Blog Archives | Page 2 of 3

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.

Why Attestation Isn’t Sufficient for Quality Provider Data

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

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

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

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

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

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

A Better Way to Source Quality Provider Data

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

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

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

Access To Comprehensive and Accurate Provider Data

We offer three unique products to address provider data challenges.

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

The Impact of Bad Healthcare Data

The information included in provider directories changes often and the scope of required information keeps expanding. Practices move, physicians change practices, and contracts between practices and health plans expire. According to a report from CAQH and AMA, between 20% and 30% of directory information changes annually.

Yet, no single party is the exclusive keeper of this information. Some of the information is governed and controlled by the practice, such as contact information and the roster of clinicians who practice there. Other data, such as whether a clinician is accepting new patients under a specific plan, can be owned by the practice, the health plan, or in some instances, shared by both parties.

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

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

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

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

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

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

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

The Veda Approach to Provider Data Quality

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

Guaranteed Outcomes

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

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

The Impact of Provider Data on Star Ratings

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

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

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

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

What happened with the 2023 Star Ratings?

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

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

Business impacts for loss in Stars

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

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

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

An uphill Star Ratings battle on tap for 2024

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

How can Veda help health plans boost their Star Ratings?

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

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

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

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

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

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

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

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

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

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

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

What else can Veda’s provider data do?

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

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

Optimal and Proven Provider Data from Veda

What makes Veda’s data so great?

Healthcare provider data can be riddled with inaccuracies—just ask anyone who uses network directories to find an in-network specialist or view clinics in a 10-mile radius. The Centers for Medicare & Medicaid Services (CMS) Medicare Advantage (MA) online provider directory reviews between September 2016 and August 2017 found that 52.2% of the provider directory locations listed had at least one inaccuracy.

Health tech companies have attempted to solve provider data inaccuracy problems with a number of products, platforms, and integrations. No solutions have been able to ultimately offer a better experience for members where it matters: the ability to easily book an appointment armed with accurate information.

Many solutions in the market focus on gathering all data sources available to identify providers but don’t have the ability to clean up those databases so they have only current and accurate information. A patient might find a doctor in a directory but if the location and coverage information was wrong, they still can’t make an appointment.

Enter Veda’s latest offering: Vectyr Data Curation. Vectyr offers the most up-to-date, comprehensive, and accurate source of provider data on the market. Vectyr’s database uses more than 100,000 unique sources to create an optimal collection of provider information. The data is continuously monitored, validated daily, and backed by our accuracy guarantees.

Prove It

How does Veda back up claims of accuracy and completeness? For one, our team of data scientists behind the development of Vectyr has the clout and expertise needed for intensive data modeling. From creating ground-breaking machine learning code to researching at the largest particle physics laboratory in the world, the best in science and technology are found at Veda. Here is how Veda employs a different approach than other data companies on the market:

Automation: Veda fully automates static and temporal data, boosting accuracy and reducing provider barriers. This validation process is automated in real-time, a fundamental advantage for healthcare companies seeking effective data structure.
Performance Measurement: Veda’s team of scientists carefully monitors the data’s success rate, creating statistical models, sample sizes, and methodologies to consistently guarantee accuracy. This process ensures specialty and data demands are evaluated and performing at the highest level.
Data Reconciliation: As temporal data evolves, Veda’s entity resolution process follows. Our technology accounts for data drifts over time, so our entity resolution is calibrated to recognize correct data from the abundant sources available today. New data is always cleansed and standardized, then consolidated within a database to eliminate duplicates.
Test Outcomes: Even with 95%+ accuracy, Veda doesn’t rely on automation to do all the work. The Veda team inspects all aspects of delivered data, including quality, delivery methods, bugs, and errors with a continuous monitoring process. By continually auditing and testing our data fields to confirm they are the competitive, current, and optimal quality we know reasonable coverage is reached.

Coverage, Precision, and Recall are numbers reported and recorded by the science team.

Coverage: What is the fraction of the data that isn’t blank?

Precision: When we do have an answer, how often is it right?

Recall: If we should have an answer, how often do we actually have it?

“Anyone can make an API. They are flashy, they can help operations, they can automate processes. But if your API is pulling in duplicative, inaccurate, and just plain bad information it’s useless,” says Dr. Robert Lindner, Chief Science & Technology Officer at Veda. “With our science backing, Veda’s data is guaranteed accurate and with flexible query so data delivery is where, when, and how users need it.”

Veda’s data is currently being used by top health plans for the correction and cleansing of their directories. Now, customers, new prospects, and new channel partners have direct access to Veda’s best-in-class provider information based on their nuanced business use case.

Vectyr has profiles on more than 3.5 million providers who have an NPI 1 number—including MDs, DOs, RNs, social workers, DDS, and pharmacists.

What can health plans do with Veda’s data?
Staying atop changing information ensures provider directories are always accurate. This is no small feat as 20-30% of all provider directory information changes annually. With Vectyr, health plans can offer a better experience for members and providers by:

Expanding network offerings: Members need both provider options and location access to get the care they need. Using Veda’s data can help health plans identify providers they aren’t currently contracted with and fill geographic or provider gaps in their network.
Sourcing correct providers for referrals: Providing accurate and on-the-spot information for in-network referrals relieves administrative burdens and eliminates frustrating hours spent searching for answers.
Quick credentialing: Credential providers faster and deliver faster onboarding and credentialing support with data that’s updated every 24 hours and guaranteed accurate.

What’s possible with optimal provider data?
There are immediate benefits to using Veda’s data. Health plan members will no longer wonder if their doctor of choice accepts their insurance or where the closest allergist to their home is. Hours of phone calls and administrative burdens are eliminated for both the member and the health plan. And, most importantly, health plans can trust Veda’s rigorous scientific validation methodology to ensure they have the optimal data for every provider in the country, on-demand, every day.

When health plans have access to optimal data, it means members have access to optimal data and that results in a markedly better customer experience.

More about Vectyr Data Curation

Star Systems Meet Star Ratings: Using Science and Imagination to Solve Healthcare’s Most Complex Data Problems

What the heck does astrophysics have to do with provider data quality?

With an entire science department dedicated to solving complex data issues, science is at the very core of Veda’s existence. After all, our Chief Science & Technology Officer and Co-founder, Dr. Bob Lindner, began his career in astrophysics.

After taking a leap from the academic world and into political data analysis, Bob and co-founder Meghan Gaffney realized the potential of provider data automation. [READ Q&A WITH DR. BOB LINDNER]

The commitment to the scientific method and investment in science is what sets Veda apart from other data and healthcare tech companies—and what led to a robust science department with an impressive five IP and automation patents.

But you might be thinking: what exactly does this background in galaxy-staring, particle-measuring, and the expansive universe have to do with ensuring health plans’ provider directories are accurate?

The answer lies in wholeheartedly embracing the scientific method and Veda’s mission: We blend science and imagination to arrive at solutions for our customers. In fact, Bob argues one would not be able to tackle provider data problems accurately and reliably without a science department.

In the healthcare industry, data changes rapidly, some sources of data claim to be sources of truth but may in fact not be accurate, and data can be a heavily manual process. The only way to uncover the truth is with a careful and accurate measurement process.

Science meets imagination with Veda’s Science Team

Here is an expert from Dr. Lindner on problem-solving at Veda:

There are two kinds of main prediction problems. One where the answer to any given problem is self-evident. You can look at it and immediately know what the answer is. You give this problem a fast feedback loop and design your system to get the right answer based on immediate feedback from engineers. Outside of healthcare, an example is image classification. Is there a smiling person in this picture or not? You can look at an image and immediately tell.

A different kind of problem that we’re faced with every day at Veda is if the answer you’re trying to predict is not self-evident by a trained user in the field. For example, does this provider work at this address? It may look like a reasonable address and provider name but you don’t know if it’s accurate just by looking at it.

The only way to know if the system is working is to be very disciplined with the art of measurement and calibration. You must have a good set of test data that you trust that was collected in a way that was very tightly controlled. And you have to trust you are training your models on the data in a way that’s not overfitting because when your system gets used in production you don’t know—aside from that measurement in comparison to your training data set—if it’s working or not. You have to trust in science fully because if you do that part wrong, by using a biased training set or too narrow of a sample, there are errors that are invisible until you actually try to use the data. It can be a devastating effect. If you have a 10-digit number that says it’s the phone number of a provider, and you can’t call every phone number, how do you know it’s correct? You must have faith in the process. And the process to have faith in is the scientific method.
Dr. Robert Lindner

Provider data is complex and vast just like data in the field of astrophysics. However, provider data is nuanced and complicated in ways that even monitoring billions of stars is not.

The challenges with provider data are more complex than say, finding the largest thing in the universe, because the information included in directories changes often and the scope of required information keeps expanding. Practices move locations, physicians change practices, and contracts between practices and health plans expire. Multiple industry reports state between 20% and 30% of directory information changes annually.

Having different authoritative sources depending on the data contributes to the difficulty for health plans and practices in keeping information accurate.

So yes, provider data is more complicated to monitor than the stars but the Veda science department, using the scientific method day in and day out, can solve complex provider data problems faster and more accurately than anyone else. We start by understanding problems deeply before pairing them with an appropriate model and AI technology.

Before you select a healthcare data vendor, ask yourself, why don’t they have a science department backed by patented IP?

Get your provider data assessed by Veda.

People, Patterns, and Physics: Q&A with Veda Data Science Manager, Rishi Patel

Veda’s science department is dedicated to solving complex data issues with creativity and imagination. Learn more about Veda Data Science Manager, Rishi Patel, and his scientific approach (and improv background) in the Q&A below.

You have a history with Veda Co-founder, Dr. Bob Lindner, how did you meet?

I did my graduate degree at Rutgers with Bob. Within the first week of school, Bob and I were in the library looking for the same book. We started discussing and working on assignments together. We’ve kept in touch and encouraged each other in different pursuits in the academic and scientific fields. Bob encouraged me to step out of the lab and get into the world to attack more pertinent problems with the same skill set we used in academia.

For example, during the early start of the pandemic, there was a giant data set of all the COVID-19 literature coming out, including things from the past. Like an article from the 1800s about early pulmonary diseases. It was in PDF and you couldn’t mine it. Usually, all my data is quantitative but this data was text. I’m used to looking at pictures and in this, I was looking at text and breaking it into keywords like particles. If you have enough of certain keywords, you can say “maybe this is really important,” but let me make that decision later and filter out the important keywords.

I was able to take half a year’s worth of COVID-19 literature and combine it into a couple of paragraphs. It proved Bob’s point–that if I tried to take what I learned in the lab and look at a dataset that I have no expertise in, something good would still happen in the end. Basically, we could generate knowledge through experimentation the same way we were doing in our research. I still get messages and questions about this research today.

Talk about your role in Veda’s science department, how are different members of the team approaching science?

When Bob and Carlos Vera Ciro (Veda architect) were focused on the biggest and largest aspects of the universe, I worked on creating small versions of the big bang in a controlled environment. Putting it another way, while Bob and Carlos may see a huge picture, I see little wisps of energy in a particle detector.

What’s powerful about our comparisons, the methods that they use, and the methods that I use–for what should appear to be very disparate and strangely different–in analyzing data, it’s roughly the same. We approach things in the same way and we have the same divisions: I’m experimentation, Bob is observation, and Carlos is theory. My work requires me to know a little bit about theory and the constraints that come from what we’re trying to prove. To solve provider data problems, you have to have a good hypothesis and that’s likely going to come from seeing a solution in an intuitive way from the perspective of our clients.

Why do you think a health tech start-up even needs an entire science department?

I love that Veda has a whole team dedicated to the scientific process. We want to know “what is true and how do we test it?” That’s a strong competitive edge compared to other companies.

“Having an entire science department, all verifying something is true in collaboration with each other, is more than just the icing on the cake, it is the full entree.”

The incredibly challenging data we work with gets me jazzed. We have to ask, “how do we present this in the simplest fashion for the end user?” That is a powerful engine in data science, not just finding the data and guiding strategy, but creating a picture or a product where there are a-ha moments all day long. Being able to build products that provide those moments makes me super excited.

A big part of science culture is the ability to break things down so that science is sharable. This is also what keeps me motivated.

Tackling a big challenge is like climbing a mountain, there are highs and lows and points at which you want to give up. It is satisfying to finally make it to the top and learn something about what you are capable of. But then taking a picture from the top and sharing it with the world, that’s the moment when you feel victorious.

How did you become interested in science as a career path?

In high school, I hated math and science. I thought it was super boring and I even remember doing an abysmally low amount of math homework compared to any other subject. I thought that I would never major in a hard science or a quantitative field. I thought I’d be a writer and I went to NYU planning to major in philosophy and writing.

But the real hook for me was how different math was taught in college compared to high school. The math started with the bigger question and then you start to build out answers in a sequential way. It felt like reading a mystery novel– you guess what the right answer might be, test it, and maybe you’re wrong, maybe you’re right. That really pivoted my enthusiasm for math and science. Physics felt real and tangible. You could look at the sun and imagine what’s going on there. Really, you could look at anything in the world, big or small, and try to solve it like a puzzle.

“In science, seeing sparks a kind of wonder, and plants seeds of questions centered around the “how” and the “why.” When the seeds become a forest, the crux of being a scientist is being able to say “I have seen this before, I know how to find my way.”

You previously did research at CERN, the largest particle physics laboratory in the world. How did you end up in the healthcare field?

When you start off as a data scientist, there are so many sectors and you have to find what you’re interested in. There isn’t a perfect fit for going from particle physics data to anything else. I realized healthcare data is very complex because it’s people and patterns.

“People and patterns are way more complex than particles and patterns because all particles behave the same while people of course do not. When you start to look at different patterns in similar contexts, it really challenges your ability to recognize patterns.”

Describe your role at Veda. If someone asks “what do you do at work,” what do you tell them?

I am a data science manager for the data science team. This means, I ask the key scientific questions to try and poke at the data scientists’ arguments and check to make sure that what they’re developing is aligned with the truth. Everything we’re working on must align with both our intuition and the patterns found in the data.

It’s my job to take the problem and break it down into actionable items and testable output. We go from big questions to small actionable questions and test the hypothesis to see if we can continually make improvements in the model.

What data process do you use at Veda?

I am focused on three letters: ETL (Extract, Transform, and Load). This data process is what I’m living every day.

Extract. Can we collect the data to inform our hypothesis? What truth can we collect? What is available out there from public records? Could we make phone calls and verify what our models are saying? Is it still up to date? Is there an inefficiency in our model that a human could catch when they ask questions to another human?

Transform. This is where the core skills of data science come in. We get a bunch of data but we want to make it useable. We want an answer to give somebody for what they should do based on the data and the patterns we believe are true. For example, for a care location we do a bunch of checks to be sure an address is really valid and give it an accuracy score. We want to make sure our clients know what to do with that score. The transformation always connects to the customer vision and our pattern must be intuitive enough to interpret.

Load. Here’s the customer vision. We go back to the model and see it from a customer’s lens. Is the row really valid? What can we do to justify it? For example, does this provider have a state board address and do they have prescribing power? We see doubts from the client’s perspective and make an argument to see how to best prove the decision we are recommending.

When you get stuck on a problem, what is a resource you use to get past it?

I’m part of a start-up collab Slack channel called On Deck where we meet regularly to discuss general data science problems. We get to see what challenges we’re all facing. For instance, how do we communicate really technical things to those who need brief and actionable answers? Or how much time should we invest in this issue? We work through those questions and agree on steps. It’s magical to me that data scientists can come from different backgrounds but when you start to compare notes on a given problem, there are pretty strong steps for how to solve it. It’s reassuring that we walk down similar paths to find a solution, so there is some standardization in the process of being a data science manager.

Say a student comes to you and says they want to be a data scientist. What advice do you have for them?

Can you let things go? The ability to ruthlessly prioritize and reach a more balanced focus is a key part of data science management. If there’s one thing that really irritates you, and you want to get it right, and there are so many other priorities that are more important, you have to let that irritating thing go and solve it later. You can dream up a new idea every day but that might not answer the question you’re trying to solve.

You work at Veda so you’re statistically likely to have a pet. Tell me about yours.

I have a cat named Luna who has lived in three different countries. She was an Italian farm cat, who lived with an Australian family, then we adopted her in Switzerland. We love her broken tail–there’s always beauty in imperfection!

You did improv and acting while you lived in Switzerland. I can’t imagine there are many data scientists sharing a stage with you. How does this relate to science?

I was always interested in improv and acting. As a scientist you’re always taught to look and judge but in improv, you have to invert that process. You have to accept what’s given to you and do the “yes and.” It’s exciting to see where an improv scene is going to go and how you’re going to respond. That degree of chaos is exciting.

I see science as creativity with constraint. That’s what makes it a cousin of art. You have to think out of the box to solve problems but the constraints are heavy. Improv removes the constraints and it’s fun to build a new world without constraints.

Connect with Rishi on LinkedIn.

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 3.5 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.

Veda solutions

For Blog

Blog