The death of managed care: a regulatory autopsy

On the heels of widespread patient protection legislation in the states, the managed care industry abandoned or greatly scaled back the core elements of gate-keeping, utilization management, and financial incentives, which are the very targets of this legislation. This article explores whether, and to what extent, the industry's abrupt change in course can be attributed to these laws. Based on extensive interviews with key informants in six representative states, the article concludes that these laws were not the primary driver of changes in managed care practices. However, patient protection laws interacted with other social and market forces, through complex forms of feedback and reinforcement, to bring about more thoroughgoing change than would have otherwise occurred.     

Some limitations of the groves-ledyard optimal mechanism

Public Choice - The tax rule suggested by Groves and Ledyard is certainly an ingenious one, although there is no indication as to how one might fall upon that specific scheme. If offers Pareto...     

Error Reduction, Patient Safety and Institutional Ethics Committees

Institutional ethics committees remain largely absent from the literature on error reduction and patient safety. In this paper, the author endeavors to fill the gap. As noted in the Hastings Center's recent report, "Promoting Patient Safety," the occurrence of medical error involves complex web of multiple factors. Human misstep is certainly one such factor, but not the only one. This paper builds on the Hastings Center's report in arguing that institutional ethics committees ought to play an integral role in the transformation of a "culture of blame" to a "culture of safety" in healthcare delivery.     

A laboratory incubation method for determining the rate of microbiological degradation of skeletal muscle tissue in soil

A controlled laboratory experiment is described, in principle and practice, which can be used for the of determination the rate of tissue decomposition in soil. By way of example, an experiment was conducted to determine the effect of temperature (12 degrees, 22 degrees C) on the aerobic decomposition of skeletal muscle tissue (Organic Texel x Suffolk lamb (Ovis aries)) in a sandy loam soil. Measurements of decomposition processes included muscle tissue mass loss, microbial CO2 respiration, and muscle tissue carbon (C) and nitrogen (N). Muscle tissue mass loss at 22 degrees C always was greater than at 12 degrees C (p < 0.001). Microbial respiration was greater in samples incubated at 22 degrees C for the initial 21 days of burial (p < 0.01). All buried muscle tissue samples demonstrated changes in C and N content at the end of the experiment. A significant correlation (p < 0.001) was demonstrated between the loss of muscle tissue-derived C (Ct) and microbially-respired C (Cm) demonstrating CO2 respiration may be used to predict mass loss and hence biodegradation. In this experiment Q10 (12 degrees C-22 degrees C) = 2.0. This method is recommended as a useful tool in determing the effect of environmental variables on the rate of decomposition of various tissues and associated materials.     

Classification of premium and regular gasoline by gas chromatography/mass spectrometry,principal component analysis and artificial neural networks

Detection and correct classification of gasoline is important for both arson and fuel spill investigation. Principal component analysis (PCA) was used to classify premium and regular gasolines from gas chromatography-mass spectrometry (GC-MS) spectral data obtained from gasoline sold in Canada over one calendar year. Depending upon the dataset used for training and tests, around 80-93% of the samples were correctly classified as either premium or regular gasoline using the Mahalanobis distances calculated from the principal components scores. Only 48-62% of the samples were correctly classified when the premium and regular gasoline samples were divided further into their winter/summer sub-groups. Artificial neural networks (ANNs) were trained to recognise premium and regular gasolines from the same GC-MS data. The best-performing ANN correctly identified all samples as either a premium or regular grade. Approximately 97% of the premium and regular samples were correctly classified according to their winter or summer sub-group.