A construção imaginativa de cuidados: a experiência de profissionais de enfermagem em um serviço de assistência remota

Indexación: Web of Science; ScieloThe direction of care delivery goes from the action to the being; a process built from professional experience, which gains special characteristics when the service is delivered by telephone. The goal of this research was to understand the interaction between professionals and users in a remote care service; to do so, a research is presented, using Grounded Theory and Symbolic Interactionism as theoretical references. Data were collected through eight interviews with professionals who deliver care by telephone. The theoretical understanding permitted the creation of the theoretical model of the Imaginative Construction of Care, which shows the interaction processes the professional experiences when delivering care by telephone. In this model, individual and social facts are added, showing the link between the concepts, with special emphasis on uncertainty, sensitivity and professional responsibility, as essential components of this experience.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0104-11692012000400009&nrm=isohttp://ref.scielo.org/44chq

Social encounter networks : characterizing Great Britain

A major goal of infectious disease epidemiology is to understand and predict the spread of infections within human populations, with the intention of better informing decisions regarding control and intervention. However, the development of fully mechanistic models of transmission requires a quantitative understanding of social interactions and collective properties of social networks. We performed a cross-sectional study of the social contacts on given days for more than 5000 respondents in England, Scotland and Wales, through postal and online survey methods. The survey was designed to elicit detailed and previously unreported measures of the immediate social network of participants relevant to infection spread. Here, we describe individual-level contact patterns, focusing on the range of heterogeneity observed and discuss the correlations between contact patterns and other socio-demographic factors. We find that the distribution of the number of contacts approximates a power-law distribution, but postulate that total contact time (which has a shorter-tailed distribution) is more epidemiologically relevant. We observe that children, public-sector and healthcare workers have the highest number of total contact hours and are therefore most likely to catch and transmit infectious disease. Our study also quantifies the transitive connections made between an individual's contacts (or clustering); this is a key structural characteristic of social networks with important implications for disease transmission and control efficacy. Respondents' networks exhibit high levels of clustering, which varies across social settings and increases with duration, frequency of contact and distance from home. Finally, we discuss the implications of these findings for the transmission and control of pathogens spread through close contact

Model-based machine learning to identify clinical relevance in a high-resolution simulation of sepsis and trauma

Introduction: Sepsis is a devastating, costly, and complicated disease. It represents the summation of varied host immune responses in a clinical and physiological diagnosis. Despite extensive research, there is no current mediator-directed therapy, nor a biomarker panel able to categorize disease severity or reliably predict outcome. Although still distant from direct clinical translation, dynamic computational and mathematical models of acute systemic inflammation and sepsis are being developed. Although computationally intensive to run and calibrate, agent-based models (ABMs) are one type of model well suited for this. New analytical methods to efficiently extract knowledge from ABMs are needed. Specifically, machine-learning techniques are a promising option to augment the model development process such that parameterization and calibration are performed intelligently and efficiently. Methods: We used the Keras framework to train an Artificial Neural Network (ANN) for the purpose of identifying critical biological tipping points at which an in silico patient would heal naturally or require intervention in the Innate Immune Response Agent-Based Model (IIRABM). This ANN, determines simulated patient “survival” from cytokine state based on their overall resilience and the pathogenicity of any active infections experienced by the patient, defined by microbial invasiveness, toxigenesis, and environmental toxicity. These tipping points were gathered from previously generated datasets of simulated sweeps of the 4 IIRABM initializing parameters. Results: Using mean squared error as our loss function, we report an accuracy of greater than 85% with inclusion of 20% of the training set. This accuracy was independently validated on withheld runs. We note that there is some amount of error that is inherent to this process as the determination of the tipping points is a computation which converges monotonically to the true value as a function of the number of stochastic replicates used to determine the point. Conclusion: Our method of regression of these critical points represents an alternative to traditional parameter-sweeping or sensitivity analysis techniques. Essentially, the ANN computes the boundaries of the clinically relevant space as a function of the model’s parameterization, eliminating the need for a brute-force exploration of model parameter space. In doing so, we demonstrate the successful development of this ANN which will allows for an efficient exploration of model parameter space

Social networks : the future for health care delivery

With the rapid growth of online social networking for health, health care systems are experiencing an inescapable increase in complexity. This is not necessarily a drawback; self-organising, adaptive networks could become central to future health care delivery. This paper considers whether social networks composed of patients and their social circles can compete with, or complement, professional networks in assembling health-related information of value for improving health and health care. Using the framework of analysis of a two-sided network – patients and providers – with multiple platforms for interaction, we argue that the structure and dynamics of such a network has implications for future health care. Patients are using social networking to access and contribute health information. Among those living with chronic illness and disability and engaging with social networks, there is considerable expertise in assessing, combining and exploiting information. Social networking is providing a new landscape for patients to assemble health information, relatively free from the constraints of traditional health care. However, health information from social networks currently complements traditional sources rather than substituting for them. Networking among health care provider organisations is enabling greater exploitation of health information for health care planning. The platforms of interaction are also changing. Patient-doctor encounters are now more permeable to influence from social networks and professional networks. Diffuse and temporary platforms of interaction enable discourse between patients and professionals, and include platforms controlled by patients. We argue that social networking has the potential to change patterns of health inequalities and access to health care, alter the stability of health care provision and lead to a reformulation of the role of health professionals. Further research is needed to understand how network structure combined with its dynamics will affect the flow of information and potentially the allocation of health care resources

Images of coordination : how implementing organizations perceive coordination arrangements

A crucial challenge for the coordination of horizontal policy programs those designed to tackle crosscutting issues is how to motivate government organizations to contribute to such programs. Hence, it is crucial to study how practitioners in implementing organizations view and appreciate the coordination of such programs. Assisted by Q-methodology, this inductive study reveals three significantly different "images" centralframe setting, networking via boundary spanners, and coordination beyond window dressing Most surprisingly, different images show up among respondents within the same organizations and horizontal programs. The authors find that the images reflect elements of the literature: the resistance to hierarchical central control, the need for local differentiation and increased incentives, and a collaboration-oriented culture. Most importantly, practitioners of implementing organizations perceive top-dawn mechanisms as ineffective to achieve coordination and ask for adaptive arrangements, involvement, and deliberative processes when designing coordination arrangements and during the collaboration

The Emergent Logic of Health Law

The American health care system is on a glide path toward ruin. Health spending has become the fiscal equivalent of global warming, and the number of uninsured Americans is approaching fifty million. Can law help to divert our country from this path? There are reasons for deep skepticism. Law governs the provision and financing of medical care in fragmented and incoherent fashion. Commentators from diverse perspectives bemoan this chaos, casting it as an obstacle to change. I contend in this Article that pessimism about health law’s prospects is unjustified, but that a new understanding of health law’s disarray is urgently needed to guide reform. My core proposition is that the law of health care provision is best understood as an emergent system. Its contradictions and dysfunctions cannot be repaired by some master design. No one actor has a grand overview—or the power to impose a unifying vision. Countless market players, public planners, and legal and regulatory decisionmakers interact in oft-chaotic ways, clashing with, reinforcing, and adjusting to each other. Out of these interactions, a larger scheme emerges—one that incorporates the health sphere’s competing interests and values. Change in this system, for worse and for better, arises from the interplay between its myriad actors. By quitting the quest for a single, master design, we can better focus our efforts on possibilities for legal and policy change. We can and should continuously survey the landscape of stakeholders and expectations with an eye toward potential launching points for evolutionary processes—processes that leverage current institutions and incentives. What we cannot do is plan or predict these evolutionary pathways in precise detail; the complexity of interactions among market and government actors precludes fine-grained foresight of this sort. But we can determine the general direction of needed change, identify seemingly intractable obstacles, and envision ways to diminish or finesse them over time. Dysfunctional legal doctrines, interest group expectations, consumers’ anxieties, and embedded institutional and cultural barriers can all be dealt with in this way, in iterative fashion. This Article sets out a strategy for doing so. To illustrate this strategy, I suggest emergent approaches to the most urgent challenges in health care policy and law—the crises of access, value, and cost