from 3,330 miles away.

10 Ways I Found Belonging

Seeing as remote work has become more commonplace, I’m offering up the specific things I did to connect to my colleagues and work. These helped me feel like a part of a team and fit in even when I was physically distant:

I trusted my manager and VP to direct my work. Through months of conversation, they helped guide me toward my goals and eventually accomplish them.
I dressed the part—complete with professional outfits, Zoom backgrounds, and a workspace I was proud of. That way, if anyone wanted an impromptu meeting, I was ready and excited to join.
I was aware of how much space I was taking up during virtual meetings. In college, I learned the phrase “take space, make space.” It has always stuck with me. I assessed how much I was talking, and made sure others were also getting to contribute to the conversation.
I owned my personality. With remote work, I find it easy to feel replaceable because others can also get the work done. I felt like my personality could set me apart. It became about how I did the work and with what attitude I did it that mattered most to me.
I found meaning in the small things. If a 1:1 went really well with my manager, or my whole team joined a Zoom call to collaborate in what we call Zoom parties, it made my day.
I learned to leverage my manager who taught me to never stop learning. The more questions I asked, the better we both became at communicating and working together to make our work lives more productive and fruitful.
I learned to make a list of my accomplishments at work for days when I have imposter syndrome. (I also learned this from leveraging my manager.)
I didn’t make work my everything and I didn’t expect work to be everything from my coworkers. I learned that CEOs, Vice Presidents, managers, and board members are people at their core with life responsibilities outside of work too. I learned it was okay to take and enjoy time off because my coworkers understood and related to living life outside of work.
I viewed travel as a privilege and not a burden when I participated in in-person events.
I connected with my coworkers even when it didn’t come naturally. Sometimes connection is natural and when it was I would go with it. Sometimes, however, connection is uncomfortable, and I learned to try despite the discomfort because even if there wasn’t an immediately apparent reason to connect, there was someone on the other end of the Zoom trying to find belonging too.

Conclusion

I feel lucky to work at Veda and definitely feel like an important part of the team. However, it took hard work and time to get to this level of comfort and belonging. Working remotely, and in a time of economic and global instability, is an ever-changing challenge. I continue to lean on those I trust for guidance and find joy in the journey.

Katie Titus is a Customer Success Manager at Veda. She’s a proud dog owner of the cutest puppy on the pet-pic Slack Channel, Nali. She earned a Master’s in Public Health from UC Berkeley in September 2021 and started with Veda shortly after in October 2021. She is passionate about expanding access to healthcare and feels her work at Veda contributes to this passion. When she’s not working, Katie is chasing adventure, spending time with her family, and sharing joy through health and fitness.

See open positions at Veda.

How Smart Automation Brings The Healthcare Ecosystem Closer To True Interoperability

Most of the public discourse on interoperability has been centered around EHR vendors and clinical data, in large part because the Office of the National Coordinator for Health Information Technology (ONC) is requiring that these vendors make an expanded set of personal health data available by October 2022. However, EHR data represents just a fraction of the massive amounts of data that are constantly being harvested in healthcare. And ONC’s mandate represents just one of many interoperability scenarios.

“WHILE THE LACK OF INTEROPERABILITY IN HEALTHCARE CREATES ENDLESS BARRIERS, STAKEHOLDERS ACROSS THE SYSTEM TRULY NEED AI SOLUTIONS TO HELP THEM CONNECT THE DOTS WHEN IT COMES TO BOTH RECEIVING DATA, SHARING THEIR OWN DATA WITH OTHER ENTITIES, AND MINING ALL DATA FOR MEANINGFUL INSIGHTS.”

Given the industry’s host of disparate IT systems—layered on top of its tendency to customize technology platforms—there are myriad examples of how extremely challenging it is to derive value, produce accurate insights, and achieve connectivity from healthcare data. And the fact that all of these different systems are unable to communicate with one another and properly exchange information is one of the healthcare industry’s most critical issues.

True interoperability is the long-term goal for the industry—some would go so far as to say it’s the golden ticket for delivering patient-centered care. But like other major initiatives intended to address the inefficiencies of the healthcare system, such as value-based care, we are still far from a universal solution. In the meantime, providers, payers, and other key stakeholders are hungry for solutions that will help them connect their existing legacy IT systems and enable them to share data across systems. Fortunately, companies like Veda now offer relief through Smart Automation solutions that can be implemented both in the near term and the future.

Common interoperability issues

The ultimate reason to strive for global interoperability is to improve patient care and make it easier for all stakeholders to navigate healthcare’s messy data environment. While strides in sharing health data have been made, there are still many hurdles to jump.

“THE NEW PROVISIONS OF THE 21STCENTURY CURES ACT ARE DESIGNED TO IMPROVE HEALTHCARE’S IT ECOSYSTEM IN THE LONG RUN, BUT IN REALITY, IT’S UNLIKELY THAT ANY SINGLE PIECE OF LEGISLATION WILL BE ABLE TO SOLVE ONE OF THE MOST COMPLEX AND LONG-STANDING CHALLENGES IN THE INDUSTRY.”

As mentioned, the most universally acknowledged and long-standing interoperability problem in the healthcare system is around electronic health records (EHRs) data-sharing. Today, approximately 90% of hospitals and physician practices use EHRs. But even when two hospitals have the same EHR vendor, it’s so common for hospitals to customize their systems that in the end, oftentimes neither hospital is able to fully “talk to” each other and easily share information. Many in the industry have set their sights on Health Level 7 (HL7) as a universal solve for this problem—it makes sense that using a standard language would improve data sharing. However, while HL7’s goal is to function as a bridge between modern healthcare systems, it’s also being customized by healthcare organizations, much like EHR platforms.

Although it receives less attention, for payers, the provider roster data processing problem is just as significant of a problem as the EHR data sharing issue, especially now that the No Surprises Act (NSA) is being implemented. It’s so complex an issue that many industry insiders have deemed it unsolvable. More often than not, insurers’ member-facing provider directories are outdated and riddled with errors and inaccuracies. Patients could go through as many 40 entries in a directory before they actually find a provider who can address the health issue they’re experiencing and who is in their geography. Not to mention that it can take up to 6 weeks for new provider information to get updated in the directories. This creates a significant barrier to patients seeking care, one that would never be tolerated or left unsolved by a retailer. If a consumer went on a website like GrubHub, for example, and the first 40 restaurants that came up in their search results weren’t in their delivery area—GrubHub would likely be out of business.

“VEDA’S AUTOMATION IS PARTICULARLY ATTRACTIVE BECAUSE IT CAN DO ALL OF THIS WITHOUT REQUIRING AN ORGANIZATION TO OVERHAUL ITS EXISTING IT INFRASTRUCTURE”

The reason this issue exists is that health plans are continuously processing enormous amounts of provider data that’s not being shared through a common platform between both payers and providers. Payers are ultimately receiving human-generated, messy, and incomplete data spreadsheets from providers, formatted in many different templates. Updates to the roster data can take weeks on end to process, end up costing millions of dollars a year, and still have accuracy rates as low as 60%. This is a problem, as health plans rely on this data to make updates to their directories, along with impacting how providers are paid.

Automation: A solution with both short- and long-term potential

The new provisions of the 21stCentury Cures Act are designed to improve healthcare’s IT ecosystem in the long run, but in reality, it’s unlikely that any single piece of legislation will be able to solve one of the most complex and long-standing challenges in the industry. Given the state of the healthcare ecosystem and the growing number of data sources, AI solutions like Veda’s will be as crucial for achieving data connectivity in the future as this seminal piece of legislation and others that will come after it.

And in the near term, prior to legislative format consolidation, stakeholders across the system (including payers) need AI solutions to help them connect the dots when it comes to both receiving data, sharing their own data with other entities, and mining all data—regardless of its origin—for meaningful insights. Veda’s automation is particularly attractive because it can do all of this without requiring an organization to overhaul its existing IT infrastructure or communicate in a standard language like HL7. The technology is able to sit between disparate systems and act as a translator for the data coming out of each. (Not to mention the major cost efficiencies achieved through automating rote manual tasks that do not require a human brain to execute with accuracy.)

This same automation that helps healthcare organizations function in the absence of true interoperability offers many more benefits. Through Veda’s technology, customers also gain the ability to more easily address compliance at both the federal and state levels, achieve both cost savings and productivity gains, reduce backlog, increase data quality to further cut costs downstream, and more. Existing Health plan customers see improvements across Medicare star ratings (specifically fields related to ease of access to care and quality of member experience), reduce their overall risk exposure (i.e., from sanctioned providers, poor claims system quality, or violations of the NSA), and streamline referral management.

The future of interoperability

Complex, messy data, which is pervasive throughout the entire healthcare ecosystem, creates equally complex issues–not just from a data processing and analysis perspective, but across the whole system. Data sharing and the struggle to achieve interoperability are some of the most difficult and important challenges in the healthcare industry.

Schedule a demo to see how Veda’s science-driven approach can help you optimize data for your organization.

For key stakeholders in the space, waiting on legislation may not be ideal, and as mentioned, there’s ultimately not likely to be a one-size-fits-all approach to achieving interoperable health information exchange. Smart automation is exactly what healthcare organizations need to overcome the lack of data integration across industry systems and bridge data connectivity gaps, both now and in the future.

Why Healthcare is Behind in AI and How The Industry Can Catch Up

Artificial intelligence (AI) and machine learning have proven their worth in numerous industries—social media platforms that are perfectly curated to your tastes, the ability to shop online for clothes, groceries, and even real estate and cars (not to mention cars that drive themselves). The healthcare industry however, lags behind others. In this post, we’ll discuss why this happened, how automation solutions can help process and surface insights from the masses of data flooding the healthcare system, and what the future will look like for patients and plans alike when healthcare catches up and embraces automation.

WHY HEALTHCARE IS BEHIND WHEN IT COMES TO AI

There’s an understandable extreme level of caution around embedding automation in healthcare systems and technology; lives are on the line, and if there were ever an industry where it’s critical that humans make major decisions, healthcare is it. That being said, many of the decision-makers in healthcare lack an in-depth understanding of the current capabilities of these kinds of tools, the use cases for them (many of which are administrative rather than clinical), or the mechanisms put in place to ensure humans remain in control of patient care.

A holistic view of a patient’s health is just out of reach in the absence of tools that make data processing efficient.

A second reason AI hasn’t achieved deep penetration in healthcare is the state of the industry’s technology. It wasn’t too long ago that hospitals housed huge document storage rooms and hired file clerks to sort, alphabetize, and distribute medical documents into physical patient folders. Although electronic health records (EHRs) are now the standard, every hospital has customized its installation, making it difficult for these systems (even those from the same manufacturer) to “talk” to one another. There are many examples of technology not standardized across the industry. The typical national payer, for instance, uses up to 15 technology tools and platforms to meet the needs of its members. But interoperability is an issue—only a few of these systems can communicate with each other.

Further complicating the picture, is the very nature of healthcare data. There is not one standard way of recording and translating data between healthcare institutions or corporations, or even systems within the same corporation. Because of that, it makes it very challenging for an automation algorithm to predict and understand errors in the data (…but not impossible, as we’ll elaborate on below). It’s much easier to leverage automation for Uber, DoorDash, or Amazon, because the data is generated by machines, and therefore inherently controlled and clean. The humans who run healthcare are anything but standard, on the other hand. Each has their own way of understanding and organizing data points (language, phrasing, punctuation, emojis, and shorthand). It takes incredibly sophisticated algorithms to process an Excel spreadsheet created by a person.

HOW AUTOMATION SOLUTIONS CAN PROCESS AND SURFACE INSIGHTS FROM THE MASSES OF DATA FLOODING THE HEALTHCARE SYSTEM

Given all these barriers—particularly the “messy data” issue—some question whether it’s even possible to successfully leverage AI and machine learning in healthcare. The answer is a resounding, “Yes.” As tech platforms intended to advance care continue to proliferate, so do the data they generate. The problem in healthcare today isn’t a lack of data; it’s actually the inverse. There’s so much data that neither administrators nor clinicians can successfully process all of it and extract value. A holistic view of a patient’s health is just out of reach in the absence of tools that make data processing efficient.

A smart solution like Veda’s can step in as a “Rosetta stone” to translate this messy data and process it in just hours and with 98% accuracy.

Luckily, in the past few years, automation algorithms have become more sophisticated, with a “next generation” of solutions that are capable of parsing the messy, human-generated data that permeate healthcare now emerging. There are almost endless use cases for putting such sophisticated solutions to use, but one that’s very easy to understand is using AI to make the search for in-network care simpler for patients.

Health plans are constantly receiving updates from providers in their networks, such as where they are located, who has joined or left a practice, and more. Currently, most plans have staff manually inputting these updates from Excel spreadsheets into their unique systems. As a result, updates take up to six weeks to show in the patient-facing portals, and the accuracy of the entries can be as low as 60%, despite payors’ best efforts.

A smart solution like Veda’s can step in as a “Rosetta stone” to translate this messy data and process it in just hours and with 98% accuracy. Veda’s AI understands human-generated data points, in all their diversity, and makes it possible for healthcare organizations to exchange data seamlessly. The provider directory use case is just one of many ways that automation can be used to organize and cleanse data, making it possible to extract insights that previously remained locked.

A FUTURE WHERE HEALTHCARE CATCHES UP AND PATIENTS BENEFIT

The pandemic created a huge influx of patient data that overwhelmed healthcare organizations, creating the final push that many needed to finally test the automated solutions they had been wary of for so long. The outcomes of these “tests” conducted out of pure necessity were overwhelmingly positive; patients were receiving the care they needed in a more timely manner, reduced administrative costs and errors, and health plan readiness for compliance with the provision of the No Surprises Act that requires them to make provider directory updates in just 48 hours starting January 1, 2022.

What do we have to look forward to in the future as more and more healthcare organizations adopt automation? We’ll continue to see the $1 trillion annual administrative spend in healthcare go down. We’ll continue to see patients accessing care more easily. And best of all, we’ll see more resources dedicated to what really matters to all stakeholders in the system—patient care.

Veda’s AI understands human-generated data points, in all their diversity, and makes it possible for healthcare organizations to exchange data seamlessly.

Veda’s AI automation solution helps health plans leverage machine learning to process data efficiently and effectively, so you can continuously maintain compliance and improve ROI. Schedule a demo to see what Veda can do for you.

Are You in Compliance? What Health Plans Need To Know About The No Surprises Act

The No Surprises Act (NSA), which is a part of the Consolidated Appropriations Act of 2021 (Public Law 116-260), went into effect at the beginning of this year, on January 1, 2022. The goal of this law is to protect consumers from unexpected medical bills arising from circumstances beyond their control. In an effort to ensure a patient knows what providers are available within their health plan network, one of the provisions of the NSA requires health plans to update their provider directories more frequently (you can review the law in full here).

Three months into the year, we’re now at a point where it’s important for any health plans to ensure that they are updating their provider directories in 48 hours or less, as the law mandates, or be working towards a solution that can meet these critical timing requirements.

WHAT ARE THE PROVIDER DIRECTORY REQUIREMENTS IN THE NO SURPRISES ACT?

The “No Surprises Act” will require all provider directory updates to be processed quickly. This is a pivot from the manual processing and attestation that health plans have traditionally incurred

Update databases and new directory information: All provider directory updates will need to be processed within 2 business days of receipt of changes
Quarterly database quality audits: Validate provider data in databases and directories at least every 90 days

PENALTIES FOR NON-COMPLIANCE OF THE NO SURPRISES ACT

There are mechanisms for enforcement in place at both the state and federal levels. In Q1 2022, CMS began to levy fines for “coverage determination appeals and grievances” (42 C.F.R. § 422.105(a)) and an uptick in fines for non-compliance is likely down the road. Now is the time to assess if you’re in compliance with the new mandates and understand the potential risk they have for your business.

BUILDING YOUR COMPLIANCE CHECKLIST

Assessing your situation today: To understand what changes your health plan may still need to make, we recommend that you ask and answer the following questions:

What are your goals for processing provider data? Obviously meeting the 48-hour requirement should be the topline goal, but some plans may have variations on this goal based on the number of covered lives under their purview and the geographies they cover (some may want for example, to make updates within a 24-hour window).

What process(es) do you have in place for updating provider info? How does your plan deal with messy data coming from provider organizations? Are these processes documented, or is the organization reliant on employees with historical knowledge?

What process(es) do you have in place for verifying the accuracy of provider info? Are there documented procedures for communicating with providers, and are these mechanisms effective? What’s in place to manage providers who are submitting “bad” data?

What are the data points that you currently verify? Thinking beyond basic data such as first name, last name, and specialty… you will want to make sure your plan is also able to track individual and group NPIs, organizational tax identification numbers, whether providers are accepting new patients, and more.

How quickly are you currently able to make provider directory updates? If the answer is weeks, which is often the case for large, national plans, it’s important that new processes be put in place as soon as possible.

What process(es) do you have in place for helping members that are having difficulty navigating your provider directory? The entire purpose of the NSA is to shield consumers from “bad” bills, and to overall improve their experience with the healthcare system. A system that your plan is part of.

How often are you cleaning your provider data in aggregate? In addition to processing regular data updates, what processes are in place to keep your database clean as a whole, and to ensure that “old” data is verified at regular intervals?

USING AUTOMATION TO IMPROVE YOUR SITUATION

Most plans have historically used manual processes—human hands on keyboards—to update provider directories. But with the NSA’s 48-hour requirement in effect, manual processes are unlikely to remain effective, and automation truly is needed. Before bringing in an automation solution, however, you should get educated about their capabilities.

Understand what automation can and cannot do. Automation cannot completely solve the global interoperability problem. What some of the more sophisticated platforms can do, however, is sit between disparate systems and act as a translator.
Assess the situation and set realistic goals. Under current manual processes, how long does it take to make provider updates, on average? What percentage of the updates are accurate? What types of issues does your plan most commonly experience with the data you receive (is it missing column headers and or containing blank fields in Excel files, providers listed at the wrong practice locations, or something else entirely)? The answers to these questions will vary from plan to plan, as will the goals for improvement.
Understand the range of automation solutions available. Not all automation solutions are created equal. Some require a “rip and replace” approach that health plans may find disruptive to existing IT infrastructure, but other solutions can co-exist with current systems. Solutions also vary in terms of the type of data they can automate—your plan should seek out those that are sophisticated enough to deal with the inherent messiness of human-generated data. Finally, you should look for an automation partner that provides human support in addition to technology.

THE ONLY TOOL AVAILABLE THAT ALLOWS FOR COMPLETE PROVIDER DIRECTORY COMPLIANCE

Veda is the only solution on the market today that makes it possible for plans to fully comply with the provider directory provision of the NSA. And we can do it in 24 hours. Our smart automation platform offers the fastest provider roster & delegated network processing available, with guaranteed accuracy thresholds.

Our platform performs multiple functions that increase efficiency and accuracy for provider data processing. Key features include:

Intake: automate manual workflows
Validate: stop bad data from entering your system
Enhance: simplify audits with cleaner data
Compare: integrate to quickly update your database

Learn more about Veda’s automation solutions and why six of the top 10 health plans trust Veda with their automation.

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.

Stay up-to-date on industry trends and discover a wealth of resources on provider data management, roster automation, and AI.

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.

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