Deadline to Save Up to $1,000:
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Monday, March 16th, 2026 — Pre-Conference Workshops - EST/EDT (Eastern Daylight, GMT-4)
Identify upcoming legislative and regulatory changes most likely to impact patient support programs and access initiatives
Understand how policy changes are viewed differently by government, industry, and patient advocacy organizations
Develop strategies to anticipate and prepare for policy shifts before they disrupt patient access
- Aaron Winn - Associate Professor, University of Chicago
Understand how Medicare drug price negotiations reshape patient assistance program design and eligibility requirements
Leverage premium smoothing provisions to enhance affordability while maintaining program sustainability
Adapt patient support services to align with Part D benefit restructuring and out-of-pocket caps
Distinguish between generative AI, machine learning and predictive analytics models and evaluate the unique benefits and challenges of each
Map specific AI technologies to common patient journey pain points, from enrollment to adherence support
Build a framework for assessing AI maturity and readiness within your organization's patient support infrastructure
Master the current FDA guidance and industry standards governing AI applications in patient support programs
Implement ethical frameworks that protect patient data while enabling personalized support experiences
Create governance structures that maintain human oversight while leveraging AI capabilities
Implement a strategic timeline for engaging market access, trade, patient services, field teams and pricing/contracting stakeholders at optimal pre-launch intervals
Develop effective distribution models through collaborative planning between commercial and trade colleagues
Create frameworks for assessing market and payer challenges that inform contracting strategies and access planning
Design patient identification approaches that scale appropriately from the focused rare disease setting to broader specialty markets
Implement competitive positioning tactics that account for different stakeholder landscapes and treatment alternatives
Create resource allocation models that optimize investment across the different demands of rare and mainstream therapeutic areas
Explore how the Big Beautiful Bill (BBB) and new administration priorities transforms the patient support landscape, requiring innovative approaches to ensure continued access
Develop contingency plans for the revived Most Favored Nation drug pricing model and its impact on patient affordability
Master the changing Medicaid/Medicare landscape to serve dual-eligible populations
Identify cross-sector collaboration opportunities, including how to generate tools and resources for education
Understand the early warning signs for potential access barriers
Generate methodology on how to evaluate policy impact on patient access
Analyze case studies of AI implementations that significantly improved prior authorization success rates and adherence metrics
Evaluate integration approaches that connect AI capabilities with existing hub service platforms and workflows
Recognize warning signs of AI applications that create more problems than they solve
Witness compelling arguments from both AI advocates and critics on key implementation controversies
Develop strategies for addressing resistance to AI adoption while also legitimatizing concerns
Create a balanced perspective that acknowledges both AI's transformative potential and its practical limitations
Implement "watchtower" data aggregation systems that provide patient-level visibility into the treatment journey
Design cross-functional governance models that maintain launch-team integration well beyond initial market entry
Develop strategic approaches to hub program design that balance control requirements with access optimization
Create intervention protocols that quickly address reimbursement hurdles and access challenges as they emerge
Design patient support ecosystems that leverage uniquely US-market hub and specialty pharmacy capabilities
Create adaptable brand planning frameworks that accommodate different critical success factors across global markets
Implement competitive differentiation strategies tailored to market size, competition level, and treatment paradigms