Extending External Agent Capabilities in Healthcare Social Networks

A social health care system, such as palliative care, can be viewed as a social network of interacting patients and care providers. Each patient in the network has a set of capabilities to perform his or her intended daily tasks. However, some patients may not have the required capabilities to carry out their desired tasks. Consequently, different groups of care providers - consist of doctors, volunteers, nurses, etc.- offer the patients support by providing them with a variety of needed services. Assuming there are a cost and resource limitations for providing care within the system, where each care provider can support a limited number of patients, the problem is to find a set of suitable care providers to match the needs of the maximum number of patients. In this dissertation, we propose a novel agent-based model to address this problem by extending the agent\u27s capabilities using the benefit of the social network. Our assumption is that each agent, or patient, can cover its disabilities and perform its desired tasks through collaboration with other agents, or care providers, in the network. The goal of this work is to improve the quality of services in the network at both individual and system levels. On the one hand, an individual patient wants to maximize the quality of his/her life, while at the system level we want to achieve quality care for as many patients as possible with minimum cost. The performance and functionality of this proposed model have been evaluated based on various synthetic networks. The results demonstrate a significant reduction in the operational costs and enhancement of the service quality

Polypharmacy and potentially inappropriate medication use in geriatric oncology.

Polypharmacy is a highly prevalent problem in older persons, and is challenging to assess and improve due to variations in definitions of the problem and the heterogeneous methods of medication review and reduction. The purpose of this review is to summarize evidence regarding the prevalence and impact of polypharmacy in geriatric oncology patients and to provide recommendations for assessment and management. Polypharmacy has somewhat variably been incorporated into geriatric assessment studies in geriatric oncology, and polypharmacy has not been consistently evaluated as a predictor of negative outcomes in patients with cancer. Once screened, interventions for polypharmacy are even more uncertain. There is a great need to create standardized interventions to improve polypharmacy in geriatrics, and particularly in geriatric oncology. The process of deprescribing is aimed at reducing medications for which real or potential harm outweighs benefit, and there are numerous methods to determine which medications are candidates for deprescribing. However, deprescribing approaches have not been evaluated in older patients with cancer. Ultimately, methods to identify polypharmacy will need to be clearly defined and validated, and interventions to improve medication use will need to be based on clearly defined and standardized methods

The associations of palliative care experts regarding food refusal : a cross-sectional study with an open question evaluated by triangulation analysis

Introduction: Health professionals in oncologic and palliative care settings are often faced with the problem that patients stop eating and drinking. While the causes of food refusal are very different, the result is often malnutrition, which is linked to health comorbidities and a high mortality rate. However, the professionals lack the time and knowledge to clarify the cause for each patient. What associations do health professionals have when faced with food refusal? Objective: To investigate the associations that health professionals in oncological and palliative settings have about denied eating behavior. Methods: A cross-sectional study, starting with an open question focusing professionals’ associations regarding food refusal. The results were inductively analyzed, whereby generic categories were developed. Subsequently, the categories were transformed into quantitative data to calculate the relationships between the categories. Results: A total of 350 out of 2000 participants completed the survey, resulting in a response rate of 17.5%. Food refusal is primarily associated with physical and ethical aspects and with endof-life. Half of the participants frequently find that patients refuse to eat. The attitudes show that the autonomy of the patient is the highest good and is to be respected. Even in the case of patients with limited decision-making capacity, the refusal to eat is acceptable. Conclusion: Clarifying the cause of food refusal requires a great deal of knowledge and is strongly influenced by the associations of health professionals. While the associations have very negative connotations, information and training is needed to make professionals aware of this and to change their associations. With this knowledge and in an interprofessional cooperation, mis-labelling of patient settings can be avoided and fears can be reduce

Addressing the Health Needs of an Aging America: New Opportunities for Evidence-Based Policy Solutions

This report systematically maps research findings to policy proposals intended to improve the health of the elderly. The study identified promising evidence-based policies, like those supporting prevention and care coordination, as well as areas where the research evidence is strong but policy activity is low, such as patient self-management and palliative care. Future work of the Stern Center will focus on these topics as well as long-term care financing, the health care workforce, and the role of family caregivers

The doctor and the blue form: learning professional responsibility

Book synopsis: This book presents leading-edge perspectives and methodologies to address emerging issues of concern for professional learning in contemporary society. The conditions for professional practice and learning are changing dramatically in the wake of globalization, new modes of knowledge production, new regulatory regimes, and increased economic-political pressures. In the wake of this, a number of challenges for learning emerge: more practitioners become involved in interprofessional collaboration developments in new technologies and virtual workworlds emergence of transnational knowledge cultures and interrelated circuits of knowledge. The space and time relations in which professional practice and learning are embedded are becoming more complex, as are the epistemic underpinnings of professional work. Together these shifts bring about intersections of professional knowledge and responsibilities that call for new conceptions of professional knowing. Exploring what the authors call sociomaterial perspectives on professional learning they argue that theories that trace not just the social but also the material aspects of practice – such as tools, technologies, texts but also bodies and actions - are useful for coming to terms with the challenges described above. Reconceptualising Professional Learning develops these issues through specific contemporary cases focused on one of the book’s three main themes: (1) professionals’ knowing in practice, (2) professionals’ work arrangements and technologies, or (3) professional responsibility. Each chapter draws upon innovative theory to highlight the sociomaterial webs through which professional learning may be reconceptualised. Authors are based in Australia, Canada, Italy, Norway, Sweden, and the USA as well as the UK and their cases are based in a range of professional settings including medicine, teaching, nursing, engineering, social services, the creative industries, and more. By presenting detailed accounts of these themes from a sociomaterial perspective, the book opens new questions and methodological approaches. These can help make more visible what is often invisible in today’s messy dynamics of professional learning, and point to new ways of configuring educational support and policy for professionals

Investigation of Team Formation in Dynamic Social Networks

Team Formation Problem (TFP) in Social Networks (SN) is to collect the group of individuals who match the requirements of given tasks under some constraints. It has several applications, including academic collaborations, healthcare, and human resource management. These types of problems are highly challenging because each individual has his or her own demands and objectives that might conflict with team objectives. The major contribution of this dissertation is to model a computational framework to discover teams of experts in various applications and predict the potential for collaboration in the future from a given SN. Inspired by an evolutionary search technique using a higher-order cultural evolution, a framework is proposed using Knowledge-Based Cultural Algorithms to identify teams from co-authorship and industrial settings. This model reduces the search domain while guiding the search direction by extracting situational knowledge and updating it in each evolution. Motivated from the above results, this research examines the palliative care multidisciplinary networks to identify and measure the performance of the optimal team of care providers in a highly dynamic and unbalanced SN of volunteer, community, and professional caregivers. Thereafter, a visualization framework is designed to explore and monitor the evolution in the structure of the care networks. It helps to identify isolated patients, imbalanced resource allocation, and uneven service distribution in the network. This contribution is recognized by Hospice and the Windsor Essex Compassion Care Community in partnership with the Faculty of Nursing. In each setting, several cost functions are attempted to measure the performance of the teams. To support this study, the temporal nature of two important evaluation metrics is analyzed in Dynamic Social Networks (DSN): dynamic communication cost and dynamic expertise level. Afterward, a novel generic framework for TFP is designed by incorporating essential cost functions, including the above dynamic cost functions. The Multi-Objective Cultural Algorithms (MOCA) is used for this purpose. In each generation, it keeps track of the best solutions and enhances exploration by driving mutation direction towards unexplored areas. The experimental results reach closest to the exact algorithm and outperform well-known searching methods. Subsequently, this research focuses on predicting suitable members for the teams in the future, which is typically a real-time application of Link Prediction. Learning temporal behavior of each vertex in a given DSN can be used to decide the future connections of the individual with the teams. A probability function is introduced based on the activeness of the individual. To quantify the activeness score, this study examines each vertex as to how actively it interacts with new and existing vertices in DSN. It incorporates two more objective functions: the weighted shortest distance and the weighted common neighbor index. Because it is technically a classification problem, deep learning methods have been observed as the most effective solution. The model is trained and tested with Multilayer Perceptron. The AUC achieves above 93%. Besides this, analyzing common neighbors with any two vertices, which are expected to connect, have a high impact on predicting the links. A new method is introduced that extracts subgraph of common neighbors and examines features of each vertex in the subgraph to predict the future links. The sequence of subgraphs\u27 adjacency matrices of DSN can be ordered temporally and treated as a video. It is tested with Convolutional Neural Networks and Long Short Term Memory Networks for the prediction. The obtained results are compared against heuristic and state-of-the-art methods, where the results reach above 96% of AUC. In conclusion, the knowledge-based evolutionary approach performs well in searching through SN and recommending effective teams of experts to complete given tasks successfully in terms of time and accuracy. However, it does not support the prediction problem. Deep learning methods, however, perform well in predicting the future collaboration of the teams