Categoria: 2014

Abstract – This paper proposes the use of Bayesian networks tosupport the decision-making process in health systems governance. In particular, this paper presents LARIISA_Bay, a new component based on Bayesian networks that works together with LARIISA, acontext-aware platform to support applications in public health systems. The main goal of the proposed component is to assist teamsof health specialists in order to better diagnose diseases through data collected from users of LARIISA. As a case study, we focus on scenarios of dengue fever disease. We classify dengue cases into oneof the following levels: emergency (i.e., dengue hemorrhagic fever),grave (i.e., dengue fever) or normal (i.e., absence of the disease). Based on this classification, teams of health specialists can accurately make decisions, for example, to alert a health care agent to visit locations with a high incidence of the disease, to send an ambulance when an dengue emergency case has occurred, as well as give technical instructions on how to deal with specific cases. We present a prototype of LARIISA_Bay and the corresponding interfaces to support the interactions of the patient, the health careagent and the specialist with the system.

Many intrinsically related determinants of health and disease exist, including social and economic status, education, employment, housing, and physical and environmental exposures. These factors interact to cumulatively affect health and disease burden of individuals and populations, and to establish health inequities and disparities across and within countries. Biomedical models of health care decrease adverse consequences of disease, but are not enough to effectively improve individual and population health and advance health equity. Social determinants of health are especially important in Latin American countries, which are characterised by adverse colonial legacies, tremendous social injustice, huge socioeconomic disparities, and wide health inequities.

Abstract: LARIISA is a framework that makes use of context-aware to support decision-making and governancein the public health area. This paper presents the CLARIISA, an evolution of LARIISA towards an intelligent classifier model for enhancing the decision making process. Two applications are shown to illustratethe upgrade on the LARIISA system. The first one uses the Bayesian networks on a dengue case. The second uses ontology to manage home care scenarios. In both cases, the contributions related to our framework include patient health diagnosis provided remotely, support for decision making health systems, and context information for context-aware health systems.

Abstract: Introduction:
The Brazilian National Health System May Reduce Inequalities In Access To Health Services Through Strategies That Can Reach Those Most In Need With no Access To Care Services.

Objective:
To Identify Factors Associated With The Use Of Health Service By Children Aged 5 To 9 Years In The City Of Sobral, Ceará, Northeastern Brazil.

Results:
Only 558 (17.0%) Children Used Health Care Services In The 30 Days Preceding This Survey. Children With Any Health Condition (or = 3.90) Who Were Frequent Attenders Of Primary Care Strategy Of Organization (the Family Health Strategy, Fhs) (or = 1.81) And Living In The City’s Urban Area (or = 1.51) Were More Likely To Use Health Services. Almost 80% Of Children Used Fhs as Their Referral Care Service. Children From Poorer Families And With Easier Access To Services Were More Likely To Be Fhs Users.

Conclusion:
The Study Showed That Access To Health Services Has Been Relatively Equitable Through The Fhs, a Point Of Entry To The Local Health System.

Social Determinants Of Health; Health Service/utilization; Health Services Accessibility; Equity; Family Health; Child Health Services