Reimagining patient services: The transformative role of Artificial Intelligence and Intelligent Automation in US market access

The US challenge: fragmentation, friction, and fatigue 

For patients with prescribed high-cost specialty or rare-disease therapies, the journey from prescription to therapy initiation can be daunting. Benefit verification, prior authorization, co-pay assistance, financial counseling, and adherence support are typically managed through Patient Support Programs (PSPs), multi-stakeholder ecosystems designed to bridge access gaps. 

Yet many PSP operations remain heavily manual, fragmented across disparate technology platforms, and reliant on human intervention for repetitive data entry, case triage, and communication. The result: delays in therapy start, inconsistent patient experiences, and inefficiencies that strain both manufacturers and service providers. 

As patient needs grow, more complex and payer scrutiny intensifies, a new model for PSP operations is emerging—one powered by Artificial Intelligence (AI) and Intelligent Automation (IA). 

The promise of Artificial Intelligence (AI) and Intelligent Automation (IA) 

AI and IA, when referenced in the context of PSPs, include Robotic Process Automation (RPA), Natural Language Processing (NLP), and Machine Learning (ML), are redefining how US patient services organizations operate. These technologies enable faster, more consistent, and more scalable operations across the patient journey. However, the current rate of adoption varies across each of the technologies. 

Key applications include: 

• Automated Benefit Verification (eBV): AI models interpret payer responses, standardize data fields, and determine coverage criteria in seconds, reducing turnaround time and human error.
• Smart Prior Authorization (PA) Processing: NLP-powered tools analyze payer requirements and auto-populate authorization requests, streamlining a historically manual and error-prone process. 
• Case Triage and Routing: ML algorithms categorize inbound referrals and route them appropriately, ensuring urgent cases receive priority attention.  
• Omnichannel Communication Automation: Conversational AI engages patients via chat, text, or email to provide updates, reminders, and education—augmenting human case managers rather than replacing them.  
• Continuous Improvement Loops: Intelligent analytics track performance indicators such as turnaround time, abandonment rate, and satisfaction, allowing PSPs to self-optimize through real-time learning.  

From reactive to predictive: the next phase of automation

While today’s automations often focus on efficiency, the next evolution lies in predictive and prescriptive analytics. AI can now analyze historical program data to anticipate barriers, such as patient drop-off risk, payer denial probability, or refill non-adherence before they occur. 

By surfacing these insights in real time, manufacturers and hub partners can proactively intervene, improving patient outcomes while maximizing operational ROI. 

The human element: augmentation, not replacement 

AI and IA are not designed to replace the empathy, expertise, and judgment of patient support specialists. Instead, they elevate these professionals, freeing them from administrative tasks to focus on what matters most: patient engagement, emotional support, and adherence coaching. 

Upskilling case managers to interpret AI-driven insights, manage digital tools, and maintain “digital empathy” will be essential to realizing the full potential of automation

Responsible AI: governance and trust as imperatives 

As adoption accelerates, governance frameworks are critical to ensuring that AI in patient services remains transparent, explainable, and aligned with ethical standards. Responsible AI requires clear accountability, bias monitoring, and human-in-the-loop oversight. 

In the United States, AI-enabled PSPs must also comply with the Health Insurance Portability and Accountability Act (HIPAA) and related data privacy regulations, preserving patient trust while enabling operational scale. 

Integration with real-world data and digital health 

The next generation of patient support will connect seamlessly with broader digital health ecosystems. Integrating Real-World Data (RWD), electronic health records, and digital therapeutics will allow manufacturers and PSP partners to measure outcomes longitudinally and personalize interventions dynamically.

This convergence of clinical, behavioral, and operational data will unlock new insights into adherence, efficacy, and equity across the patient population.

Quantifying the impact: the economics of AI in PSPs 

The business case for automation is compelling. Intelligent automation in healthcare could drive over $200 billion in annual efficiency gains by 2030 In patient services specifically, early adopters are already seeing 30–40% reductions in manual processing time and significant improvements in time-to-therapy.

By combining automation with continuous analytics, manufacturers can demonstrate not only financial ROI but also improved access and patient satisfaction metrics—outcomes that strengthen payer and provider partnerships. 

A global perspective

For colleagues outside the United States, the scale of administrative complexity in the US healthcare system may seem extraordinary. Yet it is precisely this complexity that makes it an ideal testbed for responsible AI adoption. Lessons learned from these implementation, particularly around governance, interoperability, and patient engagement, can inform market access strategies worldwide. 

The path forward 

The future of patient services will be defined by intelligent orchestration—where every touchpoint, from benefit verification to adherence, is informed by data, guided by AI, and executed through automation.

Those who invest early in building agile, AI-enabled operations will not only reduce cost but also deliver faster, more equitable access to therapy. The future of patient services is one where human compassion and intelligent automation coexist harmoniously, empowering manufacturers, providers, and patients to achieve better outcomes, faster.