OBJECTIVES: Introduction of a compulsory economic evaluation policy for pharmaceutical reimbursement was enacted in Korea in 2000, but has yet to be fully implemented. Concerns about the quality and availability of population specific clinical data, treatment patterns, health care prices and subsequent cost-effectiveness data have been raised. We evaluated the quality of Korean economic evaluation studies of pharmaceuticals to understand gaps. From this, we propose policy options that might strengthen the research infrastructure in order to support such studies. METHODS: We reviewed 21 published studies of drugs conducted between 1996 and 2004. We utilized a published algorithm to assess quality. Evaluation criteria included methodological characteristics (perspective, selection of comparators, and modeling framework), health care system characteristics (relevance and applicability of clinical, treatment pattern and cost data), population characteristics (generalizability), and implications. RESULTS: Large variation in study quality was observed, particularly with outcome data and treatment patterns. We found that data on the effectiveness of drugs was typically extracted from clinical trials that did not include an Asian population, used inappropriate comparators and involved practice patterns that were not consistent with standards of care in Korea. With respect to treatment patterns, the most frequent situation relied on expert opinion from academic physicians in specialty practice. The Korean National Health Insurance Claims Database was a good source of disease specific costs, but was rarely used. Furthermore, the database failed to capture non-covered services. Preference measures, when used, were not elicited from the Korean population. Most studies (80%) did not clarify the funding source. CONCLUSIONS: If the Korean economic evaluation policy is to provide meaningful data for decision makers, the quality of cost-effectiveness studies will need to improve dramatically. This may involve access to or creation of better data, more diverse funding, improved training of researchers and evaluators, and partnerships with technology manufacturers.