AI in Prior Authorization: How Technology is Streamlining Access to Care

Prior authorization is a widely used cost-control measure that requires healthcare providers to obtain approval from a patient’s insurance coverage before delivering certain services, procedures, or medications. While designed to ensure appropriate care and manage healthcare spending, the prior authorization process is often time-consuming and complex, creating administrative burdens and delaying patient treatment.

As healthcare demands grow and operational efficiency becomes more critical, artificial intelligence (AI) is playing an increasingly important role in transforming how prior authorization is handled. AI automation and AI-enabled tools are now being adopted by health insurers and managed care organizations to streamline workflows, improve payer decision-making, and enhance patient access to care.

The Traditional Prior Authorization Process

In its traditional form, the prior authorization process is largely manual and paper-based. It requires physicians and their staff to submit detailed documentation, often by fax or phone, to justify the medical necessity of a proposed treatment, medication, or procedure. Health insurers review these requests to determine if they meet payer requirements and are covered under the patient’s insurance policy.

This manual workflow is widely recognized as inefficient and burdensome. Physicians and their teams spend significant time gathering records, filling out forms, and following up with payers, diverting attention from direct patient care.

According to the American Medical Association, physicians and staff spend an average of 13 hours per week on prior authorization tasks for a single physician. Many practices employ dedicated staff just to manage these requests. This administrative burden not only reduces productivity but also contributes to physician burnout, as clinicians are forced to navigate complex approval processes.

More than 90% of physicians surveyed report that prior authorization delays care. Nearly a third say it has led to serious adverse events, like hospitalizations and emergency department visits. Waiting for approval can postpone treatment, leading to worsened clinical outcomes and, in some cases, patient harm. Patients may also face additional office visits or out-of-pocket costs. Difficult processes and denied coverage can even lead to patient abandonment.

The traditional prior authorization process is characterized by administrative inefficiency, increased costs, and negative impacts on both patient access and clinical outcomes. These challenges have prompted calls for reform and set the stage for the adoption of augmented intelligence and artificial intelligence in prior authorization.

The Role of AI in Prior Authorization

AI technologies, particularly those involving machine learning and natural language processing, can help automate and streamline various steps of the PA process. Here’s how:

1. Determining Authorization Requirements

AI automation can instantly review payer policies and clinical guidelines to determine whether a prior authorization is needed, reducing reliance on manual checks and outdated documents, and supporting evidence-based criteria.

2. Document Preparation and Submission

AI-powered systems can auto-populate required forms using clinical data pulled from electronic health records (EHRs). This automation decreases the likelihood of errors or incomplete submissions that lead to delays or denials.

3. Payer Policy Monitoring

AI algorithms can monitor changes in payer requirements in real -time and alert providers when documentation protocols or PA rules are updated, helping maintain compliance.

4. Faster Decision-Making

By facilitating cleaner submissions and proactive tracking, AI-powered automated decision systems can help reduce turnaround times from days to hours, benefiting both providers and patients.

5. Denial Prediction and Prevention

Predictive analytics powered by AI-enabled tools can flag high-risk cases that are likely to be denied, allowing staff to correct issues before submission and improve overall approval rates. This can reduce prior authorization denials.

READ MORE: careviso’s Analytics Model: Predictive Power in Healthcare

[H2] Benefits of AI in the Prior Authorization Process

Integrating AI into prior authorization workflows offers a range of benefits for healthcare providers, payers, and patients.

• Reduction in administrative workload: By automating routine and repetitive tasks such as checking payer requirements, generating documentation, and submitting requests, AI automation frees up clinical and billing staff to focus on more value-added activities, reducing physician burnout. Submission accuracy and completeness: Quality submissions reduce the likelihood of claim denials and the need for resubmissions. This not only helps prevent patient care delays but also enhances revenue cycle performance for healthcare organizations.  
• Keep up with payer needs: AI-enabled tools monitor and adapt to changing payer requirements in real time, helping providers stay compliant and avoid costly errors.  
• Easily scale processes: AI solutions are scalable, meaning they can be deployed across various departments, service lines, and specialties. This makes AI a sustainable solution for healthcare systems seeking long-term operational improvements.

Overall, these efficiencies translate directly to faster patient access to care, as AI-driven automated decision systems can significantly shorten the time it takes to obtain approval, reducing wait times from days to hours in many cases.

Addressing the Risks and Challenges of AI in Prior Authorization

While artificial intelligence in prior authorization offers significant promise, it also represents new challenges. The industry is working to address these challenges, but providers should be aware of them nonetheless.

• Increased Denials: If not set up properly, automated decision systems can lead to increased care denials and “batch denials,” sometimes without adequate human review or clear explanations. This lack of transparency can leave both patients and providers frustrated, especially when denials are based on rigid algorithms rather than nuanced, evidence-based criteria.
• Patient Care Delays: Incorrectly denied treatments can lead to delayed care, additional office visits, or even hospitalizations. Over-reliance on automation could remove the “human in the loop” aspect of payer decision-making, making it harder to appeal decisions or address unique patient circumstances.
• Regulatory Oversight: Without clear standards for transparency, accountability, and auditable recommendations, there is a risk that health insurers may prioritize cost control over clinical outcomes and patient safety. AI tools must be used responsibly with ongoing human review, evidence-based criteria, and robust regulatory oversight to prevent patient harm.

Industry Adoption and Outlook

Healthcare organizations from hospitals to specialty practices, are increasingly adopting AI tools to manage prior authorizations more efficiently. Regulatory efforts, such as the push for electronic prior authorization from the Centers for Medicare & Medicaid Services (CMS), are further accelerating this trend among managed care organizations.

As payer rules become more complex and provider workloads increase, AI is expected to play a central role in modernizing prior authorization workflows and improving healthcare delivery at scale.

READ MORE: The Road to Improved Prior Authorization: What the Interoperability in Healthcare Act Means for 2026 and Beyond

Best Practices for Using AI in Prior Authorization

Because AI in prior authorization is a relatively new concept, best practices are still being developed while these tools are already in use. Keep these guidelines in mind when implementing AI in the prior authorization process:

1. Maintain Human Oversight

While AI automation can efficiently process straightforward prior authorization requests, it is essential to maintain human review for cases that are complex, unusual, or do not fit established patterns. Having a clinician or trained reviewer involved in these decisions helps ensure that unique patient needs are considered and reduces the risk of incorrect care denials.

2. Train Clinically Accurate AI Models

AI systems should be built and regularly updated using the latest clinical guidelines and evidence-based criteria. This approach ensures that automated decision systems align with current standards of care and support appropriate determinations of medical necessity. Regular audits and updates help prevent outdated or biased algorithms from negatively impacting patient care and clinical outcomes.

3. Prioritize Transparency

Health insurers and technology vendors should prioritize transparency in how AI-enabled tools make decisions. Clear communication about the criteria used, reasons for prior authorization denials, and the steps required for the appeals process empowers both providers and patients.

READ MORE: Enhancing Healthcare Transparency: Explanation of Benefits for In-Office Procedures

4. Balance Costs With Clinical Outcomes

While one of the goals of prior authorization and the use of AI is to manage costs, it is crucial that cost control measures do not affect patient safety or clinical outcomes. AI tools should be designed to support high-quality care, minimize patient harm, and avoid unnecessary patient care delays. Ongoing monitoring of outcomes and patient experiences can help identify areas where the balance between efficiency and safety needs adjustment.

5. Provide AI Training and Maintenance

Successful implementation of AI in prior authorization requires ongoing training for both providers and payer staff. Users should understand how to interact with AI-enabled tools, recognize their limitations, and know when to escalate cases for human review. Continuously monitoring AI performance by tracking error rates, appeals, and clinical outcomes will help identify issues and drive improvements.

How careviso Uses AI in Prior Authorization

AI is a core component of careviso’s platform, seeQer, which helps automate and simplify front-end processes like eligibility verification, benefits determination, and prior authorization. Our platform uses AI automation to:

• Instantly determine whether PA is required
• Auto-generate payer-specific documentation
• Track and adapt to policy changes
• Deliver real-time cost estimates alongside PA guidance
  

The result is a faster, more transparent process that improves patient access and reduces the operational strain on providers, making more efficient use of health care resources

READ MORE: Enhancing the PA Process For Faster, More Accurate Results: The careviso Approach