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CMS Directory Accuracy Audits and Sanctions: Achieving True Directory Accuracy

The Centers for Medicare & Medicaid Services (CMS) regularly audits health plan programs and provider directories. All health plans providing services to Medicare and Medicare Advantage members are nearly guaranteed to be audited by CMS. By definition, the CMS directory accuracy audits aim to improve patient access and experience. Additionally, many standards for provider directories and network adequacy are developed based on CMS regulations.

Veda works with health plans to prepare for CMS audits and then interpret and address their audit results.

Unfortunately, health plans’ directory accuracy claims may not match with CMS’s findings—in the case of lower accuracy discovered, the plans may receive CMS sanctions and fines. Why are the directory accuracy rates differing and what can be done to reconcile the accuracy rates?

Why do accuracy rates determined by CMS and health insurance providers differ?

Many factors determine accuracy rates in provider directories. CMS zeroes in on specific fields (such as name, address, and phone number) for determining accuracy while insurance providers may go further in determining accuracy (such as specialty fields). Here are the reasons why updating directories while maintaining high accuracy levels—is an uphill battle:

In anecdotes shared by those in the industry, 20–30% of providers are unresponsive during attestation requests. Attestation is not a sufficient data-collection tool and does not result in data quality.
Many systems rely on heavily manual workflows, causing delays in data updates. Human error degrades data quality
Provider abrasion and long turnaround times are present when constantly attesting to information
Phone calls, even when used for verification, have a 20% variability rate. Meaning, if your call center has two people call the same provider twice in one day, you’ll get a different answer 20% of the time

Why Does CMS Audit Provider Directories?

A few years ago, a CMS Online Provider Directory Review Report looked at Medicare Advantage directories and found that 52% had at least one inaccuracy. The areas of deficiency included such errors as:

The provider was not at the location listed,
The phone number was incorrect, or
The provider was not accepting new patients when the directory indicated they were.

And, despite provisions in the 2021 No Surprises Act legislation, new research has shown that directories remain inconsistent, one study citing “of the almost 450,000 doctors found in more than one directory, just 19% had consistent address and specialty information.” (Let alone complete accurate information including phone numbers.) The audits continually find inaccuracies as the years go on.

How Do Health Plans Prepare for CMS Audits?

Traditional approaches to audit preparation include phone calls and mock audits.

Phone Calls

Pricey and oftentimes inconsistent, call campaigns amount to hundreds of thousands of phone calls being made every day to check data.

Mock Audits

Mimics the audit experience with sample sets of small amounts of data but are not reflective of the overall directory.

These approaches are not sufficient for achieving successful audit results.

What Is CMS Looking for in Audits of Directories?

Not all information is equally important during an audit. The scoring algorithm assigns different weights for fields so if you’re starting somewhere, Veda recommends starting with the key areas of focus: Name, Address, Phone, Speciality, and Accepting Patients.

Addressing the most important data elements with quality validated data will move a health plan towards audit success.

How Veda’s Solutions Interpret and Address CMS Audit Results

CMS performs audits to advocate for members and better outcomes so interpreting audit results is the perfect place to get started with directory updates. Our research shows that when it comes to what members care about it is pretty simple: Choice, Accuracy, and Accessibility —meaning the ability to schedule, with their preferred provider, easily and quickly. On the first try.

Where to Start For CMS Audit Success

Many health plans are realizing that achieving directory accuracy and audit success is not a one-and-done. An ongoing surveillance approach is needed to confidently prepare and ultimately, achieve success in an audit.

Veda’s approach consistently evaluates the directory to provide ongoing insights. For example, we leverage technology to identify and prioritize providers for updates who haven’t attested recently, to ensure they have a data trail that supports their current status and information in a directory. By prioritizing bad data, this audit strategy is efficient and effective.

Diagnose your provider directory and fix critical data errors ahead of CMS audits with Veda.

AI for Amateurs: Questions Answered by Veda’s AI Experts

Everything You Always Wanted to Know About AI But Were Afraid to Ask

Feeling overwhelmed by AI? You’re not alone. At Veda, we take complicated data and we make it simple. Now we’re here to explain the basics of the complicated topic of artificial intelligence. If you’re feeling like everyone is diving into the deep end with AI knowledge but you’re still in the kiddy pool, this is for you.

Impossible to miss, 2023 is synonymous with the year AI debuted to the masses. AI capabilities have brought up questions in every industry, including healthcare. Your organization will likely find itself navigating the risks and rewards associated with healthcare AI in the coming year.

But, let’s start with a question you’re too afraid to ask at the company meeting: What is AI? Like, really. We’ve found a lot of false information out there and we’re here as a trustworthy source you can pull information from.

Why is Veda a Trusted Source?

As pioneers who have used AI technology since our founding, we’re passionate advocates for AI and want to ensure everyone else feels comfortable with it too.

Want our credentials? Our technology and data science team has 80 years of collective AI experience. Veda co-founder and Chief Science and Technology Officer, Bob Lindner, is the author of five technology patents on AI, entity resolution, and machine learning. Bob also has over 16 years of experience writing and publishing scientific and academic papers in the artificial intelligence field.

Backed by extensive experience and science, we’re the AI experts.

What is AI?

OFFICIAL ANSWER: Artificial intelligence Is a field of study that focuses on how machines can solve complex problems that usually involve human intelligence.

AI is not one specific tool. It is a field of study. With AI’s computing power, computers can make decisions and predictions, and take actions. An algorithm recommending which movie you should watch next is an AI action.

VEDA’S TAKE: So why does this matter? Why is AI important? By freeing up human resources, AI can reduce manual and often error-prone tasks. Freeing up people so they have the time to do the things they do best, that’s the power of AI.

What is machine learning?

OFFICIAL ANSWER: Machine learning is a sub-field of AI and focuses on algorithms that train models to make predictions on new data without being explicitly programmed. Meaning, the machine learns the way humans do, with experience.

Note: In recent years, some organizations have begun using the terms artificial intelligence and machine learning interchangeably.

Instead of learning step by step, computers using machine learning can learn through trial and error and lots of practice. What does machine learning practice on? Lots and lots of data. The data can be things like images, video, audio, and text. When fed loads of data, machine learning will recognize patterns and make predictions based on these patterns.

AI is not one specific tool. It is a field of study. With AI’s computing power, computers can make decisions and predictions, and take actions.

VEDA’S TAKE: Veda uses machine learning, and therefore, AI for the healthcare industry, every day. For what exactly? To power our provider information. Veda uses machine learning to:

Determine correct addresses and phone numbers
Transform provider rosters from one format to another
Simulate an experience a member may have when booking an appointment

With a patented training data approach, our machine learning can make predictions on a wide variety of new data (that it has never seen before in the training set).

Feeling good about AI and machine learning? Further your AI understanding with these blogs:

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.

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.

Artificial Intelligence, ChatGPT, and the Relationship Between Humans and Machines

By: Dr. Bob Lindner, Chief Science & Technology Officer, Co-Founder

If the explosive launch of ChatGPT has taught us anything, it’s that there is a growing appetite for engaging with AI. According to a recent UBS study, the chatbot from OpenAI reached 100 million monthly active users in January— only two months after its launch. By comparison, it took TikTok about nine months to reach that milestone and Instagram two-and-a-half years.

While ChatGPT and the generative AI that powers it represent the latest advancements in AI and machine learning, the fact is that organizations and individuals have been trying to harness the power of AI for years. Some see it as the wave of the future. Others are scared of what it portends for the complicated relationships between humans and machines.

Many people are so afraid of being displaced by the automation that artificial intelligence brings that they overlook the benefits of this amazing technology. But the fear of “robots replacing humans” isn’t the only thing that gives people pause. There’s also concern that machines will make unacceptable errors. Of course, when people make the occasional mistake, we’re used to giving them the benefit of the doubt, but we struggle to do the same for machines because we don’t know how to contextualize their errors.

Why do we react so emotionally to AI? How can we shift our perspectives? And how can we actually score recommendations in AI systems? The hope is that with greater understanding, we can apply AI to more business settings and drive greater success.

Digging deeper into our fears and hesitations

Behaviorally, people tend to fear things we don’t understand or that seem out of our control. When it comes to risk, specifically, we struggle to comprehend how to assess it in an objective—rather than emotional—way.

For example, think about self-driving cars. The thought of a car without a driver makes many of us uneasy. Even though more than 75% of us will be in at least one major car accident during our driving lifetime, we’re afraid to put autonomous cars with this type of driving record on the road. While the probability of an accident is likely not higher than for a human driving a car, the combination of not knowing the exact percentage of risk and not being in control makes it harder to accept. We’re just not used to making our decisions based on probability; we are used to listening to our gut.

In order to process the data with a probabilistic AI system, we have to score it and set a threshold for “good” data; anything with a score below our threshold is discarded and anything higher is deemed an acceptable level of risk and included in the data set.

In my experience, the best way to get comfortable with objective assessment of risk is practice. Over time, it becomes more natural to look at the numbers as opposed to looking at our emotional response. Of course, understanding exactly how AI works helps too.

Understanding how to assess risk associated with AI

AI acts on two types of systems: deterministic and probabilistic. With a deterministic system, an outcome can be determined with relative certainty. This includes apps like Amazon, Doordash, and Venmo, which generate predictable types of data within a confined system. These are usually not considered “mission-critical,” and as a result, we’re willing to tolerate some level of inaccuracy in their algorithms. For example, when Netflix recommends a movie that doesn’t actually interest us, we don’t cancel our subscription to the service. We just look at the next recommendation in the queue or scan the top 10 titles of the week. We’re forgiving.

Probabilistic systems have built-in uncertainty. The exact output is not known. Think about the difficulty of forecasting the weather. It’s hard for us to understand the uncertainty of probabilistic systems and the stakes get even higher when we’re dealing with “mission critical” data, like we are in healthcare technology. In order to process the data with a probabilistic AI system, we have to score it and set a threshold for “good” data; anything with a score below our threshold is discarded and anything higher is deemed an acceptable level of risk and included in the data set.

The first step is to understand how these systems work, and the second is to set thresholds to score data that matches your risk tolerance.

Take a risk

With machine learning models, we are training a system to learn and adapt in order to improve—so it’s necessary to make assessments on an ongoing basis, rather than measuring an automation system’s performance once and only once. Because of that, it’s essential to have patience, as data can and will change, depending on many factors.

While risk makes people feel uncomfortable regardless of the setting, it’s time to address those fears and reluctance to move forward. Once we have tangible examples and parallels we often relate and tolerate it better.

As for ChatGPT and its generative AI brethren, the key will be for each person who engages with these tools to determine what level of risk they are willing to take. For most of us, a simple chat about something mundane or unimportant is likely acceptable. For some, the exchange of critical data or asking it to perform an important function will be a bridge too far. For now.

Dr. Bob Lindner is the Chief Science & Technology Officer and Co-Founder of Veda. More about Veda’s science and technology: Automation, Machine Learning, and the Universe: Q&A with Bob Lindner.

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

Medica’s Journey to Building a Data-Driven Directory: Q&A with Veda and Medica

Medica’s Director of Provider Network Operations & Initiatives, Patty Franco, sat down with Veda to talk about all things provider data and directories. As a current Veda client, Medica is making targeted changes to their directory resulting in improved accuracy and positive member experience.

Tell us about Medica, what areas does your health plan cover?

Our nonprofit health plan serves about 1.5 million members in 12 states: Minnesota, Arizona, Illinois, Iowa, Kansas, Missouri, Nebraska, North Dakota, Oklahoma, South Dakota, Wisconsin, and Wyoming. I’ve been with Medica for over 18 years.

I’ve been with Medica for over 18 years and we’ve had some exciting changes recently, such as our merger with Dean Health Plan out of Madison, Wis. Our plans combined have a better opportunity to support the healthcare needs of members and to further enhance our provider relationships.

From a provider data collection, ingestion, and validation standpoint, what was Medica’s process before partnering with Veda?

Medica receives data from providers in a number of ways: our portal, delegated rosters, forms, new contracts, and more. Depending on how that data comes in, it will be processed by our teams to get provider demographics and reimbursement set up in our systems.

As to what we were doing to ensure accuracy, that too had many options — none of them ideal. We used manual audits throughout the processing of the data and also performed a monthly audit of all our directory (public-facing) data. This audit is what is used to determine where we have inaccuracies. The audit includes randomly selected records in which we make secret shopper calls and ask the same questions CMS does in their audit.

Can you explain your data lifecycle?

Our process with Veda is to use a monthly file that we give to Veda and Veda runs through their Smart Automation tools and provides a response back in two days. We use the responses and run them through a series of queries that sorts the data we want to address and correct. Depending on the edit, it may be automated into our system and corrected. If needed, it could be something we want to do further investigation on so we will send that to another team to do that research, such as validation with a practitioner.

Veda’s tools get us started in the right direction and, more often than not, we can put their suggested correction directly into our system.

All health plans have different internal structures. What teams in your organization interact with provider and practitioner data?

So many departments within Medica interact with our provider data. Network Management, Provider Finance, Provider Operations, Provider Call Center, Reporting & Analytics, and Health Services all use it.

What have you found to be an industry best practice in terms of using Veda to maintain high data accuracy?

Veda supplements the various sources we receive data from and validates the accuracy of it. Plus, Veda has helped us use claims data to obtain information for providers that do not submit any claims or have limited claims.

Of course, we still use some internal tools, but Veda has helped us pinpoint areas we can focus our attention on with relative ease. For example, we’ve been able to better work with our contract managers to address gaps in provider data. We’ve also learned to reframe our contract language for delegated agreements to ensure that providers maintain their data with us.

What challenges and opportunities did you encounter, internally and externally, on your path to success?

Internally, when you add a new vendor or service, it is all about the training and handoffs to other departments and systems. How many systems you have that hold this data can be challenging along with ever-changing technology. Processes that have to remain manual are a challenge, but necessary. And of course, executive buy-in and understanding took time, as did budget approval.

Externally, we are still working on drilling down using data-cleansing processes with our delegates. They account for about 50% of our data, so they are important partners in getting the data accurate on the way in. As we know, the root of bad data starts at ingestion. With targeted recommendations from Veda, we identified where the worst data issues were coming from. This allowed us to rethink how we interact with the data on the front end. Now, if we see a roster with 30 locations for one provider, we call and inquire about it.

It is imperative to both our organizations to have accurate data so that our members can find providers in our directories and that the phone number is correct so they get an appointment quickly to address their health care needs.

How do you approach making changes internally?

With the suggestions we received from Veda, we determined which changes we could automate and which ones may need further information from the provider. This drove multiple processes for us to build or modify to accommodate. Then we started to integrate the changes into our systems and review the results of the next month’s audit. This is one barometer of how the changes are affecting our accuracy. We continue to look for opportunities in the data and Veda helps us to focus on areas that may get the biggest return. We work together to see what is most feasible, as this is a partnership that includes our providers who want to get on board to help us on the journey. This process improves the experience for the providers’ patients and Medica’s members.

How do you report your department’s work and results within the organization?

We have a dashboard that goes to senior leadership that reports our accuracy rate and compliance. Of course, it’s always exciting to see accuracy increasing and bad data consistently being corrected. Our reporting helps highlight both gaps and opportunities.

How have you seen data accuracy positively impact member experience?

Accuracy, adequacy, and efficiency are what our members expect and we want to deliver. They depend on us for their healthcare needs and it starts with knowing they can view our directories to find a provider near them to take care of their family’s health. As we all know, health care is complicated, so it is our job to make things easier in whatever ways we can. My role is to focus on ensuring that the data we receive is accurately displayed to our members and they can trust it.

As Director of Provider Operations and Initiatives, Patty is responsible for Medica’s provider network data in online and print directories. Based in Minnesota and with Medica for over 18 years, Patty oversees network compliance and adequacy. Connect with Patty on LinkedIn.

Veda Leads the Industry in Patented Provider Data Technology

Only Veda offers proprietary technology with five approved patents and counting

Veda’s provider data technology isn’t only powerful, it’s patent-protected intellectual property. Innovated precisely for healthcare organizations and unique provider data challenges, our five unique patents are essential to the delivery of fast and accurate data to Veda’s customers.

Patented Technology Outshines Competition

When you work with Veda, you have the fastest, most optimally accurate source of provider data on the market. And because it’s patented, no competitors have a solution that can compare. (Before you select a healthcare data vendor, ask yourself, why don’t they have a science department backed by patented IP?)

Provider directory inaccuracies are vast and burdensome. A large reason why provider data is so complex? The underlying information is constantly changing. For example, a provider may change the location where they practice; they may add a new phone number, or change their specialty. Veda’s technology specifically accounts for these changes. Many data vendors boast solutions that are appropriate for static information like names or birth dates of providers. Their solutions don’t address changing information that delivers the most up-to-date and accurate data that members need to book an appointment.

Provider directory inaccuracies are vast and burdensome. A large reason why provider data is so complex? The underlying information is constantly changing.

Veda’s Patents: Speed and Accuracy

We’ve categorized Veda’s patented approach into two areas that add up to provider data success: Speed and Accuracy.

Why is Veda’s Patented Technology the Fastest Way to Deliver Provider Data?

Veda’s speed patents are about all the right tasks, done in the right order, completed quickly. To make data usable, it must be cleaned and processed and Veda is doing that around the clock. We accomplish these tasks efficiently with our speed patents. From multitasking multiple events in the correct order and processing quickly, speed is necessary for backlogs of provider rosters and keeping data fresh. By managing efficiency and optimizing data, Veda guarantees processing times of 24 hours or less ensuring members have the data they need, fast.

By managing efficiency and optimizing data, Veda guarantees processing times of 24 hours or less ensuring members have the data they need, fast.

What Makes Veda’s Patented Technology the Most Accurate?

Veda’s accuracy is second to none. In fact, we don’t measure our accuracy with industry standards like attestation—we measure accuracy the same way health plan members do: can they make an appointment with a provider on the first try with the information available to them? This question can only be answered if the underlying provider data is accurate. Not only can Veda’s system assess current accuracy, but it can also predict accurate data while sorting through messy data. We also keep our training data up-to-date ensuring that our algorithms remain current and produce the best results. A data processing system is only as good as the data it’s trained on; if the training data becomes stale, inaccurate outputs will likely be the result. You will never need to worry about that with Veda’s patented solutions.

More Proprietary Innovations to Come

Veda’s patented approach gives every health plan what it needs: provider data quality. Armed with provider data quality, organizations can dramatically reduce operating costs and improve member satisfaction. Eliminating manual processes and delivering correct information, Veda is bringing the highest possible speed and accuracy to the provider data. And our groundbreaking work hasn’t stopped—Veda has 11 patents pending with the USPTO and nearly a dozen patents under review internationally. More innovation and proprietary solutions are coming your way soon.

If you’re already working with a data vendor, make sure you’re getting the most for your investment by answering these six questions.

Ready for patent-powered technology? Contact Veda.

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.

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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