PubMed · 1538635
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M Stockler. Better right than quick?. https://doi.org/10.1177/0272989x9201200111
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OBJECTIVE: To determine how often searching with Google (the most popular search engine on the world wide web) leads doctors to the correct diagnosis. DESIGN: Internet based study using Google to search for diagnoses; researchers were blind to the correct diagnoses. SETTING: One year's (2005) diagnostic cases published in the case records of the New England Journal of Medicine. CASES: 26 cases from the New England Journal of Medicine; management cases were excluded. MAIN OUTCOME MEASURE: Percentage of correct diagnoses from Google searches (compared with the diagnoses as published in the New England Journal of Medicine). RESULTS: Google searches revealed the correct diagnosis in 15 (58%, 95% confidence interval 38% to 77%) cases. CONCLUSION: As internet access becomes more readily available in outpatient clinics and hospital wards, the web is rapidly becoming an important clinical tool for doctors. The use of web based searching may help doctors to diagnose difficult cases.
Southern Europe is exposed to anthropogenic and natural forest fires. These result in loss of lives, goods and infrastructure, but also deteriorate the natural environment and degrade ecosystems. The early detection and combating of such catastrophes requires the use of a decision support system (DSS) for emergency management. The current literature reports on a series of efforts aimed to deliver DSSs for the management of the forest fires by utilising technologies like remote sensing and geographical information systems (GIS), yet no integrated system exists. This manuscript presents the results of scientific research aiming to the development of a DSS for managing forest fires. The system provides a series of software tools for the assessment of the propagation and combating of forest fires based on Arc/Info, ArcView, Arc Spatial Analyst, Arc Avenue, and Visual C++ technologies. The system integrates GIS technologies under the same data environment and utilises a common user interface to produce an integrated computer system based on semi-automatic satellite image processing (fuel maps), socio-economic risk modelling and probabilistic models that would serve as a useful tool for forest fire prevention, planning and management. Its performance has been demonstrated via real time up-to-date accurate information on the position and evolution of the fire. The system can assist emergency assessment, management and combating of the incident. A site demonstration and validation has been accomplished for the island of Evoia, Greece, an area particularly vulnerable to forest fires due to its ecological characteristics and prevailing wind patterns.
Natural disasters are caused by the exposure and vulnerabilities to natural hazards of people, infrastructure and economic activities. Analysis of these factors has permitted identification of countries and areas within them where disaster-related mortality and economic losses are likely in the future. These high-risk areas are candidates for increased attention to, and investment in, disaster risk identification, reduction and transfer. Plans are underway to further identify disaster risk levels and factors on national and subnational scales in high-risk countries to create evidence for improved risk management decision-making. In this paper, I review selected recent global and regional risk analyses to highlight findings, areas for improvement and next steps in the overall process of using disaster risk information for more effective risk management and cost-effective reduction of losses.