Understanding the implementation of evidence-based care: A structural network approach

Implementation Science, Feb 2011

Background Recent study of complex networks has yielded many new insights into phenomenon such as social networks, the internet, and sexually transmitted infections. The purpose of this analysis is to examine the properties of a network created by the 'co-care' of patients within one region of the Veterans Health Affairs. Methods Data were obtained for all outpatient visits from 1 October 2006 to 30 September 2008 within one large Veterans Integrated Service Network. Types of physician within each clinic were nodes connected by shared patients, with a weighted link representing the number of shared patients between each connected pair. Network metrics calculated included edge weights, node degree, node strength, node coreness, and node betweenness. Log-log plots were used to examine the distribution of these metrics. Sizes of k-core networks were also computed under multiple conditions of node removal. Results There were 4,310,465 encounters by 266,710 shared patients between 722 provider types (nodes) across 41 stations or clinics resulting in 34,390 edges. The number of other nodes to which primary care provider nodes have a connection (172.7) is 42% greater than that of general surgeons and two and one-half times as high as cardiology. The log-log plot of the edge weight distribution appears to be linear in nature, revealing a 'scale-free' characteristic of the network, while the distributions of node degree and node strength are less so. The analysis of the k-core network sizes under increasing removal of primary care nodes shows that about 10 most connected primary care nodes play a critical role in keeping the k-core networks connected, because their removal disintegrates the highest k-core network. Conclusions Delivery of healthcare in a large healthcare system such as that of the US Department of Veterans Affairs (VA) can be represented as a complex network. This network consists of highly connected provider nodes that serve as 'hubs' within the network, and demonstrates some 'scale-free' properties. By using currently available tools to explore its topology, we can explore how the underlying connectivity of such a system affects the behavior of providers, and perhaps leverage that understanding to improve quality and outcomes of care.

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Understanding the implementation of evidence-based care: A structural network approach

Implementation Science Understanding the implementation of evidence- based care: A structural network approach Michael L Parchman 0 Caterina M Scoglio Phillip Schumm 0 Family & Community Medicine Department , 7703 Floyd Curl Drive , University of Texas Health Science Center , San Antonio, Texas, 78229-3884 , USA Background: Recent study of complex networks has yielded many new insights into phenomenon such as social networks, the internet, and sexually transmitted infections. The purpose of this analysis is to examine the properties of a network created by the 'co-care' of patients within one region of the Veterans Health Affairs. Methods: Data were obtained for all outpatient visits from 1 October 2006 to 30 September 2008 within one large Veterans Integrated Service Network. Types of physician within each clinic were nodes connected by shared patients, with a weighted link representing the number of shared patients between each connected pair. Network metrics calculated included edge weights, node degree, node strength, node coreness, and node betweenness. Log-log plots were used to examine the distribution of these metrics. Sizes of k-core networks were also computed under multiple conditions of node removal. Results: There were 4,310,465 encounters by 266,710 shared patients between 722 provider types (nodes) across 41 stations or clinics resulting in 34,390 edges. The number of other nodes to which primary care provider nodes have a connection (172.7) is 42% greater than that of general surgeons and two and one-half times as high as cardiology. The log-log plot of the edge weight distribution appears to be linear in nature, revealing a 'scale-free' characteristic of the network, while the distributions of node degree and node strength are less so. The analysis of the k-core network sizes under increasing removal of primary care nodes shows that about 10 most connected primary care nodes play a critical role in keeping the k-core networks connected, because their removal disintegrates the highest k-core network. Conclusions: Delivery of healthcare in a large healthcare system such as that of the US Department of Veterans Affairs (VA) can be represented as a complex network. This network consists of highly connected provider nodes that serve as 'hubs' within the network, and demonstrates some 'scale-free' properties. By using currently available tools to explore its topology, we can explore how the underlying connectivity of such a system affects the behavior of providers, and perhaps leverage that understanding to improve quality and outcomes of care. - Background Efforts to date to understand the slowness of physicians to implement evidence-based guidelines has been hindered by an overreliance on the attributes, knowledge, decision making, and actions of individual clinicians and an under-recognition of the network of care within which they operate [1-5]. For example, in efforts to understand adoption of guidelines, research to date has largely focused on individual attributes of the providers using theories such as the theory of planned behavior [6]. However, little is known about adoption of guidelines from the perspective of the network of providers within which a single provider is embedded. One of the earliest examinations of diffusion of information and behaviors between physicians is the landmark study of physician prescribing behavior by Coleman, Katz and Mentzel in the mid-1950s [7]. They found that the properties of relationships formed by physicians in a network predict the adoption of a new medication. The adoption occurs first between community physicians who have contact with opinion leaders, and then between physicians who are social friends. However, re-analysis of the data raised questions about the findings and how the opinions and behaviors of other physicians affect those with whom they interact [8]. Physicians may also influence each other as they observe and compare the care provided to their patients by other physicians, even if they have no direct communication with the other physician. As noted by Mittman and colleagues, healthcare professionals work within peer groups who share common values, assumptions, and beliefs, and individual behavior can be strongly influenced by these factors [3]. Patients often return to their physician after contact with another physician with a new diagnostic workup, or taking a new medication the primary physician may not be familiar or comfortable with. For example, Keating and colleagues documented that physicians obtain information from other physicians who they consider to have more expertise in the knowledge area [9]. The sharing of care between two physicians creates a link or a connection. Physicians who share the care of many patients have stronger linkages than with physicians whom they share the care of few patients. Physicians are also connected with many other physicians through these linkages, all of which when taken into cons (...truncated)


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Michael L Parchman, Caterina M Scoglio, Phillip Schumm. Understanding the implementation of evidence-based care: A structural network approach, Implementation Science, 2011, pp. 14, 6, DOI: 10.1186/1748-5908-6-14