Beyond Approval: AI’s Role in Expanding Access to Rare Disease Therapies
This is part of our ongoing content series from the Syneos Health Trends report.
AI is not just an innovation—it is a lifeline for patients with rare diseases.
For those living with rare diseases, regulatory approval of a new therapy is just the beginning. Access remains a significant challenge, with barriers ranging from high costs and reimbursement hurdles to physician awareness and patient identification. AI is now playing a pivotal role in ensuring that therapies reach the patients who need them—faster, more efficiently and equitably.
"The challenge doesn’t end with FDA approval—getting the right therapy to the right patient at the right time remains a monumental hurdle,” noted Stacy Hartung, Vice President of Marketing, Technology and Data Solutions at Syneos Health. “AI has the potential to harmonize fragmented healthcare data, helping patients find the treatments they need and ensuring providers have the right information at their fingertips.”
The Access Gap in Rare Disease Treatments
Despite growing innovation in rare disease drug development, access remains a major bottleneck. Gene therapies and other advanced treatments come with staggering price tags, often exceeding $1 million per patient. Even when treatments are available, physicians may not be aware of them, and patients may struggle to navigate complex reimbursement landscapes.
These access challenges have led to significant disparities, particularly for patients in low-resource healthcare systems or those with ultra-rare conditions. AI is helping to bridge these gaps by optimizing everything from reimbursement strategies to real-world evidence (RWE) generation and patient engagement.
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AI-Powered Solutions for Expanding Access
AI in RWE and Outcomes-Based Reimbursement: Payers increasingly demand real-world data to justify the cost of rare disease treatments, especially for high-cost therapies like gene and cell therapies. AI is transforming how post-market evidence is collected and analyzed utilizing predictive analytics (from sources such as electronic health records, insurance claims and wearable devices); automated data processing (via data extraction from disparate sources to reduce the burden of manual reporting for HCPs); and adaptive pricing models, such as pay-for-performance models.
AI is fundamentally shifting how we think about evidence generation,” notes Hartung. “Instead of waiting years for post-market studies, we can now use real-world data in near real-time to support reimbursement and ensure ongoing access.”
These AI-driven capabilities support more flexible reimbursement models, making it easier for insurers to cover rare disease therapies while ensuring that patients receive sustained benefits.
AI-Driven Patient Identification and Education
Even after approval, many rare disease treatments remain underutilized due to a lack of physician awareness and diagnostic challenges. AI is addressing this by enhancing physician awareness with AI-powered natural language processing (NLP) tools; identifying patients in need with tools like PheNet; and personalizing patient support programs by offering tailored education, reminders and adherence support to ensure continued access and compliance.
"For many rare disease patients, navigating the healthcare system is like finding a needle in a haystack,” noted Hartung. “AI can help cut through the noise, connecting patients with specialists, clinical support, and even financial assistance programs that can help them start and stay on therapy.”
AI in Decentralized and Hybrid Care Models
Another barrier to post-approval access is the burden of frequent hospital visits, particularly for patients in remote or underserved areas. AI-driven telemedicine and decentralized care models are making it easier for patients to receive ongoing treatment and monitoring, with interventions such as remote monitoring with AI-powered wearables, AI driven virtual consultations and predictive adherence models. "The future of rare disease care is not just in the hospital—it’s wherever the patient is,” said Hartung. “AI is allowing us to move from reactive care to proactive engagement, ensuring patients stay on therapy without unnecessary hurdles.”
The Future of AI in Rare Disease Access
Despite its promise, AI in post-approval access faces challenges, including data standardization, regulatory hurdles and the need for ethical AI frameworks. However, experts agree that AI’s role in expanding access to rare disease therapies will only continue to grow.
"We’re still in the early innings of AI in healthcare…But what we’re seeing already—smarter patient identification, streamlined reimbursement and personalized support—suggests we’re on the cusp of a real transformation in rare disease access.”
AI is no longer just a tool for discovery; it is a force multiplier for access. By harnessing the power of machine learning, predictive analytics and real-world data, AI is making rare disease treatments more accessible, more equitable and more effective.
Learn more about how Syneos Health is reimagining what’s possible in healthcare. Download our 2025 Health Trends report today.
Contributor
Stacey Hartung | Vice President, Marketing, Technology and Data Solutions
