Powering Real-Time Clinician Data Management with AI-Native MDM
How a Leading Healthcare Workforce Organization Modernized Clinician Data Management with Tamr’s AI-Native MDM Solution
Challenges
- Fragmented, duplicated clinician data disrupted the user experience
- Lack of real-time data access led to missed opportunities to match clinicians with open assignments
- Existing rules-based matching processes failed to meet data quality thresholds and generated 1M+ unresolved data conflicts
- Decentralized data enrichment processes led to redundant spending
Outcomes
- Reduced record mastering time from 1-2 hours to less than 30 seconds
- Enabled real-time mastering and downstream publishing of records within seconds of creation
- Processed approximately 4.5 million golden records within only a few hours
- Reduced publishing times to analytics platforms from 5-10 hours to minutes
The organization’s master data management (MDM) transformation was driven by a need to improve operational speed, data quality, and user experience across the business.
With data scattered across multiple source systems, the team struggled with duplicate records and login issues in the clinician mobile application, an all-in-one platform that helps travel nurses and allied healthcare professionals streamline job searching, credentialing, assignment tracking, and communication.
Speed-to-placement is everything. If we can match and onboard faster, we win—clinician satisfaction goes up, and the business sees results. With Tamr, we went from several days to just a couple of minutes for massive record loads.”
The previous system lacked real-time capabilities and couldn’t keep up with business demands. Mastering over a million clinician records could take up to two weeks—far too slow for a business that relies on rapid clinician placement to drive revenue and user satisfaction. At the same time, clinician data was fragmented across multiple systems, making entity resolution—determining whether two records referred to the same person—difficult and error-prone. This confusion led to multiple logins in the clinician mobile application, hurting adoption and reducing engagement.
The organization needed a faster, smarter, and more scalable solution. By switching to Tamr, the team replaced their slow, heavily manual mastering process with a proven AI-native MDM platform.
Tasks that once took weeks now take minutes. Record mastering time dropped from 1-2 hours to just 20-30 seconds. At enterprise scale, the transformation delivered substantial operational improvements. The organization can now process approximately 4.5 million golden records within just a few hours, compared to processing windows that previously took 7-10 days.
Tamr has unified millions of clinician records, providing a single, trusted, real-time view of each clinician across all internal platforms. And instead of fixing data quality issues after the fact, Tamr continuously identifies duplicate and governance issues and pushes improvements back into source systems through ongoing remediation workflows.
Governance was also reimagined. With the previous solution, publishing mastered data to Snowflake—a key step in making data available for reporting and analysis—could take 5-10 hours. With Tamr, it takes only 3 minutes. As a result, the organization can refresh its analytics dashboards daily, providing business users with automated insights into data quality issues and cluster inconsistencies, without requiring manual checks or interventions.
It gives us the ability to go back to the business with data in hand and say, ‘You don’t need to come to us—here’s the report, here’s the link, go clean it up.’ We’re not just nickel-and-diming teams—we’re giving them actionable insights they can act on every day.”
Lastly, Tamr has transformed how the organization manages external data. In the past, individual business units purchased third-party datasets—such as National Provider Identifiers (NPIs) and other provider data—on their own, leading to redundant spending and fragmented usage. With Tamr now serving as the central hub for clinician data, the organization has consolidated external data procurement, streamlining access and eliminating unnecessary costs.
Tamr’s enrichment capabilities have also enabled teams to tap into Tamr’s extensive firmographic datasets, improving match accuracy and ensuring every healthcare organization record represents a real, verified entity. The project marked a shift from controlling access to data to empowering business units with accurate, trusted information to support their operations.
With Tamr, we became a data provider to our own organization. Instead of business units buying data from vendors, they now come to us. We give them the best version of the truth.”
With a trusted, real-time view of clinician data, the healthcare staffing provider is poised to continue driving greater operational efficiency—unlocking strategic insights and further enhancing the clinician and business user experiences. The organization is also exploring relationship-based knowledge graphs that could help identify clinicians with prior experience working within the same healthcare environments or care teams.
To learn more about how Tamr helps healthcare organizations master their data at scale, please read our ebook, “Healthcare Provider Data Management: Tamr’s New Approach with AI-Native MDM.”
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