Promoting Patient Safety and Preventing Medical Error in Emergency Departments

An estimated 108,000 people die each year from potentially preventable iatrogenic injury. One in 50 hospitalized patients experiences a preventable adverse event. Up to 3% of these injuries and events take place in emergency departments. With long and detailed training, morbidity and mortality conferences, and an emphasis on practitioner responsibility, medicine has traditionally faced the challenges of medical error and patient safety through an approach focused almost exclusively on individual practitioners. Yet no matter how well trained and how careful health care providers are, individuals will make mistakes because they are human. In general medicine, the study of adverse drug events has led the way to new methods of error detection and error prevention. A combination of chart reviews, incident logs, observation, and peer solicitation has provided a quantitative tool to demonstrate the effectiveness of interventions such as computer order entry and pharmacist order review. In emergency medicine (EM), error detection has focused on subjects of high liability: missed myocardial infarctions, missed appendicitis, and misreading of radiographs. Some system-level efforts in error prevention have focused on teamwork, on strengthening communication between pharmacists and emergency physicians, on automating drug dosing and distribution, and on rationalizing shifts. This article reviews the definitions, detection, and presentation of error in medicine and EM. Based on review of the current literature, recommendations are offered to enhance the likelihood of reduction of error in EM practice.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74930/1/j.1553-2712.2000.tb00466.x.pd

Alzheimer disease models and human neuropathology: similarities and differences

Animal models aim to replicate the symptoms, the lesions or the cause(s) of Alzheimer disease. Numerous mouse transgenic lines have now succeeded in partially reproducing its lesions: the extracellular deposits of Aβ peptide and the intracellular accumulation of tau protein. Mutated human APP transgenes result in the deposition of Aβ peptide, similar but not identical to the Aβ peptide of human senile plaque. Amyloid angiopathy is common. Besides the deposition of Aβ, axon dystrophy and alteration of dendrites have been observed. All of the mutations cause an increase in Aβ 42 levels, except for the Arctic mutation, which alters the Aβ sequence itself. Overexpressing wild-type APP alone (as in the murine models of human trisomy 21) causes no Aβ deposition in most mouse lines. Doubly (APP × mutated PS1) transgenic mice develop the lesions earlier. Transgenic mice in which BACE1 has been knocked out or overexpressed have been produced, as well as lines with altered expression of neprilysin, the main degrading enzyme of Aβ. The APP transgenic mice have raised new questions concerning the mechanisms of neuronal loss, the accumulation of Aβ in the cell body of the neurons, inflammation and gliosis, and the dendritic alterations. They have allowed some insight to be gained into the kinetics of the changes. The connection between the symptoms, the lesions and the increase in Aβ oligomers has been found to be difficult to unravel. Neurofibrillary tangles are only found in mouse lines that overexpress mutated tau or human tau on a murine tau −/− background. A triply transgenic model (mutated APP, PS1 and tau) recapitulates the alterations seen in AD but its physiological relevance may be discussed. A number of modulators of Aβ or of tau accumulation have been tested. A transgenic model may be analyzed at three levels at least (symptoms, lesions, cause of the disease), and a reading key is proposed to summarize this analysis

Snow cover is a neglected driver of Arctic biodiversity loss

Snow has far-reaching effects on ecosystem processes and biodiversity in high-latitude ecosystems, but these have been poorly considered in climate change impact models1,2. Here, to forecast future trends in species occurrences and richness, we fitted species-environment models with temperature data from three climate scenarios and simulated up to a 40% decrease in snow cover duration (SCD)3. We used plot-scale data on 273 vascular plant, moss and lichen species in 1,200 study sites spanning a wide range of environmental conditions typical for mountainous Arctic landscapes (within 165 km2). According to the models, a rise in temperature increased overall species richness and caused only one species to lose all suitable habitat. In contrast, a shorter SCD tempered the effect of increasing temperature on species richness and led to accelerated rates of species’ local extinctions after a tipping point at 20-30% SCD decrease. All three species groups showed similar extinction rates but contrasting species richness responses. Our simulations indicate that future biodiversity patterns in Arctic regions are highly dependent on the evolution of snow conditions. Climate impact models that ignore the effects of snow cover change may provide biased biodiversity projections, with potentially erratic implications for Arctic nature conservation planning.Peer reviewe