HTA-Ready Evidence Dossiers HTA (Health Technology Assessment)-ready evidence dossiers represent a critical deliverable for HEOR teams seeking market access and reimbursement for new therapies. These documents compile clinical, economic, and real-world evidence to meet strict requirements from health technology assessment bodies such as NICE, CADTH, or HAS. HTA-ready evidence dossiers demand more data, tighter timelines,…
AI in Evidence Generation was the quiet headline at ISPOR 2026 in Philadelphia this May. Most exhibit-hall chatter, though, was still about which chatbot gives the "smartest" answer. MadeAi presented four posters at ISPOR 2026. They covered literature review methodology, GenAI validation, drug repurposing, and social media listening for CSL Behring. Together, they tell a…
Why Evidence for Rare Disease Matters AI and real-world evidence (RWE) are reshaping the path from laboratory discovery to regulatory approval. Their convergence matters most for patients who can't afford to wait. For patients with rare diseases, waiting for treatment can mean years of uncertainty. In the United States, a rare disease is generally defined…
SLR vs. Meta-Analysis: An Overview You've just received a research question from your stakeholders. They want to understand the effectiveness of a new therapeutic intervention across the published literature. Naturally, your next thought is: should we conduct a systematic literature review (SLR) or a meta-analysis? In healthcare and life sciences, this question sits at the…
Hereditary Angioedema patient burden remains a significant challenge for those living with this rare, unpredictable condition, even as new treatments emerge. At MadeAi, we specialize in turning real-world patient conversations into actionable intelligence. Our latest social media listening analysis, presented at ISPOR USA 2026, reveals how HAE continues to affect daily lives while highlighting opportunities…
AI-augmented literature reviews are transforming evidence generation across life sciences. This blog explores multi-project analysis of AI-assisted literature reviews. It examines improvements in review speed, screening accuracy, traceability, and workflow efficiency while maintaining rigorous human oversight for submission-ready outcomes. For teams in health economics and outcomes research (HEOR), regulatory affairs, and evidence generation, systematic literature…

