Healthcare Provider Data Management: How AI-native MDM Addresses Key Challenges

Tamr Webinar: Tackling Health Care Provider Data Challenges with AI-Native Master Data Management

Eleni:
Alright, welcome to today’s webinar on tackling health care provider data challenges with AI-native Master Data Management. I’m Eleni, a Solutions Engineer at Tamr. You might recognize me from a previous webinar, but if not, I’m joined today by my wonderful colleague, Catie. I’ll pass it over to her.

Catie:
Hey, everyone! It’s great to be here. I’m a Product Manager at Tamr. Before moving into this role, I worked with customers in post-sales as a Post-Sales Engineer. I’ve helped numerous customers, including Fortune 500 companies, get into production with Tamr solutions.

I currently own our health care data product and our new real-time curation solutions. I’ve worked extensively with health care and life sciences organizations, helping tackle the data challenges they often face—including those tricky edge cases.

As you know, health care organizations encounter complex data challenges, and we’re excited today to shed some light on how AI-native MDM can truly transform the way provider data is managed.

So, if there was a right person to bring from the team, it’s Catie!

Eleni:
Alright. To set the stage for everyone here, over the next 30 minutes, we’ll discuss the significant issues health care providers face with fragmented, inconsistent data, and how these challenges impact everything from patient care to operational efficiency.

We’ll also look at real-world examples of health care organizations using Tamr to unify and manage their data effectively, transforming their ability to make timely, data-driven decisions.

For logistics, you’ll see a Q&A box on the right-hand side of your screen. Feel free to submit questions at any time—we’ll address them as we go. These are private, so don’t hesitate to ask what’s on your mind.

Without further ado, let’s get into it. Catie, let’s start by framing the problem. How do these data challenges typically manifest in health care organizations? Any top two you can think of?

Catie:
Definitely. The two big challenges I’ve seen across the industry are:

1. Fragmented Data Silos:
   Health care data is often spread across multiple systems—electronic health records (EHRs), billing systems, internal databases, or external sources like Definitive Healthcare or IQVIA. For example, you might have different Snowflake tables storing data or data coming in from third-party vendors. Tamr helps bring all these datasets into a single platform and enables organizations to create unified 360-degree views of their data across the enterprise.
2. Poor Data Quality:
   Incomplete, inconsistent, and outdated data is a common issue. It leads to errors in provider staffing, patient care, and financial inefficiencies. Many customers rely on enrichment through third-party sources to validate and supplement their data. Tamr simplifies this by bringing enrichment sources—like NPEZ and CMS—into the platform and helping manage them seamlessly.

Eleni:
So, fragmented silos and poor data quality are huge issues. If you had to add a third challenge, what would it be?

Catie:
A third challenge is duplicates. With multiple datasets dispersed across systems—internal or third-party—you often see the same health care provider represented multiple times and in different ways. These duplicates create operational inefficiencies and inaccurate reporting.

Tamr resolves this by using its AI-native MDM to deduplicate and create high-quality “golden records.” For example, we’ve helped customers deduplicate CRM systems like Veeva. These golden records make it easy to search, manage, and analyze data through 360-degree views—something I’ll show you in today’s session.

The final challenge is lack of interoperability. Often, data systems lack standardized formats, which prevents seamless data exchange across platforms. This is where Tamr shines by creating a clear, standardized “golden record” that can be defined and managed by business users.

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