Anticipated Impacts on Veterans Healthcare: Our ultimate goal is to improve the health of veterans by designing interventions at the clinical microsystem level that will facilitate the integration of KM-SD roles in primary care through improving relationships and learning. We anticipate that such a cross-cutting intervention has the potential to improve clinical performance as well as staff and patient satisfaction. If we are successful, this could be a breakthrough in the efficiency of VA organizational change. Project Background: The way in which we conceptualize the basic nature of the primary care clinic (PCC) greatly influences our beliefs and ideas about what should work to improve its operations. If we view it as a production line, then we design our improvement efforts using systems engineering techniques aimed at standardizing, reducing variation, and improving efficiency. If instead we view the PCC as an organic and complex social system, then we might design our improvement efforts using methods designed to embrace rather than eliminate surprise (which is inevitable in health care settings), to enhance the connections and communication between the individuals in the PCC rather than focusing on improving the skill sets of the individuals, and to use feedback and time for reflection as part of a strategy for the PCC to make sense of their own operations and behaviors in relation to their mission and goals. For the purpose of this proposal, this latter approach will be denoted by the term knowledge management techniques. We propose to study whether PCC performance correlates with the presence of higher levels of use of knowledge management techniques within PCCs. The VA has been in the forefront of implementing some knowledge management practices, such as sue of performance data systems and integrated health records, but has not yet paid sufficient attention to other such as relationship management, increasing diversity in decision making, active learning, and sense-making. Project Objectives: Describe the range of knowledge management techniques, including relationship management and active learning) currently employed by 15 VA and 5 VA contract PCCs. 2. Analyze the relationship between the nature and roles of relationships and learning in PCCs and their performance across multiple outcomes including clinical performance measures, worker satisfaction and turnover, and patient satisfaction. Our research propositions are: (1) High performing PCCs exhibit higher quality relationships and more active learning than low performing clinics; (2) The quality of relationship within PCCs will have the largest effects on worker and patient satisfaction. Research Propositions: 1. High performing VA and VA contract primary care clinics will exhibit higher quality relationships and more active learning than low performing clinics. 2.a. Variation in temporal change in both relationships and learning among the 20 clinics over the 30 months will be observed. 2.b. The temporal change in relationships (such as stable, improving or deteriorating between adjacent years) will be associated with a similar temporal change in patient and employee satisfaction.2 c. The temporal change in learning (such as stable, improving or deteriorating between adjacent years) will be associated with a similar temporal change in process of care quality indicators and with intermediate clinical outcomes. Project Methods: Descriptive, iterative study of 15 VA and 5 VA contract primary care clinics within VISN 17. Using ethnographic, qualitative and quantitative data collection methods, each clinic will be profiled on the nature of relationships between staff and between staff and patients and the nature of learning. Clinical outcomes (patient functional status, BP, cholesterol, HgA1c, preventable admissions), patient satisfaction, employee satisfaction and turnover will serve as outcomes. The association of these outcomes with the nature of relationships and learning will be explored qualitatively and quantitatively. Stability of these profiles will be evaluated in two subsequent waves of data collection.