Background: Those attempting to implement changes in health care settings often find that intervention efforts do not progress as expected. Unexpected outcomes are often attributed to variation and/or error in implementation processes. We argue that some unanticipated variation in intervention outcomes arises because unexpected conversations emerge during intervention attempts. The purpose of this paper is to discuss the role of conversation in shaping interventions and to explain why conversation is important in intervention efforts in health care organizations. We draw on literature from sociolinguistics and complex adaptive systems theory to create an interpretive framework and develop our theory. We use insights from a fourteen-year program of research, including both descriptive and intervention studies undertaken to understand and assist primary care practices in making sustainable changes. We enfold these literatures and these insights to articulate a common failure of overlooking the role of conversation in intervention success, and to develop a theoretical argument for the importance of paying attention to the role of conversation in health care interventions.

This study investigated how interaction with peers influenced the ways students managed uncertainty during collaborative problem solving in a 5th-grade class. The analysis focused on peer responses to individuals' attempts to manage uncertainty they experienced while engaged in collaborative efforts to design, build, and program robots and achieve assignment objectives. Patterns of peer response were established through discourse analysis of work sessions for 5 teams engaged in 2 collaborative projects. Three socially supportive peer responses and 2 unsupportive peer responses were identified. Peer interaction was influential because students relied on supportive social response to enact most of their uncertainty management strategies. This study provides a useful theoretical contribution to understanding the roles of peer interaction in collaborative problem solving. Conceptualizing collaborative problem solving as a process of negotiating uncertainties can help instructional designers shape tasks and relational contexts to facilitate learning.

Internal models of the environment h a v e an important role to play in adaptive systems in general and are of particular importance for the supervised learning paradigm. In this paper we demonstrate that certain classical problems associated with the notion of the teacher" in supervised learning can be solved by judicious use of learned internal models as components of the adaptive system. In particular, we show h o w supervised learning algorithms can be utilized in cases in which an unknown dynamical system intervenes between actions and desired outcomes. Our approach applies to any supervised learning algorithm that is capable of learning in multi-layer networks.*This paper is a revised version of MIT Center for Cognitive Science Occasional Paper 40. We wish to thank

Surprise can emanate from two sources: lack of sufficient information or knowledge and the basic dynamics of complex adaptive systems. The authors expand the traditional view of surprise with a complexity perspective that makes it possible to ask new questions and to consider new ways of understanding the world around us. They discuss creativity and learning as two strategies for capitalizing on the surprises that confront organizations.

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BackgroundEfforts to improve the care of patients with chronic disease in primary care settings have been mixed. Application of a complex adaptive systems framework suggests that this may be because implementation efforts often focus on education or decision support of individual providers, and not on the dynamic system as a whole. We believe that learning among clinic group members is a particularly important attribute of a primary care clinic that has not yet been well-studied in the health care literature, but may be related to the ability of primary care practices to improve the care they deliver.To better understand learning in primary care settings by developing a scale of learning in primary care clinics based on the literature related to learning across disciplines, and to examine the association between scale responses and chronic care model implementation as measured by the Assessment of Chronic Illness Care (ACIC) scale.MethodsDevelopment of a scale of learning in primary care setting and administration of the learning and ACIC scales to primary care clinic members as part of the baseline assessment in the ABC Intervention Study. All clinic clinicians and staff in forty small primary care clinics in South Texas participated in the survey.ResultsWe developed a twenty-two item learning scale, and identified a five-item subscale measuring the construct of reciprocal learning (Cronbach alpha 0.79). Reciprocal learning was significantly associated with ACIC total and sub-scale scores, even after adjustment for clustering effects.ConclusionsReciprocal learning appears to be an important attribute of learning in primary care clinics, and its presence relates to the degree of chronic care model implementation. Interventions to improve reciprocal learning among clinic members may lead to improved care of patients with chronic disease and may be relevant to improving overall clinic performance.

Most physicians in our sample were willing to discharge actual and hypothetical patients from their practices. This tendency may have significant implications for the initiation of pay-for-performance programs. Physicians should be educated about the importance of the patient-physician relationship and their fiduciary obligations to the patient.

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