OBJECTIVES: Real-world evidence (RWE) may provide good estimates of absolute event probabilities and costs in patients in actual clinical practice, but their use in decision-analytic models poses many challenges. A literature review based on a systematic search was conducted to summarize the limitations of using RWE in decision-analytic modeling reported in the literature but also to identify existing recommendations about real-world modeling. METHODS: A literature search was performed on Medline and Embase databases, as well as relevant websites. No restrictions in language or geographical scope were imposed. RESULTS: A total of 14 references were included. RWE is recognized as a valuable source of data for market access and reimbursement, and as a complement to clinical trial evidence for treatment pathways, resource use, long-term natural history, and effectiveness. The main limitations identified in the literature were: confounding bias, missing data, lack of accurate data related to drug exposure and outcomes, errors during the record-keeping process, protection of private data, and insufficient numbers of patients. Although most submission guidelines recognized the potential biases associated with RWE, guidance on the appropriate methods to deal with these biases, and approaches to review different relevant evidence to inform model development, were scarce. Several initiatives have attempted to provide guidance on the use of RWE in decision-modeling. CONCLUSIONS: RWE is likely to be particularly valuable for informing healthcare policy-makers when formulating appropriate treatment pathways, encouraging the optimal allocation of scarce resources, and improving aggregate patient outcomes. However, little guidance is available on the relative merits of using efficacy and/or effectiveness evidence in Health Technology Appraisal submissions. Further research is needed to better understand these methods and their potential applications in a broader range of scenarios and simulation studies, and their impact on economic modeling.