An Introduction to Health Economics and Outcomes Research (HEOR) [Video]

Health economics and outcomes research, or HEOR, is the practical work of using real world healthcare data to support outcomes and economic questions that sit behind pricing, access, and policy decisions. This short explainer breaks down what HEOR programming typically involves, why it matters when evidence needs to be transparent and reproducible, and how teams can work efficiently with large longitudinal datasets without overstating what those data can prove.

We walk through the common HEOR use cases sponsors bring to real world data, including tracking treatment patterns over time, comparing utilisation and costs between products, and checking whether a database contains a feasible population that matches planned inclusion and exclusion criteria for an upcoming trial. You’ll also hear how programmers translate decision questions into concrete cohorts, code based definitions, derived variables, and analysis ready datasets that can support outcomes research and economic evaluations.

Finally, we outline what good practice looks like when multiple stakeholders need confidence in the results. That includes clear cohort specifications and code lists, early discussion of data limitations like missing variables or incomplete follow up, independent quality control to catch logic errors, and documentation that supports review by payers and assessment bodies.

At Quanticate, our HEOR, biometrics, and data management teams help sponsors build scalable real world data workflows and decision focused analyses that are practical, traceable, and reproducible. Request a consultation today.