VARIABLE POPULATION MOPSO APPLIED TO MEDICAL VISITS

563 Objective: This study identified factors associated with use of the emergency department for medical reasons among individuals with serious mental illness. Methods: A total of 200 randomly selected outpatients who were receiving community-based psychiatric care were recruited for the study and interviewed with items from the National Health Interview Survey and other national health surveys. Results: Emergency department use (37 percent of the sample) was negatively associated with older age and positively associated with the number of co-occurring medical conditions, smoking, recent injury, and recent change in health care provider. Conclusions: The relatively high rate of emergency department use may be suggestive of inappropriate use or may reflect perceived barriers to care. Future work should identify specific reasons for seeking care in the emergency department and develop interventions to optimize appropriate emergency department use. (Psychiatric Services 57: 563–566, 2006)

Fuzzy systems (FSs) have been extensively applied to automatic control, pattern recognition, and decision analysis. However, a common bottleneck is encountered in the derivation of fuzzy rules, which is often difficult, time consuming, and relies on expert knowledge. To automate the design of FSs, many metaheuristic learning algorithms have been proposed. One major optimization category uses Swarm Intelligence (SI) model (Kennedy et al., 2001). The SI technique studies collective behavior in decentralized systems. Its development was based on mimicking the social behavior of animals or insects in an effort to find the optima in the problem space. SI models are initialized with a population of random solutions. One well-known SI model is particle swarm optimization (PSO) (Kennedy & Eberhart, 1995). Many modified PSO models have been proposed and successfully applied to different optimization problems (Clerc & Kennedy, 2002; Bergh & Engelbrecht, 2004; Ratnaweera et al., 2004; Juang, 2004; Kennedy & Mendes, 2006; Parrott & Li, 2006; Chen & Li, 2007). FS design using PSO has also been proposed in several studies (Juang, 2004; Chatterjee et al., 2005; Juang et al., 2007; Araujo & Coelho, 2008; Sharma et al., 2009). Another well-known SI is ant colony optimization (ACO) (Dorigo & Stutzle, 2004). The ACO technique is inspired by real ant colony observations. It is a multi-agent approach that was originally proposed to solve difficult discrete combinatorial optimization problems, such as the traveling salesman problem (TSP) (Dorigo et al., 1996; Dorigo & Gambardella, 1997). In the original ACO meta-heuristic, artificial ant colonies cooperate to find good solutions for difficult discrete optimization problems. Different ACO models have been applied to FS design problems (Cassillas et al., 2000; Cassillas et al., 2005; Mucientes & Casillas; 2007; Juang & Lo, 2007; Juang et al., 2008; Juang & Lu; 2009). In (Cassillas et al.,2000; Mucientes & Casillas; 2007; Juang et al., 2008; Juang & Lu; 2009), the FS input space was partitioned in grid type with antecedent part parameters of an FS manually assigned in advance. In (Juang & Lo, 2007), the FS input space was flexibly partitioned using a fuzzy clustering-like algorithm in order to reduce the total number of rules. For all of these studies, the consequent part parameters were optimized in discrete space using ACO. Since only the consequent part parameters are optimized, and the optimization space is restricted to be discrete, the designed FSs are unsuitable for problems where high accuracy is a major concern. Source: Fuzzy Systems, Book edited by: Ahmad Taher Azar, ISBN 978-953-7619-92-3, pp. 216, February 2010, INTECH, Croatia, downloaded from SCIYO.COM

Introduction: Managing organizational culture has been increasingly viewed as a lever for health care improvement. The aim of this study was to investigate the correlation between the type of organizational culture and patient satisfaction in the selected health care centers in the Federation of Bosnia and Herzegovina (FBIH).
 Methods: We conducted a cross-sectional survey in two municipal primary health care (PHC) centers in the FBIH, referred as Primary health care (PHC) center A and Primary health care (PHC) center B. A validated questionnaire, known as the Organizational Culture Assessment Instrument (OCAI), was used for the assessment of organizational culture. The questionnaire was distributed among the family health care teams at the two PHCs. Simultaneously, we carried out a survey about patient satisfaction among patients during their visits to the family health care teams.
 Results: We observed the differences in the type of the organizational culture between the health care centers. The hierarchical culture was found the dominant culture in PHC center A, whereas the market culture was the dominant culture in PHC center B. Also, the statistical significance (t test) was recorded in the overall patient satisfaction in the health care center with the dominated hierarchical culture followed by the clan culture (PHC center A).
 Conclusions: Considering the lack of similar surveys in Bosnia and Herzegovina, we believe that this study might be a good starting point for education of human resource managers in health care.

Background/Introduction:The Minimum Data Set (MDS) has allowed governments of disaster-affected countries to collect, examine, and evaluate standardized medical data from Emergency Medical Teams in real-time. However, little study has been conducted on the use of MDS data to predict health care needs.Objectives:This research proposes an outlook on the use of machine learning and MDS data to predict the need for medical care in disaster-affected areas.Method/Description:The characteristics of the data collected by MDS and the optimal machine learning model were discussed.Results/Outcomes:The primary causes of disease after disasters are trauma (MDS Nos. 4–8), which frequently occurs immediately after a disaster, and infectious diseases (MDS Nos. 9–18), which can increase due to decreasing hygiene conditions. Furthermore, certain infectious diseases can spread quickly because of living in congested evacuation centers, and early detection is crucial.Therefore, predicting the need for medical care in a disaster area is complicated and requires a combination of many machine-learning models. Data-driven methods are mostly linear approaches and cannot capture the dynamics of infectious disease transmission. Additionally, statistical models depend heavily on assumptions, making real-time infection prediction challenging. Thus, deep learning is employed to model without losing the temporal component.Conclusion:Real-time prediction of health care needs using machine learning and MDS can be useful to policymakers by enabling them to better deploy and allocate health care resources, which is useful to patients and front-line health care providers. More detailed predictions for regions and diseases are also anticipated.

Italy has a universal health care system that covers, in principle, the whole resident population, irrespective of citizenship and legal status. This study calculates the prevalence of unmet need for medical care among Italian citizens, regular and irregular immigrants and estimates logistic regression models to assess whether differences by citizenship and legal status hold true once adjusting for potential confounders. The analysis is based on two Surveys on Income and Living Conditions of Italian households and households with foreigners. Controlling for various factors, the odds of experiencing unmet need for medical care are 27% higher for regular immigrants than for Italian citizens and 59% higher for irregular immigrants. The gaps by citizenship and legal status are even more striking among those with chronic illnesses. These results reveal the high vulnerability of immigrants in Italy and the need to develop more effective policies to achieve health care access for all residents.

Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate their activities using decentralized control and self-organization. In particular, the discipline focuses on the behavior of social insects such as fish schools and bird flocks and colonies of ants, termites, bees, and wasps. The most well-known examples of systems studied by swarm intelligence are particle swarm optimization (PSO) and ant colony optimization (ACO). Particle swarm optimization mimics the behavior of fish schooling and bird flocking. PSO is a population-based stochastic optimization strategy with fast convergent speed than general evolutionary algorithms (EAs). Different from EAs, each particle employs not only position information but also the velocity information. They communicate good positions to each other and adjust their own positions according to their decision. In PSO, a number of simple entities—the particles—are placed in the search space of some problem or function, and each evaluates the objective function at its current location. Each particle then determines its movement through the search space by combining some aspect of the history of its own current and best (best-fitness) locations with those of one or more members of the swarm, with some random perturbations. The next iteration takes place after all particles have been moved. Eventually, the swarm as a whole, like a flock of birds collectively foraging for food, moves close to an optimum of the fitness function. Ant colony optimization is a probabilistic technique for solving computational hard problems which can be reduced to finding optimal paths. The main idea is the indirect communication between the ants by means of chemical pheromone trails. The special issue on the swarm intelligence is dedicated to the latest work in the theory and applications in this exciting area. Our aim is to provide a useful reference for understanding new trends on swarm intelligence. After a detailed review process, a total of four papers were selected to reflect the call thematic vision. The contents of these studies are briefly described as follows. The first paper entitled ‘Analysis of emergent symmetry breaking in collective decision making’ by Heiko Hamann, Thomas Schmickl, Heinz Worn, and Karl Crailsheim investigates a simulated multi-agent system (MAS) that collectively decides to aggregate at an area of high utility. The agents’ control algorithm is based on random agent– agent encounters and is inspired by the aggregation behavior of honey bees. In this article, symmetry breaking is defined, several symmetry-breaking measures are proposed, and phenomenon of emergent symmetry breaking within authors’ observed system is reported. The ability of the MAS to successfully break the symmetry depends significantly on a local-neighborhood-based threshold of the agents’ control algorithm that determines at which number of neighbors the agents stop. This dependency is analyzed, and two macroscopic features are determined that significantly influence the symmetry-breaking behavior. In addition, the connection between the ability of the Z. Cui (&) Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, 030024 Shanxi, Taiyuan, China e-mail: cuizhihua@gmail.com

The article deals with the impact of the COVID-19 pandemic on the financing of the health care system, and the main challenges to the stability of the financial mechanisms of post-pandemic health care development have been identified. The author substantiates the peculiarities of the crisis of health care financing in the conditions of the current pandemic, further economic recession and decreased fiscal sustainability. The global practice of fiscal response to the manifestations of the COVID-19 pandemic has been systematized and the volumes of the corresponding financing in the countries with insurance and budgetary systems of health care financing have been estimated. The article identifies mechanisms for the transformation and expansion of the fiscal space in the context of expanded financing of the growing need for medical care in the face of new epidemic risks in different countries. Most often, the expansion of a country's fiscal space is carried out by: redistributing the existing amount of government expenditure for health care and redirecting funding flows from financing certain types of medical care to financing programs to overcome and combat COVID-19; changes in the priority of government health expenditure to combat COVID-19 compared to other budget expenditures on the social sphere and economic development; and using national reserve funds and emergency funds. It has been found that in the field of health care, the vast majority of countries have reduced the economic and territorial deprivation of all population groups in access to the diagnosis and treatment of COVID-19. The author emphasizes the weaknesses of insurance based and decentralized health financing mechanisms to respond to the growing need for health care and financial stability during the pandemic. Substantiated the necessity to expand the fiscal space needed to cover the fiscal gap in Ukraine caused by the requirement to increase health care financing in response

From #MeToo to #TimesUp in health care: can a culture of accountability end inequity and harassment?

The goal of the National Center for Medical Education Development and Research Center (NCMEDR) is to support the education and training of medical students in the care of vulnerable populations. Access to primary care services in the US is fundamental to the health and wellness of all people regardless of their socioeconomic status. LGBQ+ persons, (lesbian, gay, bisexual, transgender, queer, and other sexual and gender minority), Persons Experiencing Homelessness (PEH), and Migrant Farm Workers (MFW) are among the most underserved, marginalized, and socially vulnerable groups in the US. NCMEDR in the Department of Family and Community Medicine at Meharry Medical College was established in part, with funding from the Department of Health and Human Services (DHHS) and the Health Resources and Services Administration (HRSA). NCMEDR was developed to provide educational pathways for transforming medical education and clinical practice in the US by ascertaining whether medical students were being trained to provide primary care, and behavioral health services to LGBTQ+ persons, PEH, and MFW. Here we focus on the impact of the COVID-19 pandemic on these specific populations because they represent marginalized groups that have been heavily impacted by the pandemic, have poor social determinants of health (SDOH), and are more likely to be uninsured, and are less likely to engage primary care providers outside of emergency room care. In this study, a scoping literature review was conducted to assess the impact of COVID-19 on primary care of LQBTQ+ persons, PEH, and MFW. The pandemic provided a serious health disparities gap for the defined vulnerable populations under review by the NCMEDR. The pandemic identified the need for transformative measures for clinical practices, medical education, and health care policies required for implementation to improve health care for vulnerable groups. We make recommendations for interventions with defined populations that may influence clinical, environmental health, and SDOH in the COVID era. The COVID pandemic directed the need for medical schools, health care and social organizations to intervene in new and different ways in vulnerable and marginalized communities. The recommendations provide a model for advancing health equity, access, quality, utilization, care coordination, and treatment.

Swarm-based intelligence is a recently developed area of computational intelligence that offers a powerful framework for solving complex optimization problems. It has found applications in various scientific fields where optimization of one or more functions is required. There are several methods developed under the umbrella of swarm intelligence, and particle swarm optimization (PSO) is one of them. This chapter presents swarm intelligence and its applications in industrial engineering as well as nuclear power plants and PSO is used to illustrate the potential of such an implementation. The roadmap of the chapter is as follows: Sec. 9.1 provides an introduction to swarm intelligence and the following section presents particle swarm optimization. A discussion on the implementation of PSO in industrial engineering problems is given in Sec. 9.3 while PSO applications in nuclear power plants (NPP) are presented in Sec. 9.4. Section 9.5 concludes the chapter.

Despite much coverage in the popular press, only anecdotal evidence is available on medical tourists. At first sight, they seemed confined to small and narrowly defined consumer segments: individuals seeking bargains in cosmetic surgery or uninsured and financially distressed individuals in desperate need of medical care. The study reported in this article is the first empirical investigation of the medical tourism consumer market. It provides the demographic profile, motivations, and value perceptions of health care consumers who traveled abroad specifically to receive medical care. The findings suggest a much broader market of educated and savvy health care consumers than previously thought. In the backdrop of the health care reform, the article concludes with implications for health care providers.

Nature is a wonderful source of inspiration for building models and techniques for solving difficult problems in design, optimisation, and control. More specifically, the study of evolution, the human immune system, and the collective behaviour of insects/birds have guided the origin of evolutionary algorithms, artificial immune systems, and optimisation techniques based on swarm intelligence, respectively. In this chapter, we present the use of particle swarm optimisation (PSO) and the Taguchi method for the identification of optimised fuzzy models from the available data. PSO is a member of the broad category of swarm intelligence (SI) techniques based on the metaphor of social interaction. It has been used for finding promising solutions in complex search spaces through the interaction of particles in a swarm, and is especially useful when dealingwith a high number of dimensions and situations where problem-specific information is not available. However, caution needs to be exercised in selecting PSO, as the performance of PSO largely depends on their values. In this chapter, a systematic reasoning approach based on the Taguchi method is also presented to quickly identify PSO parameters. The Taguchi method is a robust design approach that helps in optimisation, and which requires relatively few experiments. Although we focus here on the use of PSO and the Taguchi method for fuzzy model identification, these techniques have much broader use and application. In order to validate our approach, data from the rapid Nickel-Cadmium (Ni-Cd) battery charger developed by the authors were used. The results are based on real data and illustrate the viability and efficiency of the approach.Request access from your librarian to read this chapter's full text.

A discussion of trends in consumer health care communications carries with it the risk of being instantly out of date. This is a field that is moving so quickly, in which change is so dynamic, that anything said one month may well be more historical than current in a very short period of time. The reason for this is that our health care system itself is undergoing continued significant change, both technologically as well as financially; consumers' interest in their own health continues to intensify, especially as the population ages; the expectations of the public of advances in health care continue to be high; the technology by which messages can be delivered to consumers continues to become more sophisticated; and the competition among pharmaceutical and other health care companies is such that every advantage, including communications advantages, will be sought. I am going to focus on the provision to consumers of information about prescription drugs, especially on the role that pharmaceutical companies have played in consumer communications and the prospects for expanded efforts. Some initial historical perspective may be appropriate. It was not too long ago that consumer interest in health care generally, and prescription drugs specifically, was far less intense than it is today. When I was growing up, my family had a doctor-Dr. Priedman-who took care of our medical needs. When we were sick, Dr. Priedman came to our home and treated us with medicines that he pulled out of a black bag. Except for drugs like penicillin, no one really knew the names of the drugs we were receiving or what side effects we might encounter. If I wanted to know anything about a drug, I would hesitate to ask Dr. Friedman, figuring Wayne L. Pines is Executive Vice President of Burson-Marsteller, 1850 M Street, NW, Washington, DC 20036.

BackgroundThe gig economy (characterized by short-term contracts rather than being a full-time employee in an organization) is one of the most recent and important tendencies that have expanded through the global economic market thanks to advances in internet and communication technologies. Similarly, mobile health (mHealth) technologies have also evolved rapidly with the development of the internet and mobile apps, attracting attention globally for their health care benefits.ObjectiveThis study aimed to propose an integration of mHealth within the framework of the gig economy that leads to a new dimension of health care services and the proposal of a new term: gig mHealth.MethodsA review and systematic search of articles, books, and opinions that allowed for answering the research questions were executed through the internet. In this sense, the concept of the gig economy and examples, advantages and disadvantages, were reviewed. Similarly, the general characteristics of mHealth technologies were revised. In addition, the role of technology in supporting the development of the gig economy and mHealth technologies and the interactions between them were investigated.ResultsThe findings suggested that the gig economy is characterized by its flexibility in working hours, on-demand work, free agents, freelancing, freedom in the choice of work, and independent contracts. In addition, an analysis of an mHealth system indicated that it was composed of patients, specialists, nurses, and database administrators. In this system, patients and specialists or nurses are connected to cloud services for the transmission of data and medical information through a mobile app. Here, the administrators update the database and app features, among other technical tasks. Conversely, a general structure of an integrated gig mHealth system was developed. In this structure, the mHealth care services and the mHealth care activities were incorporated into a gig economy model. In addition, a practical example of an integrated view of a gig economy app in mHealth that illustrates the interaction between the patients (consumers) and providers (partners) of mHealth care services, mHealth care activities, health care professionals, and individual contractors was presented. The consumers and providers were interconnected with the health care company, brand, or firm through digital means using a mobile app or Windows platforms.ConclusionsThe analysis carried out in this study suggested the possibility of integrating mHealth within the framework of the gig economy enhancing health care service delivery and the management of health care activities. The following 4 major areas of apps proposed in the mHealth framework that can catalyze the operations using the features of the gig economy were sharing/renting medical and diagnostic equipment and resources, on-demand appointments/self-health management, on-demand health care services, and assigning health care activities/gigs to individual contractors. This integration leads to a new dimension for health care services and the proposal of a new term: gig mHealth.

By having a comprehensive look at the existent hospitals and health care centers of the country, it can be understood that some basic principles were not considered in a large number of the mentioned health care centers. These neglected principles involve appropriate site selection and hospital accessibility. Therefore, undesirable patient service occurs that consequently leads to change the architecture of the hospitals. If a few facilities are utilized or if they are not located well this can lead to increase in mortality (death) and morbidity (disease). Therefore, this study aims on presenting a mathematical programming model to prepare the fundamentals of establishing an efficient health care network by selecting the optimal sites for building new health cares among some predefined candidate sites. The purpose of the proposed model is the maximum possible covering of the patient population, which has not been covered by the previous health care centers. Obviously this problem clarifies the necessity of establishing new health cares. The proposed model is considered and discussed in monopoly conditions. Subsequently, for validating the proposed model, a set of data related to 45 health care centers located in southern Tehran (i.e., Eslamshahr and Rey) are used and the obtained results are provided in the final section.