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Is Your Data Partner Ready for New AI Regulations?

How to Find the Right Provider Data Vendor Partnership

It seems that almost every week we see new vendor offerings within the provider data management ecosphere—each claiming to offer a revolutionary way of visualizing your data and making impactful improvements for healthcare. The provider data accuracy challenges that health plans and provider organizations face are vast, so it’s not surprising that new health tech companies are quick to capitalize. Caution: all health tech companies may not be keeping new and developing AI regulations in mind when developing their technology.

All health tech companies may not be keeping new and developing AI regulations in mind when developing their technology.

Looking at the Bigger Provider Data Picture

Quality provider data can help the healthcare industry tackle some of the biggest challenges health plans and provider networks face, like onboarding, credentialing, roster creation, and referrals.

A commitment to improving your provider data can have great ROI potential and long-term impacts on your business and patient populations. But, with all the buzzwords and capability promises from the masses, how do you find the best vendor fit for your business needs?

Necessary Topics to Cover When Vetting Provider Data Vendors

To set you up for partnership success, here are discussion ideas for data vendor conversations:

Find out the vendor’s practices for responsible data collection, storage, and validation. For example, does the vendor keep their data in the U.S.?
Veda’s Take: You want to ensure any vendor interacting with critical data is not utilizing offshore, 3rd party data processing centers that can unnecessarily expose data and therefore, providers’ information. Consider this: If patient data is protected from offshore processing vendors via HIPAA and other regulations, shouldn’t the same protections be afforded to provider data? Veda offers comprehensive security and data protection and is HITRUST-certified.
Understand the vendor’s business policies regarding AI. For example, how does an AI vendor utilize Language Learning Models?
Veda’s Take: Avoid risk by future-proofing your AI policies. Machine Learning can be a powerful tool, but should be approached thoughtfully and aligned with current and future U.S. legislative requirements such as President Biden’s executive order for the Development and Use of Artificial Intelligence. Warning: fewer companies comply with the proposed regulatory requirements than you may think.
Ask about their reporting and measurement. How does the vendor define accurate data and measure it during and after delivery?
Veda’s Take: Data can be compliant but inaccurate and unusable for health plan members who depend on the data to get care. Be prepared to discuss your business goals and thresholds for accuracy—consider going beyond meeting regulatory guidelines. We think healthcare data should truly be considered “accurate” if it meets a member’s needs when accessing care.
Discuss what is necessary for your business success. Can the vendor offer the necessary tools to reach your goals and eliminate what is unproven?
Veda’s Take: Don’t be distracted by tools that may not deliver results or provide value for your goals. For example, APIs are often necessary to save time on automation. Therefore, many businesses focus on the ability to have an API connection and the API integration above even the results the product delivers. Or, in another example, the UX and the interface of a product can become a focal point above the actual functionality of the product. If you can’t trust the data and know how to interpret and use it, then connectivity and appearances don’t matter.
Determine what happens first. Can a vendor partner prioritize your specific business requirements?
Veda’s Take: All businesses have different objectives and these goals greatly impact priorities. Your vendor should clearly articulate what needs to happen first, upon implementation of the product, to realize immediate value and reach success. There is no one-size-fits-all solution when working with a provider data vendor. Before integration and during the initial conversations is the perfect time to establish an approach to prioritization within business rules.
Get familiar with the training process. What does the implementation, training, and delivery process look like for your AI data vendor?
Veda’s Take: How a provider data vendor plans to work with you, and how they plan to train others in your organization, is key to partnership success. Beyond day-to-day use of the tools, how does the vendor recommend using the data and applying the findings? Who should be trained on what tasks? Clear and concise preparation will ready everyone in your organization.

Ensure any vendor interacting with critical data is not utilizing offshore, 3rd party data processing centers that can unnecessarily expose data and therefore, providers’ information.

Once you have a solid understanding of how a potential partner tackles the above objectives, only then can you capitalize on a business case for building a collaborative partnership with a provider data vendor.

Need more ideas on what to ask a potential provider data vendor? See Veda’s Six Questions to Ask Your Provider Data Vendor.

Eradicating Ghost Networks from Provider Directories with Accuracy

The REAL Health Providers Act and Veda’s Accurate Data Approach

The bipartisan Requiring Enhanced & Accurate Lists of (REAL) Health Providers Act, introduced by U.S. Senators Michael Bennet (D-Colo.), Thom Tillis (R-N.C.), Ron Wyden (D-Ore.) calls to eradicate ghost networks that are impacting patients nationwide.

What are ghost networks?

A ghost network is a provider network that appears robust and full of available providers but actually contains bad data and thus, much more limited availability and unreachable providers. These “ghosts” are no longer practicing, not accepting new patients, are not in-network, or have errors in their contact information.

Imagine using a “Find A Doctor” online tool to pick an in-network doctor in the specialty you seek, but when you call to book an appointment, you discover the provider is no longer practicing. That’s a ghost.

Despite the introduction of the bill, recent media coverage, and attention from the Senate Finance Committee, ghost networks are not a new phenomenon. A Yale Law & Policy Review completed in 2021 titled Laying Ghost Networks to Rest: Combatting Deceptive Health Plan Provider Directories declared “…these directories are deeply flawed.”

“ [Ghost networks are a] pervasive issue in the American health care system. A three-phase study of the accuracy of Medicare Advantage directories, which included over 15,000 providers, found that between forty-five and fifty-two percent of provider directory listings had errors, with some individual plans having error rates as high as ninety-eight percent.”

While behavioral health networks are often cited for inaccuracy and the mental health crisis in America has brought adequate network issues to light, inaccurate directories are a systemic issue prevalent across all provider types. All directories have “ghosts.”

How do ghost networks inhibit care?

Besides adding to patient frustration, ghost networks prevent patients from accessing the care they need. Impactful stories, like this one from the Yale Law & Policy Review, shed light on the millions of people dealing with the repercussions of inaccurate provider directories each year:

“KC, who manages her brother’s mental health care, gave up on trying to find an in-network psychiatrist because calling potential providers was taking up so much of her time that it was more cost effective to pay out-of-network rates. “Ironically, I can’t imagine my brother or others in his situation being organized and effective enough to be able to make all these calls and keep track.” [Yale Law & Policy Review]

Additionally, ghost networks have inhibited care by leading to unexpected medical bills. “When Americans are purchasing and using their health insurance, they have the right to know whether their doctors are covered by that plan,” said Ron Wyden (D-Ore). “Too often, seniors and families get health care whiplash when they sign up for a plan only to find out that their preferred doctor is out-of-network, or it’s impossible to find a covered mental health care provider.”

How does the REAL Health Providers Act tackle the issue of ghost networks?

The legislation aims to combat the ghost network problem by, among other things, requiring Medicare Advantage (MA) health plans (beginning with plan year 2026) to verify their provider directory data every 90 days and, if necessary, update that information.

If a health plan cannot verify the data, the plan must indicate in its directory that the information may not be up to date.
A health plan must also remove a provider within 5 business days if the provider is no longer participating in the plan’s network.
If a patient obtains care from an out-of-network provider that a health plan’s directory indicated was in-network at the time the appointment was made, the plan may only charge that patient in-network prices.

The legislation also requires MA health plans to analyze the accuracy of their provider data on an annual basis and submit a report to HHS/CMS with the results of that analysis. HHS/CMS will use this information to publish accuracy scores for each plan’s provider directory.

How does Veda banish ghost networks?

Veda has been at the forefront of efforts to eradicate “ghost networks” in the U.S. for years. Veda understands that progress on this front is essential to connect patients to the critical care they need and ensure that individuals are not burdened with unexpected healthcare costs.

Veda takes a one-two-punch approach to eradicating ghost networks. Veda leverages its patented technology and innovative solutions to first identify where the “ghosts” reside in provider directories and then fills in the gaps left behind once the ghosts have been removed. Using a mix of AI technology, smart automation, and machine learning, Veda’s provider data is proven accurate.

Find the Ghosts

Veda’s Quantym platform identifies the errors in a provider directory

High-volume audit solution that delivers comprehensive, real-time scoring of provider data quality to identify bad data and significantly improve provider directory accuracy

Fill the Gaps with Accurate Data

Veda’s Vectyr tool supplies accurate data to replace the bad data

The most up-to-date, comprehensive, and accurate source of data on healthcare providers, groups, and facilities on the market

Veda understands that ghost networks will not be eliminated by the manual attestation methods of the past. Manual verification is labor-intensive, expensive, subject to human error and time and again, it’s proven ineffective. The fact is, “attested” data is frequently not “correct” or “accurate.”

Veda’s technology takes provider attestation out of the equation and, in doing so, can more accurately identify where ghost networks exist (by pointing out inaccuracies in the health plans’ provider directory data) and provide solutions to the ghost network problem (by filling in the gaps that are created when the bad data is removed).

It’s time to let technology like Veda’s solve the ghost network problem once and for all. Give your members the accurate information they need to make an appointment for care on their first try. Contact Veda for a free data assessment.

Want to learn more about ghost networks? Watch a recording of Veda and Mathematica’s November 2, 2023, webinar, Don’t Get Spooked by Health Care Data: Tackling Zombie Rates and Ghost Networks. Read a summary of the REAL Health Providers Act here.

Leaders of B2B Podcast: Meghan Gaffney

In this episode of Leaders of B2B, Meghan Gaffney, CEO and co-founder of Veda, offers an in-depth perspective on the evolving realm of artificial intelligence in healthcare. With her extensive experience, Meghan underscores the transformative impact of implementing AI solutions in medical diagnostics and patient care pathways.

Tune in to learn why a diagnostic approach is essential for effective data management across industries, to identify and address critical issues systematically.

Supervised vs. Unsupervised Machine Learning: Using The Right Tool For The Job

It’s Veda’s philosophy that any new technology tools we utilize are not meant to wholly replace human engagement. We believe that technology should help elevate humanity. With a focus on performing meaningful work, people can achieve their highest value. Technology should help people help people.

As a Data Science as a Service (DSaaS) company, Veda leverages scientific principles within our unique AI and machine learning systems to perpetually clean, correct, and monitor evolving provider and facility data. A lot of companies claim to offer accurate provider data, but none are as committed to using science to solve deeply entrenched provider data problems.

Dr. Bob Lindner describes supervised vs. semi-supervised learning

At Veda, we use AI systems including natural language processing (NLP), supervised, and unsupervised learning components that can be leveraged to solve a wide array of payer data challenges depending on what tool is right for the job. No matter what, we start by understanding the problem—not by applying a method.

A lot of companies claim to offer accurate provider data, but none are as committed to using science to solve deeply entrenched provider data problems.

The Roles of Supervised and Unsupervised Learning

Supervised Learning

Veda utilizes supervised learning because it doesn’t require “perfect” sources of data— it can make use of the good parts of any data source and knows how to ignore errors. With supervised learning, a data scientist is watching and helping train a model with all the healthcare nuances and industry-specific language, etc. to make the model a good one.

We’ve measured individual sources of data for years—including attestation— and haven’t found any at 90% or above yet. Supervised learning is incredibly accurate with the data we have access to today. The benefit is the highest accuracy which is flexible and not dependent on a single data source.

Unsupervised Learning

We also use unsupervised learning for offline data exploration and research to learn more about a dataset, to help design better machine learning features for supervised learning systems. That’s because unsupervised learning separates big collections of data into groups on its own. The benefit of unsupervised learning is its ability to pick out patterns in the data.

Unsupervised learning separates big collections of data into groups on its own. Some will claim that unsupervised learning, alone, is superior to supervised learning because of the lack of human intervention. However, most algorithms require the user to specify upfront how many groups they want the data separated into. So no matter what data is being grouped, one would have to delineate exactly how many groups are wanted ahead of time and the exact number of groups the data is sorted into regardless of whether the groups match up well with the data. This requires the user to have upfront knowledge of exactly what labels and groups they need.

But the biggest pitfall of unsupervised learning is that there’s no labeled training data, which means there’s no actual measurement of how well it’s working and placing the items into the correct groups. And with no way of knowing how well it’s working, it’s impossible to depend on unsupervised learning as a primary method for accuracy.

The Right Tool For the Job

Supervised and unsupervised learning are tools, and just as you wouldn’t remodel your kitchen and only use a saw, you shouldn’t only use one kind of machine learning model.

Veda’s technology and approach to data challenges are fundamentally different from other provider data technology companies in that we focus on fully automating both the static information and the more challenging temporal information about a provider—data that changes at varied rates over time, like practice address, phone, and group affiliation. Our patented systems do not require manual outreach to providers, rather they rely on data created by providers throughout their established workflows. This increases data accuracy by reducing human error while also decreasing provider abrasion. Validating millions of temporal data elements in real-time requires Veda’s full automation system and could not be solved manually.

Above all, we believe that AI and machine learning are the best ways to solve the provider data quality problem because:

These techniques do not require us to know how accurate our sources of data are ahead of time—the machine figures out how to tell good data from bad
AI makes the most of imperfect and changing data
They do not require provider participation—we use data they already create in their day-to-day workflows, so no need to persuade providers to take additional action
It works—we have scientifically tested attestation along with “source of truth” modeling, and Veda’s approach has the highest measured accuracy of any approach in the industry.

Read more about Veda’s approach to AI and data science with Dr. Bob Lindner’s blog post, Artificial Intelligence, ChatGPT, and the Relationship Between Humans and Machines.

Fierce Healthcare: OutCare Health partners with Veda to deliver LGBTQ+ affirming provider list to payers

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.

Open Enrollment: Tips for Enhancing Provider Directory Accuracy

Prepare for Open Enrollment with Directory Accuracy

Provider directory accuracy leads to a positive experience for health plan members. But often overlooked is the importance of directory accuracy during open enrollment when both existing and prospective members are making choices for the upcoming year. Read on for open enrollment tips.

open enrollment tips person questioning health insurance

According to a recent eHealth survey, “Coverage for preferred doctors is a bigger consideration than affordable monthly premiums.” In fact, “31% of respondents say finding a plan that covers their preferred doctors or hospitals is their number one priority when choosing a plan.”

Inaccurate and incomplete directory information misrepresents health plan coverage, the leading priority for members during open enrollment. It also establishes an erroneous benchmark of user experience for enrollees — a first impression with your members should start on a good footing.

open enrollment tips person at computer deciding on health insurance

With nearly half of Americans considering a change to their health plan during open enrollment, your plan could gain a competitive advantage by clearly demonstrating the breadth of accurate and comprehensive provider information in your directory. Furthermore, accurate directories will only continue to enhance the member experience once they choose a plan and begin contacting their newest practitioners.

Open enrollment is right around the corner, but it’s not too late to make targeted changes that have the greatest impact on the member’s experience. Veda’s automation tool makes thousands of suggested changes to your directory in less than 48 hours.

With nearly half of Americans considering a change to their health plan during open enrollment, your plan could gain a competitive advantage by clearly demonstrating the breadth of accurate and comprehensive provider information in your directory.
Value Penguin

Open Enrollment Tips for Provider Data Improvements

Here are three open best practices and recommendations to quickly and strategically improve directory accuracy before November 1, 2023—and what to plan for in 2024 and beyond.

Make Segments: In lieu of whole directory changes, start small. Segment the directory with open enrollment priorities in mind. Veda recommends segmenting by market or specialty.
Be Specific: Target the high-impact data elements within a segment to get the needed results before open enrollment. Think provider level data such as clearing out deceased or retired providers or location level like updated addresses.
Act Confidently: Grab the highest impact, highest confidence recommendations. Bonus: Implementing mass maintenance of high confidence scores can automate directory fixes eliminating administrative burdens almost immediately.

Armed with a directory diagnostic, you can confidently address critical directory errors resulting in improved directory accuracy.

Ready, Set, Go: Get a Provider Directory Diagnostic Snapshot

Veda will diagnose your directory data, giving you a snapshot of your directory with fixes that can be enacted quickly. Armed with a directory diagnostic, you can confidently address critical directory errors resulting in improved directory accuracy.

We’re ready when you are.

Contact Veda for your Provider Directory Diagnostic Snapshot.

Quantym Diagnostic open enrollment get a data diagnosis

Six Questions to Ask Your Provider Data Vendor

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.

How often is data being refreshed?

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.

How do you perform entity resolution and resolve data conflicts?

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.

What sources are being used?

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).

How many active providers are included in data sets?

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.

How do you measure the performance of your data model?

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.

How is success defined and how is it measured?

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.

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

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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