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Racial-ethnic minorities receive lower quality and intensity of health care compared with whites across a wide range of preventive, diagnostic, and therapeutic services and disease entities. These disparities in health care contribute to continuing racial-ethnic disparities in the burden of illness and death. Several national medical organizations and the Institute of Medicine have issued position papers and recommendations for the elimination of health care disparities. However, physicians in practice are often at a loss for how to translate these principles and recommendations into specific interventions in their own clinical practices. This paper serves as a blueprint for translating principles for the elimination of racial-ethnic disparities in health care into specific actions that are relevant for individual clinical practices. We describe what is known about reducing racial-ethnic disparities in clinical practice and make recommendations for how clinician leaders can apply this evidence to transform their own practices. [ABSTRACT FROM AUTHOR]

Background: Patient complexity is often operationalized by counting multiple chronic conditions (MCC) without considering contextual factors that can affect patient risk for adverse outcomes.Objective: Our objective was to develop a conceptual model of complexity addressing gaps identified in a review of published conceptual models.Data Sources: We searched for English-language MEDLINE papers published between 1 January 2004 and 16 January 2014. Two reviewers independently evaluated abstracts and all authors contributed to the development of the conceptual model in an iterative process.Results: From 1606 identified abstracts, six conceptual models were selected. One additional model was identified through reference review. Each model had strengths, but several constructs were not fully considered: 1) contextual factors; 2) dynamics of complexity; 3) patients' preferences; 4) acute health shocks; and 5) resilience. Our Cycle of Complexity model illustrates relationships between acute shocks and medical events, healthcare access and utilization, workload and capacity, and patient preferences in the context of interpersonal, organizational, and community factors.Conclusions/implications: This model may inform studies on the etiology of and changes in complexity, the relationship between complexity and patient outcomes, and intervention development to improve modifiable elements of complex patients. [ABSTRACT FROM AUTHOR]