The Role of AI in Modernizing Provider Credentialing

Provider credentialing can be a cumbersome, error-prone, and time-consuming process. Yet, it is a critical step that ensures healthcare professionals are qualified and competent, safeguards patient safety, and helps healthcare facilities maintain compliance with regulatory standards. 

Enter artificial intelligence (AI)—a game-changer that has the potential to improve healthcare credentialing with speed and efficiency. The adoption of AI in credentialing is low, but more healthcare organizations are starting to explore how to leverage AI tools in the credentialing process. From automating routine data entry to flagging potential compliance risks, AI and machine learning could bring new opportunities to transform how healthcare practices manage credentialing. 

In this article, we will discuss:

• The Challenges of Traditional Credentialing
• Potential Applications of AI in Credentialing
• How to Navigate the Limitations of AI in Credentialing
• The Future of Credentialing with AI

The Challenges of Traditional Credentialing

Traditional credentialing is not simple because it involves countless manual tasks such as:

• Verifying credentials with licensing boards and educational institutions.
• Tracking submission deadlines to comply with payer and regulatory rules.
• Monitoring credentials for expirations or updates.
  

These processes provide little room for error. Missed deadlines or inaccuracies directly impact revenue cycles, cause compliance risks (like sanctions from the Office of Inspector General [OIG]), and delay patient care. For smaller healthcare practices that have limited resources, the work of credentialing becomes even more challenging.

The good news? AI-powered solutions are coming in this space, and some of the heavy lifting could be automated.

Potential Applications of AI in Credentialing

Data regarding the adoption rate of AI in credentialing is limited because AI is an emerging technology, and recent studies have been focused on broader trends in AI rather pinpointing specific applications. There is data regarding the overall use of AI in healthcare. The American Medical Association said that the overall use of AI among physicians is growing. They said 66% of physicians reported the use of healthcare AI for certain tasks (i.e., documentation of billing codes, medical charts, or visit notes) in 2024 and that was up from 38% of physicians who said they used AI in 2023.

Meanwhile, Bain and Co. reported that among healthcare providers surveyed, only 35% of completed proof-of-concept AI projects have made it into production. 

As AI gains traction, it is important to recognize that leveraging AI for credentialing would allow organizations to create more efficient processes and potentially position them to thrive in an industry where the technology and regulatory landscape continually evolve. 

Here’s how AI could be used to improve credentialing and enrollment processes for healthcare organizations:

1. Streamlining Data Entry

AI-powered credentialing systems could eliminate manual data entry by automatically extracting and verifying provider details from digital documents or public data sources. These tools connect to national databases to pull licensing information and certifications while flagging any discrepancies. For instance, a hospital that previously spent weeks onboarding new providers could complete primary-source verification within days and spot any red flags within hours, thanks to AI.

In addition, AI can connect disparate data sources within health care information systems. This is a valuable integration in which AI can translate data from different systems into a common format, making data compatible across diverse platforms. 

2. Real-Time Monitoring and Alerts

Credentialing is not a one-time job. Continuous monitoring is necessary to track changes such as license expirations, disciplinary actions, sanctions, malpractice claims, or board certification updates. AI systems someday may provide real-time updates and automatically alert teams about upcoming deadlines or inconsistencies in provider profiles.

This proactive approach could help healthcare organizations maintain compliance and help them avoid doing last-minute work. 

3. Optimizing Workflows

AI algorithms offer intelligent workflow management that could help teams optimize resource allocation. AI-powered systems have the ability to automatically prioritize applications based on urgency and complexity, as well as route tasks to team members based on workload. AI could predict renewal deadlines, generate alerts, and analyze historical data to predict potential delays, allowing for proactive intervention.

4. Automating Payer Enrollment

AI shows promise in improving the complex and repetitive process of payer enrollment. When submitting applications to government or commercial payers, AI could help credentialing specialists ensure all required fields are correctly completed and cross-verify data for accuracy. AI tools may also have the ability to track the progress of applications and provide updates, minimizing delays and rejected claims.

5. Simplifying Compliance Reporting

Credentialing professionals can feel like they’re chasing a moving target when trying to keep up with payer-specific and regulatory requirements. AI-driven compliance tools have the potential to help bridge this gap by analyzing healthcare industry updates and generating compliance reports tailored to accreditation standards like The Joint Commission (TJC) or the National Committee for Quality Assurance (NCQA).

For large-scale organizations, this could result in a drastic reduction in the risk of fines, lost reimbursements, and operational bottlenecks.

6. Tackling Administrative Overhead

Credentialing teams often juggle tedious administrative tasks, including email communication and document organization. AI-enabled credentialing platforms have the potential to lighten the load by automating tasks such as:

• Drafting and proofreading emails.
• Managing follow-ups and tracking responses.
• Creating and updating detailed records, like payer matrices or contract comparisons.

How to Navigate the Limitations of AI in Credentialing

AI has the potential to offer significant benefits in credentialing, but there are limitations. These limitations also exist within other healthcare environments in which AI is being considered. The key is to understand the limitations and find ways to navigate through them.

“AI technologies hold great promise for improving healthcare outcomes, enhancing patient care, and optimizing resource allocation,” researchers wrote in a recent study. “Nonetheless, integrating AI into healthcare settings remains a challenge even with its substantial potential benefits. It is, therefore, crucial to identify and address the barriers that hinder its successful implementation to fully harness the transformative power of AI in healthcare.” 

As credentialing specialists start to explore the use of AI, they should first consider the following:  

1. Over-reliance on AI must be balanced with human oversight

Credentialing offices must strike a balance, eventually, between using AI for efficiency improvements and relying on human oversight to achieve optimal results. AI is effective at detecting anomalies and finding gaps in data, but it is essential that humans retain a healthy level of skepticism towards AI to avoid potential pitfalls, including those with severe or even fatal consequences. 

Before implementing AI, identify which tasks your team can effectively automate and implement clear processes for human review and intervention. Human oversight will be needed for strategic and complex decision-making, ethical and moral considerations, and handling exceptions, while AI can be leveraged for repetitive tasks and credentialing data analysis. Humans need to regularly monitor and refine AI algorithms to ensure fairness. Finally, a human credentialing professional should provide final verification and approval to ensure accuracy and adherence to standards. 

2. Data quality and availability are key for accurate AI

AI models are trained on data, so if the data is inaccurate, incomplete, inconsistent, or outdated, the output from the AI tool will be unreliable. Inaccurate data could lead to incorrectly credentialed providers, potentially jeopardizing patient safety and compliance. Without good data quality, AI loses its potential to make a positive impact.   

Therefore, it is critical that healthcare organizations utilize high-quality data to ensure any AI models they are using learn accurate patterns and produce meaningful, reliable insights. Credentialing teams will want to continue using different methods to verify the accuracy of provider data, including primary source verification (PSV).

3. AI regulatory landscape continues to evolve

Regulatory frameworks for AI in credentialing are still in development. Credentialing specialists must stay informed as both federal and state legislation evolves as it pertains to AI. 

Healthcare organizations “may choose to leverage voluntary methods and frameworks” such as the National Institute of Standards and Technology’s (NIST) AI Risk Management Framework, stated the World Economic Forum. “Self-governance of AI systems will involve both organizational and, increasingly, automated technical controls,” they added.

4. AI training for employees is essential

Prioritize equipping your credentialing team with the skills they need to work effectively alongside the AI system. Determine which AI tools and skills are relevant for each department and each role.

Start by building a foundation of AI literacy. Provide hands-on practice with real-world applications and create role-specific training modules. Set clear goals to ensure employees understand what they are expected to achieve, whether it’s improved efficiency, reduced costs, or increased application processing.

5. Security risk

The security risks of using AI in credentialing revolve around the sensitive provider information they handle, including academic records and professional licenses, plus the potential vulnerabilities within the AI system itself. AI platforms must be properly secured to prevent them from becoming targets for cyberattacks. 

Organizations should adopt a multi-faceted approach to mitigate data security risks, including implementing robust security measures, maintaining human oversight and establishing clear audit trails, investing in AI security training and awareness programs, and collaborating with the organization’s cybersecurity team to address potential threats.

The Future of Credentialing with AI

While not yet ubiquitous, the use of AI in credentialing is expanding. Healthcare organizations have a strategic opportunity to improve the credentialing process by enhancing provider onboarding, reducing operational delays, and improving patient care. There are many pros and cons of using AI in credentialing, but it can be at the forefront of this transformation, and has the potential to provide precision, speed, and scalability to meet the demands of modern healthcare organizations.

Request a demo today and prepare for the future with the industry’s most comprehensive set of capabilities in provider credentialing.