PURPOSE: To identify and synthesize characteristics of successful data-driven Quality improvement learning collaboratives (QILCs) in the United States and Europe, and to extend previously discussed and newly identified guidelines for developing successful data-driven QILCs across health care settings and systems. METHODS: An interview guide of open-ended questions was developed and posed to 18 key informants of various disciplines involved in the development and implementation of successful QILCs across 10 organizations in 3 countries. Aspects of successful QILCs were analyzed to identify patterns emerging from structure-process interactions between complex health care systems. RESULTS: Shared patterns of successful collaboratives included cultivating trust, attendance to the human dimension, nonlinear development, attendance to organizational culture, integrated philosophy of quality improvement, and a focus on process and outcome measurement to drive change. This study extends the knowledge base through synthesis of findings from previous quality improvement research with the findings from this study to develop guidelines for establishing and developing successful QILCs. CONCLUSIONS: The core characteristics identified in this study were critical to successful collaboration when these approaches were used in the contexts identified. The intrinsic complexity of QILCs requires that effectiveness studies employ qualitative as well as quantitative methodologies.