Fierce Healthcare covered Veda’s partnership with OutCare Health on September 21, 2023.
Veda, a data automation startup serving payers, has partnered with OutCare Health to help patients and payers identify queer-affirming providers.
OutCare Health, a nonprofit advocating for queer health equity, is known for OutList—what it calls the most comprehensive directory of LBGTQ+ affirming providers. Veda will incorporate those data into the information it offers to payer clients.
The most impactful data vendors ensure top-quality data is being provided to their health plan clients. Data vendors can bring both value and collaboration to health plans’ business. A solid understanding of the vendor’s capabilities, methodology, and process can help you quickly build trust and maximize your ROI— or not.
Whether your health plan is currently working with a data vendor or hopes to do so in the future, these are the questions Veda’s data science team encourages you to talk to your partners about to get the most out of your data. Plus, we included Veda’s answers to the questions.
Provider data is not “set it and forget it.” Providers change facilities, offices move locations, phone numbers are updated, etc. Without consistent updates, there is a risk of data being inaccurate. What was once correct can quickly become void when a clinic moves next door.
VEDA’S ANSWER: Veda optimizes results for each provider every 24 hours.
Entity resolution is the foundation of all data processing, and poor entity resolution can affect results for locations, network adequacy, and provider details. One challenge in provider data is that the information about a provider is not static, and evolves over time—location, phone, specialty, etc.
VEDA’S ANSWER: Veda’s patented technology performs entity resolution in a way that specifically accounts for this data drift over time.
Knowing where source data comes from will help ensure you’re sourcing everything you need and nothing you don’t. Rather than crawling some websites for information that may already be inaccurate, using many sources means the data can be cross-referenced for quality.
VEDA’S ANSWER: Veda curates data from over 300,000 unique sources (including our proprietary data and multiple credentialing sources, such as NPPES, CMS, DEA, and State Licensing Boards).
Data sets should include active providers. Sure, having more providers and large numbers in a roster seems like a win but if the roster is full of inactive or even deceased providers you’re risking a poor member experience. Garbage in, garbage out.
VEDA’S ANSWER: Organizations using Veda’s provider data can access profiles of every active provider in the U.S.—over 3.5 million.
Once you are certain you’re measuring the right outputs, identify the key metrics that support your accuracy KPIs: Aspects such as frequency of measurement, sample sizes, methodology, etc.
VEDA’S ANSWER: Veda’s solution accurately separates data into training and test sets for statistical modeling. This is essential to avoid overfitting and production performance “surprises” from the data.
Are you measuring your performance like your patients and regulators are? We believe everyone needs to think more rigorously about what “correct” provider data means. Attested data is not the same as correct data.
VEDA’S ANSWER: The best measurement for accurate provider data? Patients should be able to make an appointment with a provider, using the data available to them, on the first try.
Ready to partner with a data quality vendor who is the authority on accuracy? Contact Veda.
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]?”
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.
We offer three unique products to address provider data challenges.
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.
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.
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.
What do healthcare and the airline industry have in common? As demonstrated by the holiday traveling disaster, outdated back-office administrative systems in desperate need of an upgrade.
Check out the latest Forbes article from Veda’s Chief Science Officer Bob Lindner, PhD, and learn the three questions to ask when updating behind-the-scenes tech systems.
Our CEO and Co-Founder Meghan Gaffney was interviewed by Carrie Nixon and Rebecca Quilt on their podcast, Decoding Healthcare Innovations—a podcast for novel and destructive healthcare business leaders seeking to transform how we receive and experience healthcare.
During Meghan’s time on the podcast, she dives in and focuses mainly on the impact of “peripheral” healthcare innovations: those platforms and technologies which were not initially designed for healthcare.
In the first portion, Meghan goes into detail on how she realized there was a huge opportunity missing in healthcare, and how automation and technology could fix it. Meghan says, “It felt to me at that time like as these conversations were happening people were stepping over piles of money on the floor to solve really big problems that are important and meaningful to solve like social determinants of health but missing the opportunity that automation and technology could provide to bend the cost curve and save money so that the 25 to 30 percent of our premium dollars that today go to healthcare administration could be reduced and get more money in the system to take care of people, which is where the funding really matters.” She continues speaking about how our other Co-Founder and CTO, Bob Linder helped her vision come to life, and how Veda was born.
Meghan and Carrie continue discussing issues that arise when it comes to looking up providers, which providers fall under your health plan, and data that may not be updated in the system. That is where veda comes to the rescue. Veda’s Velocity platform automates roster intake and takes your unstructured, unwieldy files, and standardizes it to your preferred format for faster, more accurate data management. Using patented AI technology, Velocity enables files to be verified, corrected, and updated within your database… fast.
Carrie closes out the podcast by asking Meghan if she has any advice for the listeners. Meghan goes into some great detail for those who are looking to become entrepreneurs, and for those who are looking to get into the healthcare industry.
Click here to listen or watch the entire podcast episode.
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.
Standardize and verify unstructured data with unprecedented speed and accuracy.
The most up-to-date, comprehensive, and accurate data source of healthcare providers, groups, and facilities on the market.
Review and refresh your network directory to identify areas that affect your quality metrics.
Let’s transform your healthcare data. Contact Veda to learn how our solutions can help your organization improve efficiency and data accuracy.
