OBJECTIVES Retrospective chart review studies can result in robust naturalistic data to inform evaluations of treatment patterns, resource utilization, costs of care, clinical outcomes and safety. Data quality control is challenging both as a result of poor quality documentation in the usual care medical chart, or as a result of data abstraction and data entry processes. METHODS Ten chart review case studies conducted in the United States, Canada and Europe were evaluated to provide recommendations for improving chart review data quality control mechanisms. RESULTS All 10 studies used electronic data capture (EDC) systems. Common lessons learned across the studies were that the case report forms (CRFs) should only include necessary data points required to fulfill the analysis. Direct chart-to-EDC data entry and remote real-time data quality control is recommended to reduce additional transcription errors that may occur if using paper CRFs. It is important to ensure the EDC system includes a cohort-control platform that enables selection of patient cohorts (i.e., random selection) and tracking of eligibility screening to reduce selection bias risk. Automated edit checks of primary data endpoints should be programmed into the EDC system prompting data abstractors to revise erroneous data and/or confirm data outside of expected ranges at entry. To confirm abstracted data reflect source documents (patient medical charts), a second abstractor at the site can re-abstract pre-defined critical study variables from patient medical charts for cross-referencing for data discrepancies. Site training must be effective to ensure compliance with chart abstraction and data quality requirements. CONCLUSIONS Given the frequent incomplete or poor quality medical chart information and the potential for human error in data abstraction and entry processes, data quality control methods are paramount.  Approaches to protocol, CRF and study training materials design can positively impact data quality.