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Healthcare Technology: A Domain of Inequality  [PDF]
Suman Hazarika, Akhil Ranjan Dutta
Advances in Applied Sociology (AASoci) , 2013, DOI: 10.4236/aasoci.2013.32011
Abstract: The prevailing perception that technological development facilitates universal empowerment and transcends the social domains of discriminations is now challenged by comprehensive studies. Technology itself is a domain of inequality and it accentuates more inequality with technology gradually favoring the privileged sections and the societies. The healthcare technology, which otherwise could have brought miracle achievements in attaining universal health standards, however, has failed to do so due to the inherent inequality in access to healthcare technology. The growing dominance of monopoly houses on healthcare technology and marginalization of indigenous health technology makes access to technology with a new domain of inequality. With comprehensive empirical data, the present paper investigates into this domain of inequality and argues that a move towards global well being demands a radical restructuring of the global domain of healthcare technology. 
Collaborative systems and multiagent systems  [PDF]
Alin Munteanu,Cristina Ofelia Sofran
Computer Science , 2009,
Abstract: This paper presents some basic elements regarding the domain of the collaborative systems, a domain of maximum actuality and also the multiagent systems, developed as a result of a sound study on the one-agent systems.
Privacy Preserving Risk Mitigation Approach for Healthcare Domain  [PDF]
Shaden S. Al Aqeeli, Mznah A. Al-Rodhaan, Yuan Tian, Abdullah M. Al-Dhelaan
E-Health Telecommunication Systems and Networks (ETSN) , 2018, DOI: 10.4236/etsn.2018.71001
Abstract: In the healthcare domain, protecting the electronic health record (EHR) is crucial for preserving the privacy of the patient. To help protect the sensitive data, access control mechanisms can be utilized to restrict access to only legitimate users. However, an issue arises when the authorized users abuse their access privileges and violate privacy preferences of the patients. While traditional access control schemes fall short of defending against the misbehavior of authorized users, risk-aware access control models can provide adaptable access to the system resources based on assessing the risk of an access request. When an access request is deemed risky, but within acceptable thresholds, risk mitigation strategies can be exploited to minimize the risk calculated. This paper proposes a risk-aware, privacy-preserving risk mitigation approach that can be utilized in the healthcare domain. The risk mitigation approach controls the patient’s medical data that can be exposed to healthcare professionals, according to their trust level as well as the risk incurred of such data exposure, by developing a novel Risk Measure formula. The developed Risk Measure is proven to manage the risk effectively. Furthermore, Risk Mitigation Data Disclosure algorithms, RIMIDI0 and RIMIDI1, which utilize the developed risk measures, are proposed. Experimental results show the feasibility and effectiveness of the proposed method in preserving the privacy preferences of the patient. Since the proposed approach exposes the patient’s data that are relevant to the undergoing medical procedure while preserving the privacy preferences, positive outcomes can be realized, which will ultimately bring forth quality healthcare services.
Healthcare Policy and Cost Containment: Consultation with Healthcare Agencies in the Swiss Cantons  [PDF]
Anhorn P
Revue Médicale de l'Assurance Maladie , 2003,
Abstract: Switzerland is a federal state composed of 26 sovereign cantons. Each canton elaborates its own health policy and, in addition to applying federal regulations, implements its own cost-containment measures. Here we present the results of a survey performed in May 2002 by using a questionnaire sent to healthcare authorities in each canton. Accordingly, we were able to ascertain the cost-containment measures they implemented and assess the intensity, legitimacy and to a certain extent, the potential success of their cost-containment initiatives.
MULTIAGENT LEARNING WITHIN A COLLABORATIVE ENVIRONMENT  [PDF]
CRISTINA OFELIA STANCIU,ADRIAN COJOCARIU,LJUBICA KAZI
Anale : Seria ?tiin?e Economice. Timi?oara , 2012,
Abstract: Multiagent Learning is at the intersection of multiagent systems and Machine Learning, two subdomains of artificial intelligence. Traditional Machine Learning technologies usually imply a single agent that is trying to maximize some utility functions without having any knowledge about other agents within its environment. The multiagent systems domain refers to the domains where several agents are involved and mechanisms for the independent agents’ behaviors interaction have to be considered. Due to multiagent systems’ complexity, there have to be found solutions for using Machine Learning technologies to manage this complexity.
Estimating the cost of healthcare delivery in three hospitals in southern Ghana
AQQ Aboagye, ANK Degboe, AAD Obuobi
Ghana Medical Journal , 2010,
Abstract: Objective: The cost burden (called full cost) of providing health services at a referral, a district and a mission hospital in Ghana were determined. Methods: Standard cost-finding and cost analysis tools recommended by World Health Organization are used to analyse 2002 and 2003 hospital data. Full cost centre costs were computed by taking into account cash and non-cash expenses and allocating overhead costs to intermediate and final patient care centres. Findings: The full costs of running the mission hospital in 2002 and 2003 were US$600,295 and US$758,647 respectively; for the district hospital, the respective costs were US$496,240 and US$487,537; and for the referral hospital, the respective costs were US$1,160,535 and US$1,394,321. Of these, overhead costs ranged between 20% and 42%, while salaries made up between 45% and 60%. Based on healthcare utilization data, in 2003 the estimated cost per outpatient attendance was US$ 2.25 at the mission hospital, US$ 4.51 at the district hospital and US$8.5 at the referral hospital; inpatient day costs were US$ 6.05, US$ 9.95 and US$18.8 at the respective hospitals. User fees charged at service delivery points were generally below cost. However, some service delivery points have the potential to recover their costs. Conclusion: Salaries are the major cost component of the three hospitals. Overhead costs constitute an important part of hospital costs and must be noted in efforts to recover costs. Cost structures are different at different types of hospitals. Unit costs at service delivery points can be estimated and projected into the future.
Pharmaceutical cost control in primary care: opinion and contributions by healthcare professionals
Alexandra Prados-Torres, Amaia Calderón-Larra?aga, Antoni Sicras-Mainar, Sebastià March-Llull, Bárbara Oliván-Blázquez
BMC Health Services Research , 2009, DOI: 10.1186/1472-6963-9-209
Abstract: A qualitative exploratory study was carried out using 11 focus groups composed of GPs from the Spanish regions of Aragon, Catalonia and the Balearic Islands. A semi-structured guide was applied in obtaining the GPs' opinions. The transcripts of the dialogues were analyzed by two investigators who independently considered categorical and thematic content. The results were supervised by other members of the team, with overall responsibility assigned to the team leader.GPs are conscious of their public responsibility with respect to pharmaceutical cost, but highlight the need to spread responsibility for cost control among the different actors of the health system. They insist on implementing measures to improve the quality of prescriptions, avoiding mere quantitative evaluations of prescription costs. They also suggest moving towards the self-management of the pharmaceutical budget by each health centre itself, as a means to design personalized incentives to improve their outcomes. These proposals need to be considered by the health administration in order to pre-empt the feelings of injustice, impotence, frustration and lack of motivation that currently exist among GPs as a result of the implemented measures.Future investigations should be oriented toward strategies that involve GPs in the planning and management of drug cost control mechanisms. The proposals in this study may be considered by the health administration as a means to move toward the rational use of drugs while avoiding concerns about injustice and feelings of impotence on the part of the GPs, which can lead to lack of interest in and disaffection with the current measures.The economic impact of drug prescription is one of the principal concerns of health administrations. This is common to most European countries, where drug expenditures as a proportion of the Gross Domestic Product (GDP) and of total health expenditure has increased during the last 30 years, and is expected to continue rising [1]. In
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain  [PDF]
Zhengping Che,Sanjay Purushotham,Robinder Khemani,Yan Liu
Computer Science , 2015,
Abstract: Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research. Deep Learning models have shown superior performance for robust prediction in computational phenotyping tasks, but suffer from the issue of model interpretability which is crucial for clinicians involved in decision-making. In this paper, we introduce a novel knowledge-distillation approach called Interpretable Mimic Learning, to learn interpretable phenotype features for making robust prediction while mimicking the performance of deep learning models. Our framework uses Gradient Boosting Trees to learn interpretable features from deep learning models such as Stacked Denoising Autoencoder and Long Short-Term Memory. Exhaustive experiments on a real-world clinical time-series dataset show that our method obtains similar or better performance than the deep learning models, and it provides interpretable phenotypes for clinical decision making.
DESIGN OF A MULTIAGENT SYSTEM FOR SOLVING SEARCH PROBLEMS
LEON FLORIN
Journal of Engineering Studies and Research , 2010,
Abstract: Search problems are fundamental in artificial intelligence. When domain knowledge is limited or not available, search is the only way to solve a problem. Although search algorithms have been widely implemented using structured or object-oriented programming, the design of a multiagent system for solving search problems raises a different type of challenges. In this paper, the design of such a system is presented, along with some implementation details. The performance of the system is analysed for several problems using different algorithms and parameters.
Cognitive Medical Multiagent Systems  [cached]
Barna Iantovics
Brain. Broad Research in Artificial Intelligence and Neuroscience , 2010,
Abstract: The development of efficient and flexible agent-based medical diagnosis systems represents a recent research direction. Medical multiagent systems may improve the efficiency of traditionally developed medical computational systems, like the medical expert systems. In our previous researches, a novel cooperative medical diagnosis multiagent system called CMDS (Contract Net Based Medical Diagnosis System) was proposed. CMDS system can solve flexibly a large variety of medical diagnosis problems. This paper analyses the increased intelligence of the CMDS system, which motivates its use for different medical problem’s solving.
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