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Points of Light 2025 Case Study 20
Utilizing AI to Automate the Management of Provider Directories, Improving Provider Satisfaction & Patient Access to Care
Author
Boyd Stewart
Author
Aurene Wilford
Payer organizations are pressured to maintain accurate provider directories for members but struggle to do so with resource-intensive manual processes as well as high volumes of unstructured, unstandardized provider data. Inaccuracies can lead to patient frustration, reduced access to care, and potential CMS penalties and fines. In this collaboration, HiLabs developed a solution with a proprietary large language model (LLM) to automate the maintenance process, and all stakeholders promoted alignment and training to aid adoption. The project led to increased data exchange, more up-to-date provider directories, and increased savings on administrative work.
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Key Findings:
- Executive Summary
- The Collaborators
- Points of Friction—Challenges to Be Solved
- Action Plan—How the Collaborators Worked Together to Reduce Friction
- Points of Light—Outcomes Achieved Through Collaboration
- Lessons Learned—What Best Practices Can Other Organizations Replicate?
- What's Next?—Vision for the Future
Writer
Natalie Hopkins
Designer
Jess Wallace-Simpson
Project Manager
Joel Sanchez
This material is copyrighted. Any organization gaining unauthorized access to this report will be liable to compensate KLAS for the full retail price. Please see the KLAS DATA USE POLICY for information regarding use of this report. © 2025 KLAS Research, LLC. All Rights Reserved. NOTE: Performance scores may change significantly when including newly interviewed provider organizations, especially when added to a smaller sample size like in emerging markets with a small number of live clients. The findings presented are not meant to be conclusive data for an entire client base.