Is being a Health Economics Outcomes Researcher
at risk from AI?

Not in the foreseeable future. While AI can accelerate data processing and standard analyses, the core value of HEOR lies in regulatory strategy, payer negotiation, and methodological judgment in ambiguous situations. HTA bodies require human accountability for economic claims, and payers trust individuals, not algorithms, to interpret evidence in the context of their formulary priorities. The role will evolve—less time on manual data work, more on strategic interpretation—but demand for human expertise is growing as precision medicine and value-based care create more complex reimbursement questions.

Literature screening, data extraction from EHRs and claims databases, and execution of standard cost-effectiveness models are already being accelerated by AI tools. If your day is dominated by running pre-specified Markov models or pulling ICD codes, you should shift toward study design, regulatory strategy, and stakeholder engagement. The tasks that require understanding payer psychology, anticipating regulator objections, or designing novel endpoints for first-in-class therapies remain firmly in human hands.

Both, but prioritize the latter. Fluency in R or Python helps you audit AI-generated code and stay credible with data science teams, but your competitive advantage is knowing what questions to ask and how to frame evidence for decision-makers. Deep knowledge of HTA guidelines (NICE methods, ICER value framework), causal inference, and therapeutic area nuances will differentiate you far more than coding skill alone. Use AI to handle syntax; you focus on study design and interpretation.

Senior HEOR professionals with payer relationships and regulatory expertise will see stable or rising compensation, as their strategic input becomes more valuable when routine analysis is automated. Junior roles focused on data cleaning and standard modeling may face wage pressure or slower hiring growth. However, overall demand is strong: the shift to value-based contracts, biosimilar competition, and orphan drug pricing all require sophisticated economic evidence. The field is not shrinking; it is stratifying by skill level.

Pharma and consulting HEOR roles are more resilient because they are tied to revenue-critical decisions (reimbursement, pricing, market access). Academic HEOR is valuable for methodological innovation but can be more vulnerable if funding shifts toward computational methods. In pharma, embed yourself in cross-functional teams (clinical development, commercial) so your work directly influences launch strategy. In consulting, build a reputation with specific payers or therapeutic areas where you are the trusted advisor.

Three areas: (1) Real-world evidence and causal inference—synthetic controls, target trial emulation, and external control arms are replacing traditional RCTs in some settings. (2) Value framework fluency—understanding how QALY calculations are evolving (e.g., ICER's adaptations for ultra-rare diseases, equity weighting). (3) Digital health and decentralized trial endpoints—wearables, patient-reported outcomes via apps, and remote monitoring create new measurement challenges that require methodological leadership. These are areas where precedent is thin and human judgment is essential.

Junior roles that involve executing predefined analyses, running standard models, or compiling literature reviews face moderate automation pressure—AI can do 50-70% of these tasks today. Senior roles focused on study strategy, regulatory negotiation, and payer advisory work are highly resilient. If you are early-career, accelerate your path to strategic work: volunteer for protocol design, attend advisory boards, and build relationships with market access teams. Do not stay in the 'analysis factory' role longer than necessary.