From Knowledge Transfer to Innovation Spreading: The Use of Networks in Long-Term Care (original) (raw)
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Implementation Science, 2010
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Implementation Science, 2011
Background Increasing the effective targeting and promotion of self-care support for long-term conditions requires more of a focus on patient contexts and networks. The aim of this paper is to describe how within a programme of research and implementation, social networks are viewed as being centrally involved in the mobilisation and deployment of resources in the management of a chronic condition. This forms the basis of a novel approach to understanding, designing, and implementing new forms of self-management support. Methods Drawing on evidence syntheses about social networks and capital and the role of information in self-management, we build on four conceptual approaches to inform the design of our research on the implementation of self-care support for people with long-term conditions. Our approach takes into consideration the form and content of social networks, notions of chronic illness work, normalisation process theory (NPT), and the whole systems informing self-management engagement (WISE) approach to self-care support. Discussion The translation and implementation of a self-care agenda in contemporary health and social context needs to acknowledge and incorporate the resources and networks operating in patients' domestic and social environments and everyday lives. The latter compliments the focus on healthcare settings for developing and delivering self-care support by viewing communities and networks, as well as people suffering from long-term conditions, as a key means of support for managing long-term conditions. By focusing on patient work and social-network provision, our aim is to open up a second frontier in implementation research, to translate knowledge into better chronic illness management, and to shift the emphasis towards support that takes place outside formal health services.
Recognizing Communication Patterns in Chronic Care Innovation Networks
SSRN Electronic Journal, 2014
This paper describes a methodology to measure innovation in teams. We collected data on team members' interaction by mining email communication archives over two years. We applied the methodology to study twelve innovation teams working to redesign the delivery of care to chronically ill children. We used social network analysis and sentiment analysis to calculate network metrics such as group density, group betweenness centrality, actor betweenness centrality, average weighted variance in contribution index, and emotionality ratio. Performance was measured using a consensus rating from project senior leaders on seven dimensions of performance. We administered surveys to assess team leaders' perception of teams' performance and conducted learning sessions with teams to share insights gained from analysis of email interactions
Innovation networks in healthcare as mean of knowledge transfer
Purpose -The present work focuses on the study of healthcare networks as key tool for innovation creation and spreading, by deploying the emergent paradigm of open innovation. During the early development of a technology, when the interactions among component types are unclear (in a state of flux) and, therefore, difficult to codify and freeze, organizations build connections with research centres and universities to explore alternative technological solutions. Once such interactions are better understood, codified, modularized and shared, then more exploitative networks (e.g. with suppliers and customers) may be better suited to exploit the current technology.
Social Networks in Improvement of Health Care
Materia Socio Medica, 2012
Social network is a social structure made of individuals or organizations associated with one or more types of interdependence (friendship, common interests, work, knowledge, prestige, etc.) which are the "nodes" of the network. Networks can be organized to exchange information, knowledge or financial assistance under the various interest groups in universities, workplaces and associations of citizens. Today the most popular and widely used networks are based on application of the Internet as the main ICT. Depending on the method of connection, their field of activity and expertise of those who participate in certain networks, the network can be classified into the following groups: a) Social Networks with personal physical connectivity (the citizens' associations, transplant networks, etc.), b) Global social internet network (Facebook, Twitter, Skype), c) specific health internet social network (forums, Health Care Forums, Healthcare Industry Forum), d) The health community internet network of non professionals (DailyStrength, CaringBridge, CarePages, MyFamilyHealth), e) Scientific social internet network (BiomedExperts, ResearchGate, iMedExchange), f) Social internet network which supported professionals (HealthBoards, Spas and Hope Association of Disabled and diabetic Enurgi), g) Scientific medical internet network databases in the system of scientific and technical information (CC, Pubmed/Medline, Excerpta Medica/EMBASE, ISI Web Knowledge, EBSCO, Index Copernicus, Social Science Index, etc.). The information in the network are exchanged in real time and in a way that has until recently been impossible in real life of people in the community. Networks allow tens of thousands of specific groups of people performing a series of social, professional and educational activities in the place of living and housing, place of work or other locations
International Journal of Environmental Research and Public Health, 2018
Health systems research recognizes the complexity of healthcare, and the interacting and interdependent nature of components of a health system. To better understand such systems, innovative methods are required to depict and analyze their structures. This paper describes social network analysis as a methodology to depict, diagnose, and evaluate health systems and networks therein. Social network analysis is a set of techniques to map, measure, and analyze social relationships between people, teams, and organizations. Through use of a case study exploring support relationships among senior managers in a newly established hospital group, this paper illustrates some of the commonly used network-and node-level metrics in social network analysis, and demonstrates the value of these maps and metrics to understand systems. Network analysis offers a valuable approach to health systems and services researchers as it offers a means to depict activity relevant to network questions of interest, to identify opinion leaders, influencers, clusters in the network, and those individuals serving as bridgers across clusters. The strengths and limitations inherent in the method are discussed, and the applications of social network analysis in health services research are explored.
Measuring Social Network Structure of Clinical Teams Caring for Patients with Complex Conditions
2010
Chronic, complex health conditions require teams that communicate effectively to be able to achieve best outcomes. In this study, we assessed the communication structure using social network analysis (SNA) for three distinct multidivisional teams caring for populations of children who have undergone liver transplantation, have been diagnosed with cerebral palsy or have been diagnosed with intestinal rehabilitation. In most cases, team members were geographically dispersed at the hospital campus, thereby requiring team members to depend on email for much of their communication. We used the Condor Social Network Analysis tool (Condor) to analyze the email communication patterns of team members. In our longitudinal analysis, we measured ties, betweenness centrality, contribution index, and density using social network metrics.
A Social Network Framework to Explore Healthcare Collaboration
Emerging Issues and Trends, 2015
A patient-centric approach to healthcare leads to an informal social network among medical professionals. This chapter presents a research framework to: (1) identify the collaboration structure among physicians that is effective and efficient for patients; (2) discover effective structural attributes of a collaboration network that evolves during the course of providing care; and (3) explore the impact of socio-demographic characteristics of healthcare professionals, patients, and hospitals on collaboration structures, from the point of view of measurable outcomes such as cost and quality of care. The framework uses illustrative examples drawn from a data set of patients undergoing hip replacement surgery. The practical application of the proposed framework reveals structures of physicians' collaborations that are not favourable to cost and quality of care measures such as readmission rate. The authors believe that such a framework will enable healthcare managers and administrators to evaluate the collaborative work environment within their respective healthcare organisations.