853 resultados para Health information


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Health Informatics is an intersection of information technology, several disciplines of medicine and health care. It sits at the common frontiers of health care services including patient centric, processes driven and procedural centric care. From the information technology perspective it can be viewed as computer application in medical and/or health processes for delivering better health care solutions. In spite of the exaggerated hype, this field is having a major impact in health care solutions, in particular health care deliveries, decision making, medical devices and allied health care industries. It also affords enormous research opportunities for new methodological development. Despite the obvious connections between Medical Informatics, Nursing Informatics and Health Informatics, most of the methodologies and approaches used in Health Informatics have so far originated from health system management, care aspects and medical diagnostic. This paper explores reasoning for domain knowledge analysis that would establish Health Informatics as a domain and recognised as an intellectual discipline in its own right.

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The objective of this review is to locate, critically appraise and synthesize evidence on the effectiveness of communication strategies for providing older people access to information regarding in-home health and social care services. The review question is: What is the effectiveness of communication interventions in providing older people with information about in-home health and social care services?

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Pilot studies are conducted to explain the baseline information literacy skills prevailing in the undergraduate and graduate nurses at the University of Queensland. The analyses reveal a significant difference between the skills of both the nurses, hence demonstrating the need for the development of new information literacy workshops. The author also presents various teaching strategies that can be adopted for an effective skill development of these nurses.

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With the ever increasing amount of eHealth data available from various eHealth systems and sources, Health Big Data Analytics promises enticing benefits such as enabling the discovery of new treatment options and improved decision making. However, concerns over the privacy of information have hindered the aggregation of this information. To address these concerns, we propose the use of Information Accountability protocols to provide patients with the ability to decide how and when their data can be shared and aggregated for use in big data research. In this paper, we discuss the issues surrounding Health Big Data Analytics and propose a consent-based model to address privacy concerns to aid in achieving the promised benefits of Big Data in eHealth.

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This research examined the implementation of clinical information system technology in a large Saudi Arabian health care organisation. The research was underpinned by symbolic interactionism and grounded theory methods informed data collection and analysis. Observations, a review of policy documents and 38 interviews with registered nurses produced in-depth data. Analysis generated three abstracted concepts that explained how imported technology increased practice and health care complexity rather than enhance quality patient care. The core category, Disseminating Change, also depicted a hierarchical and patriarchal culture that shaped the implementation process at the levels of government, organisation and the individual.

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Disease maps are effective tools for explaining and predicting patterns of disease outcomes across geographical space, identifying areas of potentially elevated risk, and formulating and validating aetiological hypotheses for a disease. Bayesian models have become a standard approach to disease mapping in recent decades. This article aims to provide a basic understanding of the key concepts involved in Bayesian disease mapping methods for areal data. It is anticipated that this will help in interpretation of published maps, and provide a useful starting point for anyone interested in running disease mapping methods for areal data. The article provides detailed motivation and descriptions on disease mapping methods by explaining the concepts, defining the technical terms, and illustrating the utility of disease mapping for epidemiological research by demonstrating various ways of visualising model outputs using a case study. The target audience includes spatial scientists in health and other fields, policy or decision makers, health geographers, spatial analysts, public health professionals, and epidemiologists.

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A two day workshop was convened on February 2-3, 1998 in Charleston, SC with 20 invited experts in various areas of sea turtle research. The goal of this workshop was to review current information on sea turtles with repect to health and identify data gaps. The use of a suite of health assessment indicators will provide insight on the health status of sea turtle populations. Since the relationship of health factors of sea turtles is limited, a seconde workshop was planned. Using a tiered approach, the first workshop we identified and reviewed the available, pertinent baseline information and data gaps. The second workshop will focus on developing the framework for the research plan. The workshops will address the use of integrated set of health parameters; specific objectives are: 1) Identify reliable indicators of health in sea turtles: assess advantages and disadvantages; determine new indicators/biomarkers which may be useful; 2) Review existing sea turtle field sampling projects; 3) Identify field projects suitable for inclusion for health assessment sampling; 4) Identify data gaps, particularly environmental characterization; 5) Identify new health assessment sampling sites, including reference site(s); and 6) Develop integrated five-year research plan, with focus on health assessment of environmental characterization. (PDF contains 174 pages)

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The aim of this research, which focused on the Irish adult population, was to generate information for policymakers by applying statistical analyses and current technologies to oral health administrative and survey databases. Objectives included identifying socio-demographic influences on oral health and utilisation of dental services, comparing epidemiologically-estimated dental treatment need with treatment provided, and investigating the potential of a dental administrative database to provide information on utilisation of services and the volume and types of treatment provided over time. Information was extracted from the claims databases for the Dental Treatment Benefit Scheme (DTBS) for employed adults and the Dental Treatment Services Scheme (DTSS) for less-well-off adults, the National Surveys of Adult Oral Health, and the 2007 Survey of Lifestyle Attitudes and Nutrition in Ireland. Factors associated with utilisation and retention of natural teeth were analysed using count data models and logistic regression. The chi-square test and the student’s t-test were used to compare epidemiologically-estimated need in a representative sample of adults with treatment provided. Differences were found in dental care utilisation and tooth retention by Socio-Economic Status. An analysis of the five-year utilisation behaviour of a 2003 cohort of DTBS dental attendees revealed that age and being female were positively associated with visiting annually and number of treatments. Number of adults using the DTBS increased, and mean number of treatments per patient decreased, between 1997 and 2008. As a percentage of overall treatments, restorations, dentures, and extractions decreased, while prophylaxis increased. Differences were found between epidemiologically-estimated treatment need and treatment provided for those using the DTBS and DTSS. This research confirms the utility of survey and administrative data to generate knowledge for policymakers. Public administrative databases have not been designed for research purposes, but they have the potential to provide a wealth of knowledge on treatments provided and utilisation patterns.