Is being a Health Economics Outcomes Research Manager
at risk from AI?

Unlikely in the next 5-7 years. While AI is rapidly automating literature reviews, data extraction, and standard statistical modeling, the role's core value lies in regulatory navigation, stakeholder trust, and strategic judgment under ambiguity. Payers and HTA bodies require named human experts to defend evidence submissions, and liability for reimbursement decisions cannot be delegated to algorithms. The role will evolve toward orchestrating AI tools while focusing on relationship management, study design, and interpretation—tasks where human judgment remains essential.

Routine data processing tasks face the highest automation risk: literature screening (65% automatable today), standard cost-effectiveness model execution (55%), and formatting regulatory documents (40%). AI tools like systematic review assistants and statistical scripting environments are already widely deployed. However, tasks requiring contextual judgment—selecting appropriate comparators, validating model assumptions for specific payer audiences, and designing studies that balance scientific rigor with commercial timelines—remain largely human-dependent. The shift is from doing analysis to directing and validating AI-generated analysis.

Focus on three areas AI cannot easily replicate: (1) Deepen regulatory and payer expertise—understand ICER, NICE, and regional HTA body processes intimately, including informal decision criteria. (2) Build stakeholder relationship skills—practice negotiating with clinical teams, presenting to payer medical directors, and managing advisory boards. (3) Learn to direct AI tools effectively—become proficient with AI-assisted literature review platforms, automated modeling environments, and LLM-based drafting tools so you can supervise their output rather than compete with them. Certifications in advanced health economics methods (e.g., network meta-analysis, real-world evidence) also create defensible expertise.

Large pharma offers more resilience in the near term due to established HEOR departments, regulatory infrastructure, and the complexity of managing global evidence portfolios that require experienced human oversight. However, smaller biotech firms increasingly need HEOR expertise for early-stage payer engagement and orphan drug submissions, creating niche opportunities. The key differentiator is whether you're seen as a strategic partner or a task executor—large organizations can afford to automate junior analyst roles, while strategic HEOR leaders who shape evidence strategy remain valuable across company sizes.

Not for experienced managers in the next 3-5 years. Demand for HEOR expertise remains strong as regulatory scrutiny intensifies and payers demand more sophisticated evidence. However, entry-level analyst roles may see salary pressure as AI reduces the need for manual data work. Senior managers who can design studies, navigate regulatory complexity, and manage stakeholder relationships will likely see stable or growing compensation, especially in high-value therapeutic areas like oncology, rare disease, and cell/gene therapy where evidence requirements are evolving faster than AI training data.

Junior roles focused on executing predefined analyses—running models, extracting data, formatting reports—face higher displacement risk as AI tools mature. Entry-level hiring may slow as one senior analyst with AI assistance can produce what previously required a small team. Senior roles emphasizing study design, regulatory strategy, and stakeholder management remain resilient because these require accumulated domain knowledge, professional networks, and judgment that AI cannot replicate. The career ladder is compressing: fewer junior roles, but sustained demand for experienced strategic leaders. New entrants should accelerate toward strategic responsibilities rather than spending years in purely analytical roles.

Yes. Markets with stringent regulatory requirements and established HTA processes (UK, Germany, Canada, Australia) offer more resilience because human expertise in local reimbursement pathways is harder to automate. The US market, with its fragmented payer landscape and emphasis on real-world evidence, also sustains demand for strategic HEOR expertise. Emerging markets with less mature HTA infrastructure may see faster AI adoption for cost-effectiveness modeling, but still require human judgment for adapting global evidence to local contexts. Remote work has globalized the talent pool, so geographic resilience increasingly depends on regulatory expertise rather than physical location.