991 resultados para data reports


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Objective: To assess the recall of media reports about vitamin D and associated factors. Methods: Analysis of cross-sectional telephone interview data (2,001 Queensland adults, 18-70 years) on vitamin D and personal sun protection, recall of media reports and participant characteristics. Results: 83.7% of participants had heard of vitamin D, 47.5% through the media. Only 513 (25.6%) participants recalled the media content within four main themes: vitamin D is beneficial/comes from the sun (47.0%); some people aren’t getting enough vitamin D, need more sun (27.9%); need to balance sun exposure and skin protection (11.5%); or other (13.6%). Only 65 of the 950 participants (6.8%) reported a change to their behaviour(s) due to the media report. Conclusion: Although the media were the main source of information about vitamin D for almost 50% of participants, recall of the content and direct effect on behaviour was low. Only a small minority recalled a balanced media report of beneficial and harmful aspects of sun exposure. Implications Health professionals often supply media with background information. To achieve best public health practice for sun protection and vitamin D, information to foster balanced media reports should be provided.

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The health system is one sector dealing with a deluge of complex data. Many healthcare organisations struggle to utilise these volumes of health data effectively and efficiently. Also, there are many healthcare organisations, which still have stand-alone systems, not integrated for management of information and decision-making. This shows, there is a need for an effective system to capture, collate and distribute this health data. Therefore, implementing the data warehouse concept in healthcare is potentially one of the solutions to integrate health data. Data warehousing has been used to support business intelligence and decision-making in many other sectors such as the engineering, defence and retail sectors. The research problem that is going to be addressed is, "how can data warehousing assist the decision-making process in healthcare". To address this problem the researcher has narrowed an investigation focusing on a cardiac surgery unit. This research used the cardiac surgery unit at the Prince Charles Hospital (TPCH) as the case study. The cardiac surgery unit at TPCH uses a stand-alone database of patient clinical data, which supports clinical audit, service management and research functions. However, much of the time, the interaction between the cardiac surgery unit information system with other units is minimal. There is a limited and basic two-way interaction with other clinical and administrative databases at TPCH which support decision-making processes. The aims of this research are to investigate what decision-making issues are faced by the healthcare professionals with the current information systems and how decision-making might be improved within this healthcare setting by implementing an aligned data warehouse model or models. As a part of the research the researcher will propose and develop a suitable data warehouse prototype based on the cardiac surgery unit needs and integrating the Intensive Care Unit database, Clinical Costing unit database (Transition II) and Quality and Safety unit database [electronic discharge summary (e-DS)]. The goal is to improve the current decision-making processes. The main objectives of this research are to improve access to integrated clinical and financial data, providing potentially better information for decision-making for both improved from the questionnaire and by referring to the literature, the results indicate a centralised data warehouse model for the cardiac surgery unit at this stage. A centralised data warehouse model addresses current needs and can also be upgraded to an enterprise wide warehouse model or federated data warehouse model as discussed in the many consulted publications. The data warehouse prototype was able to be developed using SAS enterprise data integration studio 4.2 and the data was analysed using SAS enterprise edition 4.3. In the final stage, the data warehouse prototype was evaluated by collecting feedback from the end users. This was achieved by using output created from the data warehouse prototype as examples of the data desired and possible in a data warehouse environment. According to the feedback collected from the end users, implementation of a data warehouse was seen to be a useful tool to inform management options, provide a more complete representation of factors related to a decision scenario and potentially reduce information product development time. However, there are many constraints exist in this research. For example the technical issues such as data incompatibilities, integration of the cardiac surgery database and e-DS database servers and also, Queensland Health information restrictions (Queensland Health information related policies, patient data confidentiality and ethics requirements), limited availability of support from IT technical staff and time restrictions. These factors have influenced the process for the warehouse model development, necessitating an incremental approach. This highlights the presence of many practical barriers to data warehousing and integration at the clinical service level. Limitations included the use of a small convenience sample of survey respondents, and a single site case report study design. As mentioned previously, the proposed data warehouse is a prototype and was developed using only four database repositories. Despite this constraint, the research demonstrates that by implementing a data warehouse at the service level, decision-making is supported and data quality issues related to access and availability can be reduced, providing many benefits. Output reports produced from the data warehouse prototype demonstrated usefulness for the improvement of decision-making in the management of clinical services, and quality and safety monitoring for better clinical care. However, in the future, the centralised model selected can be upgraded to an enterprise wide architecture by integrating with additional hospital units’ databases.

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A rule-based approach for classifying previously identified medical concepts in the clinical free text into an assertion category is presented. There are six different categories of assertions for the task: Present, Absent, Possible, Conditional, Hypothetical and Not associated with the patient. The assertion classification algorithms were largely based on extending the popular NegEx and Context algorithms. In addition, a health based clinical terminology called SNOMED CT and other publicly available dictionaries were used to classify assertions, which did not fit the NegEx/Context model. The data for this task includes discharge summaries from Partners HealthCare and from Beth Israel Deaconess Medical Centre, as well as discharge summaries and progress notes from University of Pittsburgh Medical Centre. The set consists of 349 discharge reports, each with pairs of ground truth concept and assertion files for system development, and 477 reports for evaluation. The system’s performance on the evaluation data set was 0.83, 0.83 and 0.83 for recall, precision and F1-measure, respectively. Although the rule-based system shows promise, further improvements can be made by incorporating machine learning approaches.

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The National Road Safety Strategy 2011-2020 outlines plans to reduce the burden of road trauma via improvements and interventions relating to safe roads, safe speeds, safe vehicles, and safe people. It also highlights that a key aspect in achieving these goals is the availability of comprehensive data on the issue. The use of data is essential so that more in-depth epidemiologic studies of risk can be conducted as well as to allow effective evaluation of road safety interventions and programs. Before utilising data to evaluate the efficacy of prevention programs it is important for a systematic evaluation of the quality of underlying data sources to be undertaken to ensure any trends which are identified reflect true estimates rather than spurious data effects. However, there has been little scientific work specifically focused on establishing core data quality characteristics pertinent to the road safety field and limited work undertaken to develop methods for evaluating data sources according to these core characteristics. There are a variety of data sources in which traffic-related incidents and resulting injuries are recorded, which are collected for a variety of defined purposes. These include police reports, transport safety databases, emergency department data, hospital morbidity data and mortality data to name a few. However, as these data are collected for specific purposes, each of these data sources suffers from some limitations when seeking to gain a complete picture of the problem. Limitations of current data sources include: delays in data being available, lack of accurate and/or specific location information, and an underreporting of crashes involving particular road user groups such as cyclists. This paper proposes core data quality characteristics that could be used to systematically assess road crash data sources to provide a standardised approach for evaluating data quality in the road safety field. The potential for data linkage to qualitatively and quantitatively improve the quality and comprehensiveness of road crash data is also discussed.

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The skyrocketing trend for social media on the Internet greatly alters analytical Customer Relationship Management (CRM). Against this backdrop, the purpose of this paper is to advance the conceptual design of Business Intelligence (BI) systems with data identified from social networks. We develop an integrated social network data model, based on an in-depth analysis of Facebook. The data model can inform the design of data warehouses in order to offer new opportunities for CRM analyses, leading to a more consistent and richer picture of customers? characteristics, needs, wants, and demands. Four major contributions are offered. First, Social CRM and Social BI are introduced as emerging fields of research. Second, we develop a conceptual data model to identify and systematize the data available on online social networks. Third, based on the identified data, we design a multidimensional data model as an early contribution to the conceptual design of Social BI systems and demonstrate its application by developing management reports in a retail scenario. Fourth, intellectual challenges for advancing Social CRM and Social BI are discussed.

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Aim: As molecular and cytogenetic testing becomes increasingly sophisticated, more individuals are being diagnosed with rare chromosome disorders. Yet despite a burgeoning knowledge about biomedical aspects, little is known about implications for psychosocial development. The scant literature gives a general impression of deficits and adverse developmental outcomes. Method: Developmental data were obtained from two 16 year olds diagnosed with a rare chromosome disorder – a girl with 8p23.1 and a boy with 16q11.2q12.1. Measures of intellectual ability, academic achievement, and other aspects of functioning were administered at multiple time points from early childhood to adolescence. Results: Both adolescents experienced initial delays in motor and language development. Although the girl’s intelligence is assessed as being in the average range, she experiences difficulties with motor planning, spelling and writing. The boy has been diagnosed with a mild intellectual disability and demonstrates mild autistic features. Conclusions: The two case descriptions are in marked contrast to the published literature about these two chromosome anomalies. Both adolescents are developing much more positively than would be expected on the basis of the grim predictions of their paediatricians and the negative reports in the literature. It is concluded that, for most rare chromosome disorders, the range of possible developmental outcomes is currently unknown.

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Organisations are constantly seeking efficiency improvements for their business processes in terms of time and cost. Management accounting enables reporting of detailed cost of operations for decision making purpose, although significant effort is required to gather accurate operational data. Business process management is concerned with systematically documenting, managing, automating, and optimising processes. Process mining gives valuable insight into processes through analysis of events recorded by an IT system in the form of an event log with the focus on efficient utilisation of time and resources, although its primary focus is not on cost implications. In this paper, we propose a framework to support management accounting decisions on cost control by automatically incorporating cost data with historical data from event logs for monitoring, predicting and reporting process-related costs. We also illustrate how accurate, relevant and timely management accounting style cost reports can be produced on demand by extending open-source process mining framework ProM.

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Health complaint statistics are important for identifying problems and bringing about improvements to health care provided by health service providers and to the wider health care system. This paper overviews complaints handling by the eight Australian state and territory health complaint entities, based on an analysis of data from their annual reports. The analysis shows considerable variation between jurisdictions in the ways complaint data are defined, collected and recorded. Complaints from the public are an important accountability mechanism and open a window on service quality. The lack of a national approach leads to fragmentation of complaint data and a lost opportunity to use national data to assist policy development and identify the main areas causing consumers to complain. We need a national approach to complaints data collection in order to better respond to patients’ concerns.

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This study investigated the specificity of the post-concussion syndrome (PCS) expectation-as-etiology hypothesis. Undergraduate students (n = 551) were randomly allocated to one of three vignette conditions. Vignettes depicted either a very mild (VMI), mild (MI), or moderate-to-severe (MSI) motor vehicle-related traumatic brain injury (TBI). Participants reported the PCS and PTSD symptoms that they imagined the depicted injury would produce. Secondary outcomes (knowledge of mild TBI, and the perceived undesirability of TBI) were also assessed. After data screening, the distribution of participants by condition was: VMI (n = 100), MI (n = 96), and MSI (n = 71). There was a significant effect of condition on PCS symptomatology, F(2, 264) = 16.55, p < .001. Significantly greater PCS symptomatology was expected in the MSI condition compared to the other conditions (MSI > VMI; medium effect, r = .33; MSI > MI; small-to-medium effect, r = .22). The same pattern of group differences was found for PTSD symptoms, F(2, 264) = 17.12, p < .001. Knowledge of mild TBI was not related to differences in expected PCS symptoms by condition; and the perceived undesirability of TBI was only associated with reported PCS symptomatology in the MSI condition. Systematic variation in the severity of a depicted TBI produces different PCS and PTSD symptom expectations. Even a very mild TBI vignette can elicit expectations of PCS symptoms.

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Open the sports or business section of your daily newspaper, and you are immediately bombarded with an array of graphs, tables, diagrams, and statistical reports that require interpretation. Across all walks of life, the need to understand statistics is fundamental. Given that our youngsters’ future world will be increasingly data laden, scaffolding their statistical understanding and reasoning is imperative, from the early grades on. The National Council of Teachers of Mathematics (NCTM) continues to emphasize the importance of early statistical learning; data analysis and probability was the Council’s professional development “Focus of the Year” for 2007–2008. We need such a focus, especially given the results of the statistics items from the 2003 NAEP. As Shaughnessy (2007) noted, students’ performance was weak on more complex items involving interpretation or application of items of information in graphs and tables. Furthermore, little or no gains were made between the 2000 NAEP and the 2003 NAEP studies. One approach I have taken to promote young children’s statistical reasoning is through data modeling. Having implemented in grades 3 –9 a number of model-eliciting activities involving working with data (e.g., English 2010), I observed how competently children could create their own mathematical ideas and representations—before being instructed how to do so. I thus wished to introduce data-modeling activities to younger children, confi dent that they would likewise generate their own mathematics. I recently implemented data-modeling activities in a cohort of three first-grade classrooms of six year- olds. I report on some of the children’s responses and discuss the components of data modeling the children engaged in.

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Following an epistemic frame advanced by Elliott Eisner (2002), it is argued that the tradition of the arts and perspectives from artists have the potential to yield refreshing and interesting insights for the field of educational leadership. Moreover, it is argued that Eisner’s work on tacit knowledge which he advanced as an example of connoisseurship has important implications and posits the possibility of developing a more discerning “eye” in describing the work of educational leaders. To assess these assertions, the paper reports on two stages of interviews with nine former and current artists from Australia in order to understand the processes in which they engaged when they create art and how they encountered and managed barriers. The implications of this preliminary investigation are explored in this paper as they related to how leadership is defined and the issues pertaining to claims that leadership studies must be “scientific” to have currency and credibility. The article begins by making an argument for the value of the arts to advanced a more nuanced view of leadership, considers the importance of connoisseurship as a frame for understanding it, and then explores the cognitive functions performed by the arts before turning to the study at hand.

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Within the QUT Business School (QUTBS)– researchers across economics, finance and accounting depend on data driven research. They analyze historic and global financial data across a range of instruments to understand the relationships and effects between them as they respond to news and events in their region. Scholars and Higher Degree Research Students in turn seek out universities which offer these particular datasets to further their research. This involves downloading and manipulating large datasets, often with a focus on depth of detail, frequency and long tail historical data. This is stock exchange data and has potential commercial value therefore the license for access tends to be very expensive. This poster reports the following findings: •The library has a part to play in freeing up researchers from the burden of negotiating subscriptions, fundraising and managing the legal requirements around license and access. •The role of the library is to communicate the nature and potential of these complex resources across the university to disciplines as diverse as Mathematics, Health, Information Systems and Creative Industries. •Has demonstrated clear concrete support for research by QUT Library and built relationships into faculty. It has made data available to all researchers and attracted new HDRs. The aim is to reach the output threshold of research outputs to submit into FOR Code 1502 (Banking, Finance and Investment) for ERA 2015. •It is difficult to identify what subset of dataset will be obtained given somewhat vague price tiers. •The integrity of data is variable as it is limited by the way it is collected, this occasionally raises issues for researchers(Cook, Campbell, & Kelly, 2012) •Improved library understanding of the content of our products and the nature of financial based research is a necessary part of the service.

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One of the concerns about the use of Bluetooth MAC Scanner (BMS) data, especially from urban arterial, is the bias in the travel time estimates from multiple Bluetooth devices being transported by a vehicle. For instance, if a bus is transporting 20 passengers with Bluetooth equipped mobile phones, then the discovery of these mobile phones by BMS will be considered as 20 different vehicles, and the average travel time along the corridor estimated from the BMS data will be biased with the travel time from the bus. This paper integrates Bus Vehicle Identification system with BMS network to empirically evaluate such bias, if any. The paper also reports an interesting finding on the uniqueness of MAC IDs.

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Aim Performance measures for Australian laboratories reporting cervical cytology are a set of quantifiable measures relating to the profile and accuracy of reporting. This study reviews aggregate data collected over the ten years in which participation in the performance measures has been mandatory. Methods Laboratories submit annual data on performance measures relating to the profile of reporting, including reporting rates for technically unsatisfactory specimens, high grade or possible high grade abnormalities and abnormal reports. Cytology-histology correlation data and review findings of negative smears reported from women with histological high grade disease are also collected. Suggested acceptable standards are set for each measure. This study reviews the aggregate data submitted by all laboratories for the years 1998-2008 and examines trends in reporting and the performance of laboratories against the suggested standards. Results The performance of Australian laboratories has shown continued improvement over the study period. There has been a fall in the proportion of laboratories with data outside the acceptable standard range in all performance measures. Laboratories are reporting a greater proportion of specimens as definite or possible high grade abnormality. This is partly attributable to an increase in the proportion of abnormal results classified as high grade or possible high grade abnormality. Despite this, the positive predictive value for high grade and possible high grade abnormalities has continued to rise. Conclusion Performance measures for cervical cytology have provided a valuable addition to external quality assurance procedures in Australia. They have documented continued improvements in the aggregate performance, as well as providing benchmarking data and goals for acceptable performance for individual laboratories.

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The capability of storing multi-bit information is one of the most important challenges in memory technologies. An ambipolar polymer which intrinsically has the ability to transport electrons and holes as a semiconducting layer provides an opportunity for the charge trapping layer to trap both electrons and holes efficiently. Here, we achieved large memory window and distinct multilevel data storage by utilizing the phenomena of ambipolar charge trapping mechanism. As fabricated flexible memory devices display five well-defined data levels with good endurance and retention properties showing potential application in printed electronics.