Xia Wang
Head, RWE Solutions, Global HTA & Payers, Patient Evidence UCB
Xia currently is an executive leader at UCB, manages talent teams and drives the strategic development & implementation in delivering integrated RWE innovations across full cycle of medicines development. Previously Xia held a senior director position at Data Science & Artificial Intelligence group with AstraZeneca, where Xia led the health data science efforts in RWE to support clinical development, epidemiology, and innovative payer strategy. Prior to stepping into the clinical divide, Xia was with AstraZeneca Psychiatry Discovery in the areas of informatics and computational modeling to support drug discovery. Xia holds a Ph.D. in computational chemistry.
Seminars
- Why do AI‑driven RWE initiatives so often succeed technically but fail organizationally, and where do most companies get stuck?
- How can RWE/HEOR leaders better align data scientists, analysts, and medical experts around shared goals, methodologies, and accountability?
- What practical approaches are teams using to validate AI outputs without slowing delivery or undermining confidence in the evidence?
- How should organizations address cultural resistance and skills anxiety when AI drastically changes timelines, roles, and workflows?
- Who ultimately owns AI‑generated evidence and what governance models support innovation while protecting scientific and regulatory credibility?
This interactive workshop brings together leaders across epidemiology, biostatistics, HEOR, and market access to examine where RWE breaks down in practice, and how to design evidence that consistently delivers decision impact.
This workshop will gather experts to discuss:
- Identifying where RWE fails to translate into regulatory, payer, and clinical decisions despite strong methodology
- Understanding how different stakeholders interpret uncertainty, bias and evidence strength, and why alignment breaks down even with high-quality data
- Balancing data complexity with clarity by ensuring analytical approaches, including advanced methods and AI, remaining interpretable and decision‑relevant
- Applying statistical and epidemiological rigor to improve comparability, mitigate bias, and ensure outputs are credible across use cases
- Designing evidence strategies earlier in development that align with downstream
decision needs to reduce rework, delays, and missed impact
