To reimagine the Credentialing process augmented by artificial intelligence, the approach begins with rethinking the traditional and time-consuming credentialing process. The envisioned future state centers around the development of an AI-driven process that includes automated document validation, prioritizes document requests, and intelligently ranks case files for priority completion based on various criteria. A streamlined process redesigned to take advantage of AI offers benefits such as time and cost savings, avoiding backlogs, enhancing provider experience, improving compliance, and supporting expedited collection and verification tasks. By further integrating AI technology with downstream processes to on-board newly credentialed providers, (e.g., greatly reducing the time lag to be listed in on-line provider directories), the time from credentialing to enrollment to patient scheduling can be significantly reduced, enabling new providers to start seeing patients. Embracing AI-driven methods in credentialing transforms the workflow, making it more efficient and effective while also laying the foundation for sharing new provider data with downstream processes and related core administrative systems.

Utilizing AI in Provider Credentialing

1.  Utilize the Credentialing Copilot to anticipate issues and delays continuously analyzing process activity, resource productivity, status of overdue information or verification requests, and other sources of data that assist in managing credentialing performance

2.  Utilize the Copilot Console for transmission status in sending/receiving data feeds, knowledge of new member/patient complaints, coordination with internal credentialing committees, expected volume changes in new clinicians being on-boarded

3.  Apply the Copilot’s experience with the credentialing function’s actual performance over time to plan for resources to be allocated to re-credentialing workload volume

4.  Review AI driven analysis of network growth plans and service area expansion to plan for peak credentialing periods with additional staff or a third party organization to handle credentialing capacity overflow

5.  Add learned knowledge/on-going operations experience to the AI Knowledge Base for future reference by the AI to assist in decision-making for provider network expansion, workforce planning, operational excellence and unexpected events such as a merger or acquisition

6.  Produce AI learned best practices when determining how to plan for and implement changes in contracted provider networks that may require re-credentialing

7.  Apply the knowledge of the AI to other core systems and workflows that create their own provider data files from the provider files created during the credentialing process to ensure provider data accuracy and timeliness - especially after re-credentialing events

8.  Allow the Credentialing AI to access more information by expanding its knowledge base continuously teaching the AI any unexpected results of its predictions, filling gaps in its data models to improve the reliability of its predictions to enhance its recommendations when estimating time duration, level of effort and probable outcome for peaks in credentialing volume

9.  Enhance the Credentialing AI’s knowledge to become an enterprise resource which can access strategic growth plans for the year, and then predict the required provider network expansion offering guidance, insights, reminders and relevant recommendations to prepare in advance for increased credentialing volume

10.  Grow the AI Credentialing Copilot to be a “partner” - not a replacement - for the experienced users in your provider credentialing function