LEARNING FROM NEAR MISSES IN MEDICATION ERRORS: A BAYESIAN APPROACH

Medical errors originating in health care facilities are a significant source of preventable morbidity, mortality, and healthcare costs. Voluntary error report systems that collect information on the causes and contributing factors of medi- cal errors regardless of the resulting harm may be useful for developing effective harm prevention strategies. Some patient safety experts question the utility of data from errors that did not lead to harm to the patient, also called near misses. A near miss (a.k.a. close call) is an unplanned event that did not result in injury to the patient. Only a fortunate break in the chain of events prevented injury. We use data from a large voluntary reporting system of 836,174 medication errors from 1999 to 2005 to provide evidence that the causes and contributing factors of errors that result in harm are similar to the causes and contributing factors of near misses. We develop Bayesian hierarchical models for estimating the log odds of selecting a given cause (or contributing factor) of error given harm has occurred and the log odds of selecting the same cause given that harm did not occur. The posterior distribution of the correlation between these two vectors of log-odds is used as a measure of the evidence supporting the use of data from near misses and their causes and contributing factors to prevent medical errors. In addition, we identify the causes and contributing factors that have the highest or lowest log-odds ratio of harm versus no harm. These causes and contributing factors should also be a focus in the design of prevention strategies. This paper provides important evidence on the utility of data from near misses, which constitute the vast majority of errors in our data.

Kernel Estimation of Rate Function for Recurrent Event Data

Recurrent event data are largely characterized by the rate function but smoothing techniques for estimating the rate function have never been rigorously developed or studied in statistical literature. This paper considers the moment and least squares methods for estimating the rate function from recurrent event data. With an independent censoring assumption on the recurrent event process, we study statistical properties of the proposed estimators and propose bootstrap procedures for the bandwidth selection and for the approximation of confidence intervals in the estimation of the occurrence rate function. It is identified that the moment method without resmoothing via a smaller bandwidth will produce curve with nicks occurring at the censoring times, whereas there is no such problem with the least squares method. Furthermore, the asymptotic variance of the least squares estimator is shown to be smaller under regularity conditions. However, in the implementation of the bootstrap procedures, the moment method is computationally more efficient than the least squares method because the former approach uses condensed bootstrap data. The performance of the proposed procedures is studied through Monte Carlo simulations and an epidemiological example on intravenous drug users.

Cerebral networks linked to the event-related potential P300

P300 is an event-related potential that is elicited by an oddball paradigm. In several neuropsychiatric diseases, differences in latencies and amplitude compared to healthy subjects have been reported. Because of its clinical significance, several investigations have tried to elucidate the intracranial origins of the P300 component. In the present study we could demonstrate a network of P300 generators. Investigated were 15 healthy subjects with an acoustical oddball paradigm within a fMRI block design, which enabled us to exclude attention or acoustical processing effects. The inferior and middle frontal, superior temporal, lower parietal cortex, the insula and the anterior cingulum were significantly activated symmetrical in both hemispheres.

Cerebral disconnectivity: an early event in schizophrenia

Neuroimaging and electrophysiological investigations have demonstrated numerous differences in brain morphology and function of chronic schizophrenia patients compared to healthy controls. Studying patients at the beginning of their disease without the confounding effects of chronicity, medication, and institutionalization may provide a better understanding of schizophrenia. Recently, at many institutions around the world, special projects have been launched for specialized treatment and research of this interesting patient group. Using the PubMed search engine in this update, the authors summarize recent investigations between January 2002 and September 2006 that focus on whether signs of disconnectivity already exist early in the disease process. They discuss gray and white matter changes, their impact on symptomatology, electroencephalogram-based studies on connectivity, and possible influences of medication.