OBJECTIVES: The “big data” era represents an exciting opportunity to utilize powerful new sources of information to reduce clinical and health economic uncertainty on an individual patient level. Health systems are increasingly utilizing big data analyses to guide personalized patient treatment pathways that lead to improved health outcomes and cost efficiencies. In turn, health economic outcomes research (HEOR) practices will need to evolve to accommodate individual patient-level HEOR analyses in place of current one-size-fits-all approaches based on population averages. METHODS: We propose the concept of “precision HEOR”: a big data-driven approach that synthesizes big data evidence within an HEOR framework and informs healthcare decision-making tailored to specific patient clusters and, eventually, individual patients. This approach currently relies on patient similarity analysis augmented by machine learning. To explore the concept of precision HEOR, we developed a guidance document for designing, conducting, and reporting future precision HEOR analyses. The intended audiences for this guidance may be academic, industry, and government HEOR researchers, and decision makers who evaluate clinical and economic evidence for formulary and insurance coverage policies. RESULTS: The guidance document addresses issues related to the transition from traditional to precision HEOR practices, the evaluation of big data evidence and its case-by-case appropriateness for precision HEOR analysis, decision model design with a focus on real-time decision-making support for individual patients, and the need for active model management as needed as new patient clusters, evidence, and interventions become available. CONCLUSIONS: Precision HEOR should make the promises of precision medicine more realizable by aiding and adapting healthcare resource allocation. The combined hopes for precision medicine and precision HEOR are that not only will individual patients receive the best possible medical care, but that overall healthcare costs will either remain manageable according to current standards or, ideally, become more cost-efficient.