From Delays to Approvals: AI’s Role in Streamlining Prior & Re-Authorizations for Biopharma

At Agilisium, we believe that healthcare should be accessible, efficient, and patient-centered. As we continue to see the rise of biologics and specialized therapies, particularly in oncology and rare diseases, the need for effective cost containment strategies becomes more critical. However, despite the transformative potential of these treatments, many patients still face significant barriers to access, often due to lengthy insurance approval processes and high out-of-pocket costs.

In the current healthcare landscape, Prior Authorization (PA) and Re-Authorization (RA) processes are vital in ensuring appropriate treatment but have often become a source of frustration for patients, providers, and biopharma companies alike. At Agilisium, we are committed to leveraging technology to solve these challenges. By integrating the power of AI and ML, we see a future where PA and RA processes are streamlined, approvals are expedited, and, most importantly, patients receive the treatments they need - without unnecessary delays.

The Current Landscape: A Stumbling Block for Biopharma and Patients Alike

The process of obtaining Prior Authorization has long been a source of pain for healthcare providers (HCPs), payors, and most importantly, patients. As it stands, PA requires providers to fill out time-consuming forms and submit them to insurance companies or pharmacy benefit managers (PBMs) for approval. If approved, the patient can begin treatment, but if denied, the provider must navigate a cumbersome appeals process.

In chronic therapies, PA does not guarantee ongoing treatment. Re-Authorization is frequently required at intervals, often contingent on certain clinical outcomes. As a result, many patients find themselves in a constant battle to prove the continued efficacy of their medication. Alongside this, the excessive cost of treatments, even with PA approval, often leads to abandonment as patients struggle to afford their medications.

These inefficiencies - long approval times, numerous rejections, and constant appeals result in an alarming rate of treatment delays and abandonment. In fact, some reports suggest that as many as 40% of patients abandon treatment due to these hurdles. For biopharma companies, the revenue loss from these missed prescriptions is substantial.

The Role of AI/ML in Streamlining the Prior Authorization Process

This is where AI and machine learning step in. By leveraging the power of data analytics, biopharma companies can transform the PA and RA process from a slow, reactive experience into an efficient, proactive one. Here is how:

Predicting High-Risk Patients: Identifying Fertile Cases One of the biggest challenges in the PA process is identifying patients who are likely to need PA before it becomes a bottleneck. AI/ML can analyze historical data, including patient demographics, comorbidities, past prescription history, and insurance coverage, to predict which patients are more likely to need PA.

By identifying these high-risk cases early on, case managers and reimbursement specialists can intervene proactively, helping to ensure that the necessary paperwork is filled out correctly and submitted on time. This reduces the likelihood of delays and denials, giving patients faster access to the treatments they need.

Real-Time Analytics: Improving Submission Accuracy is another way AI/ML is improving the PA process is by providing real-time analytics to enhance submission accuracy. By analyzing past submissions and outcomes, AI can identify patterns in successful approvals and common reasons for denials. With these insights, case managers can ensure that they target the right patients and are submitting the right forms, with the right information, to the right payors, thus reducing errors and improving approval rates.

Additionally, AI-powered systems can automate repetitive tasks, such as verifying patient eligibility, filling in forms, and submitting documentation. This not only speeds up the process but also minimizes the risk of human error.

Streamlining the Re-Authorization Process: Ensuring Continuity of Care while PA gets the ball rolling, Re-Authorization is just as crucial to ensure that patients continue to receive their medication. AI/ML plays a pivotal role here as well, by helping case managers track which patients are due for reauthorization and ensuring that all required clinical reports, diagnostic tests, and lab results are readied and submitted on time.

AI can also analyze the outcomes of treatments and compare them against baselined metrics to determine whether the patient is likely to continue benefiting from the therapy. If the treatment is proving effective, this data can be used to advocate for approval in the RA process, reducing the need for re-appeals.

Overcoming Financial Barriers: Bridging the Funding Gap is another significant hurdle in both PA and RA processes, because of the excessive cost of biologics and specialty treatments. Even after PA approval, many patients still cannot afford their medications due to high copays or deductibles etc.

Here, AI/ML solutions can integrate with patient assistance programs, such as copay cards or philanthropic support, to identify eligible patients and automatically enroll them in financial assistance programs. By bridging this funding gap, patients are more likely to stay on treatment, improving adherence and reducing the likelihood of abandonment.

Optimizing Resource Allocation: Focusing on fertile cases among the volume of cases biopharma companies handle, resource allocation becomes a challenge. AI/ML tools can prioritize cases based on the likelihood of success, helping case managers focus on patients with the highest chance of approval. This ensures that biopharma resources are being used most efficiently, maximizing the chances of treatment success while minimizing wasted effort on cases that are unlikely to succeed.

The Path Forward: How AI/ML Transforms Patient Outcomes

By enabling smarter workflows, AI/ML can help biopharma companies streamline the PA and RA processes, ensuring that patients are getting timely access to the medications they need. Here is a glimpse of what the future could look like:

Enhanced Patient Access: With predictive analytics, case managers can proactively identify high-risk cases and ensure they are processed quickly.

Higher Approval Rates: AI-powered insights reduce errors in form submissions, leading to fewer rejections and faster approvals.

Treatment Continuity: AI ensures timely follow-ups for Re-Authorization, making sure patients do not miss ongoing therapy.

Financial Support: With automated copay assistance tools, patients can bridge the funding gap and continue their treatments without disruption.

Data-Driven Decisions: Biopharma companies will be able to prioritize resources and focus on the most fertile cases, improving overall efficiency and outcomes.

A Win-Win for Patients and Biopharma Companies

At Agilisium, we believe that technology should not just streamline processes, it should transform lives. By embracing AI and ML, we can reimagine how Prior Authorization and Re-Authorization are done in the biopharma space. No longer should patients be delayed or denied access to life-changing therapies due to administrative hurdles. The future of healthcare is personalized, efficient, and patient-centered, and with the right tools, we can ensure that every patient gets access to the right treatment at the right time.

For both patients and biopharma companies, the adoption of AI/ML is not just a smart move, it is an essential step toward delivering better outcomes, faster treatments, and a more compassionate healthcare experience.

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