PURPOSE Clinicians often have an intuitive understanding of how their relationships with patients foster healing. Yet we know little empirically about the experience of healing and how it occurs between clinicians and patients. Our purpose was to create a model that identifi es how healing relationships are developed and maintained.METHODS Primary care clinicians were purposefully selected as exemplar healers. Patients were selected by these clinicians as having experienced healing relationships. In-depth interviews, designed to elicit stories of healing relationships, were conducted with patients and clinicians separately. A multidisciplinary team analyzed the interviews using an iterative process, leading to the development of case studies for each clinician-patient dyad. A comparative analysis across dyads was conducted to identify common components of healing relationships RESULTS Three key processes emerged as fostering healing relationships: (1) valuing/creating a nonjudgmental emotional bond; (2) appreciating power/consciously managing clinician power in ways that would most benefi t the patient; and (3) abiding/displaying a commitment to caring for patients over time. Three relational outcomes result from these processes: trust, hope, and a sense of being known. Clinician competencies that facilitate these processes are self-confi dence, emotional self-management, mindfulness, and knowledge.CONCLUSIONS Healing relationships have an underlying structure and lead to important patient-centered outcomes. This conceptual model of clinician-patient healing relationships may be generalizable to other kinds of healing relationships.

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PURPOSE Social network analysis (SNA) provides a way of quantitatively analyzing relationships among people or other information-processing agents. Using 2 practices as illustrations, we describe how SNA can be used to characterize and compare communication patterns in primary care practices.METHODS Based on data from ethnographic fi eld notes, we constructed matrices identifying how practice members interact when practice-level decisions are made. SNA software (UCINet and KrackPlot) calculates quantitative measures of network structure including density, centralization, hierarchy and clustering coeffi cient. The software also generates a visual representation of networks through network diagrams. RESULTSThe 2 examples show clear distinctions between practices for all the SNA measures. Potential uses of these measures for analysis of primary care practices are described.CONCLUSIONS SNA can be useful for quantitative analysis of interaction patterns that can distinguish differences among primary care practices. INTRODUCTIONP rimary care practices are complex systems that are characterized by dynamic patterns of interactivity among practice members and their environment. [1][2][3] One feature of complex systems is the property of emergence, which is the tendency of organized patterns to emerge that cannot be predicted from the properties of individual parts of the system. 4 Thus, to understand how primary care practices function, it is necessary to study not only the individuals within the practice or individual practice components but also the relationships among individuals.5 Study of such patterns and how they change with time or in response to interventions requires an ability to look at the entire complex web of relationships and interactions within a primary care practice. Although qualitative description 6-10 and practice genograms 11 have demonstrated utility for understanding the complex interactions in practices, a tool that captures quantitative aspects of the patterns of relationships within practices would be a useful aid in studies of primary care practices. Social network analysis (SNA) is such a tool.SNA 13 More recent work examines the association of these quantitative measures with organizational performance outcomes. Cummings and Cross, for example, found that degree of hierarchy, core-periphery structure, and structural holes of leaders correlated negatively with performance in 182 work groups in a large telecommunications company, 14 and Aydin et al found that increased network communication density was associated with higher use of an electronic medical record system by nurse practitioners and physician' s assistants. 15 There have also been studies showing how network parameters change with time. Shah, for example showed that network centrality decreased after downsizing in a consumer electronics fi rm, 16 whereas Burkhardt and Brass documented increased network centrality after introduction of a new computer system in a federal agency. 17In this article, using data from 2 primary ...

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BACKGROUND-Obesity is associated with increased colorectal cancer incidence and mortality. Previous studies using telephone survey data showed that obese women were less likely to receive colorectal cancer screening. It is unknown if this is true among patients in primary care practices.