AI-Powered HEOR—With Expert Verification Built In

Artificial intelligence has changed what is possible in health economics and outcomes research. Protocols draft faster. Code generates faster. Models assemble faster. Manuscripts, value dossiers, and payer submissions all move faster. What AI has not changed is what makes evidence trustworthy: sound design, defensible assumptions, and results that hold up under scrutiny from payers and HTA bodies weighing market access and policy decisions, from journals, and from courts evaluating healthcare economic expert testimony.

That is the gap RxEconomics was built to close. We pair generative AI with peer-reviewed rigor across our health economics and outcomes research consulting and real-world evidence and claims analytics practices, so you get results—without sacrificing rigor. Health Economics & Outcomes Research at AI Speed is not a slogan; it is how we run every engagement.

What AI Does in Our HEOR Workflow

We leverage generative AI throughout our research and analytics workflow—not as a bolt-on chatbot, but as infrastructure woven through five stages of evidence generation.

Systematic Literature Review Acceleration

AI-assisted screening, extraction, and synthesis compress the most labor-intensive phase of evidence generation. We use large language models to triage abstracts, extract structured data elements, and draft evidence tables—then a senior researcher adjudicates every inclusion decision and verifies every extracted value against its source. The result is an SLR that moves in weeks, not quarters, with an audit trail a reviewer can actually challenge.

Claims Coding and Variable Construction

Healthcare claims research lives in coding systems—HCPCS, CPT, ICD-10, and NDC—and translating a protocol into complete, accurate code sets has traditionally consumed weeks of analyst time. Our AI-assisted approach rapidly codes healthcare claims and builds analyst-reviewable variable definitions in which every coding decision is visible rather than buried. This reduces dataset preparation time and ensures exceptional accuracy.

Cohort Building From Real-World Data

A study cohort is a chain of judgment calls: index date rules, continuous enrollment requirements, washout and baseline periods, inclusion and exclusion criteria, and outcome windows. AI lets us implement and stress-test those definitions against real-world databases—client-provided, licensed, or public-use commercial claims such as MarketScan and Optum, Medicare, Medicaid, EHR, and national surveys—far faster than manual programming, while our economists confirm that the cohort constructed in code matches the cohort described in the protocol.

Intuitive Model Interfaces

A cost-effectiveness or budget impact model is only useful if decision-makers can interrogate it. We use AI to develop intuitive model interfaces that let you vary assumptions, run scenarios, and see results update live—turning static spreadsheets into tools your market access and medical affairs teams will actually use.

Research Synthesis and Writing

AI significantly accelerates research synthesis and writing—first drafts of protocols, statistical analysis plans, manuscripts, abstracts, and slide decks. Every claim in every deliverable is then checked against the underlying analysis before it ships. Fluency is not credibility; we make sure you get both.

Expert-Verified AI for HEOR: How We Validate Every AI-Assisted Output

Here is the uncomfortable truth about AI in HEOR, and the reason a verification methodology matters more than any capability list: the production cost of plausible empirical research is collapsing, but the cost of verification is not. A chatbot will draft a protocol that reads well—that does not mean it is correct. A model will produce code that runs—that does not mean it implements the estimand. The risk is not that AI replaces good researchers; it is that AI lets mediocre research move faster and read more confidently than it has any right to.

In HEOR today, production is cheap and verification is not. Expert-Verified AI for HEOR makes verification the product.

Our answer is to treat verification as a first-class deliverable. On every engagement, the most valuable AI work is not generation—it is comparison. We systematically compare the protocol to the statistical analysis plan, the SAP to the code, the code to the tables, the tables to the manuscript's claims, and every cited claim to its cited source. Model assumptions are documented where a reader can find them. Coding decisions are traceable back to the protocol language that produced them. Another team could reproduce the work—which is the real test of whether evidence deserves trust.

We align our reporting with emerging HEOR AI standards, including the ELEVATE-GenAI reporting guidelines for large language model use in health economics and outcomes research—an ISPOR Working Group report spanning transparency, accuracy, reproducibility, and related domains. When your evidence reaches a payer, an HTA body, or a journal, the AI's role is disclosed, documented, and defensible.

Typically 3× Faster—Timelines You Can Plan Around

Speed claims are cheap; timelines are commitments. Our AI-enhanced approach typically delivers results 3× faster than traditional consultancies:

• A focused economic model in 4-6 weeks instead of 12-18 weeks.
• Comprehensive real-world evidence generation in 3-4 months rather than 9-12 months.

The same efficiencies that compress timelines also compress budgets, so you get superior value while maintaining the highest quality standards. And because we are a boutique firm, you get senior-level attention on every deliverable and a response within one business day—not a project manager relaying questions to a bench you never meet.

The Research Behind the Practice

We publish what we practice. Dr. Roebuck has written a four-part series tracking AI's trajectory in the field: AI in HEOR: Reflections on Early Innovations on the first wave of machine learning in outcomes research, AI in HEOR: The Current Landscape surveying where generative AI is actually delivering today, AI in HEOR: The Road Ahead to 2030 on where the field is heading, and AI in HEOR: Toward an AI-Native Research Cycle making the case that verification—not generation—is the field's defining challenge.

This is not commentary from the sidelines—and it did not begin with the current generative-AI cycle. Dr. Roebuck's AI and predictive-analytics record includes predictive modeling at CVS Caremark, a Medical Care study on predicting total healthcare costs from pharmacy claims data, and peer-reviewed AI clinical-decision-support research developed with IBM Watson Health and oncology collaborators—including "Accuracy of an Artificial Intelligence System for Cancer Clinical Trial Eligibility: Retrospective Pilot Study" in JMIR Medical Informatics and AI studies on clinical trial eligibility screening and complex breast cancer treatment decisions in JCO Clinical Cancer Informatics. Head-to-head accuracy assessment is exactly what we believe every AI-assisted workflow owes its stakeholders.

Senior Expertise Behind Every Algorithm

AI does not supply judgment; experience does. Dr. M. Christopher Roebuck, PhD, MBA, President & CEO of RxEconomics, brings 28+ years of experience in health economics and prior service as Director of Strategic Research at CVS Caremark—a career spent doing by hand the analytical work he now verifies when AI accelerates it. His 120+ publications & research reports are verifiable through his Google Scholar and ORCID profiles. When AI accelerates your project, he is the one verifying the output.