Desirée Moraes Zouain

Markov Models in health care

Markov Chains provide support for problems involving decision on uncertainties through a continuous period of time. The greater availability and access to processing power through computers allow that these models can be used more often to represent clinical structures. Markov models consider the patients in a discrete state of health, and the events represent the transition from one state to another. The possibility of modeling repetitive events and time dependence of probabilities and utilities associated permits a more accurate representation of the evaluated clinical structure. These templates can be used for economic evaluation in health care taking into account the evaluation of costs and clinical outcomes, especially for evaluation of chronic diseases. This article provides a review of the use of modeling within the clinical context and the advantages of the possibility of including time for this type of study.

Technology Roadmapping for Renewable Fuels: Case of Biobutanol in Brazil

Technology roadmapping has been used to define long term strategies and research agenda for the development of biofuels. The present work proposes the use of technology roadmapping as a technique for long term strategic planning of the biobutanol development, aligning long term goals with the resources, funding, and priorities to fulfill the needs in the development process in Brazil. Interviews have been used in the roadmapping process as an alternative to the workshops on market, product and technology. It allows the participation of companies in a highly competitive environment. The use of interviews allows the information to be collected individually, contributing to the elaboration of a roadmap. The results show that sugar cane has the potential to be used as a feedstock in the biobutanol production process, enabling Brazil to become a key exporter to supply other countries. For the short future, biobutanol has the potential to be produced in Brazil to replace petro-butanol as a solvent in industrial applications and to build the export platform for the fuels market .

Factor analysis for the adoption of nuclear technology in diagnosis and treatment of chronic diseases

OBJECTIVE To identify and evaluate latent variables (variables that are not directly observed) for adopting and using nuclear technologies in diagnosis and treatment of chronic diseases. The measurement and management of these latent factors are important for healthcare due to complexities of the sector. METHODS An exploratory factor analysis study was conducted among 52 physicians practicing in the areas of Cardiology, Neurology and Oncology in the State of Sao Paulo who agreed to participate in the study between 2009 and 2010. Data were collected using an attitude measurement questionnaire, and analyzed according to the principal component method with Varimax rotation. RESULTS The component matrix after factor rotation showed three elucidative groups arranged according to demand for nuclear technology: clinical factors, structural factors, and technological factors. Clinical factors included questionnaire answers referring to medical history, previous interventions, complexity and chronicity of the disease. Structural factors included patient age, physicians practice area, and payment ability. Technological factors included prospective growth in the use of nuclear technology and availability of services. CONCLUSIONS The clinical factors group dimension identified in the study included patient history, prior interventions, and complexity and chronicity of the disease. This dimension is the main motivator for adopting nuclear technology in diagnosis and treatment of chronic diseases.