Diagnostic error is difficult to define, because diagnosis is both a process and the outcome of that process. Furthermore, it is difficult to judge when the process is complete – a physician may have a working diagnosis that seems to be the most likely reason for a given set of symptoms, but often only time will tell if that diagnosis was correct or not. Was his or her original diagnosis wrong,...

BACKGROUND Pediatric anaphylaxis is commonly misdiagnosed in the Emergency Department (ED). We aimed to determine the impact of inaccurate diagnosis on the management and follow-up of pediatric anaphylaxis presenting to the ED. METHODS Retrospective chart review of ED management of children aged 0-18 years with allergic presentations to three EDs in Melbourne, Australia in 2014. Cases were in...

In this paper, we present case deletion and mean shift outlier models for linear mixed measurement error models using the corrected likelihood of Nakamura (1990). We derive the corrected score test statistic for outliers detection based on mean shift outlier models. Furthermore, several case deletion diagnostics are constructed as a tool for influence diagnostics. It is found that they can be w...

A major amount of our time working in clinical practice involves thinking and decision making. Perhaps it is because decision making is such a commonplace activity that it is assumed we can all make effective decisions. However, this is not the case and the example of diagnostic error supports this assertion. Until quite recently there has been a general nihilism about the ability to change the...

In recent years, a number of new neuroimaging techniques have detected covert awareness in some patients previously thought to be in a vegetative state/unresponsive wakefulness syndrome. This raises worries for patients, families, and physicians, as it indicates that the existing diagnostic error rate in this patient group is higher than assumed. Recent research on a subset of these techniques,...

BACKGROUND There is increasing availability of data derived from diagnoses made routinely in mental health care, and interest in using these for research. Such data will be subject to both diagnostic (clinical) error and administrative error, and so it is necessary to evaluate its accuracy against a reference-standard. Our aim was to review studies where this had been done to guide the use of o...

We are often interested in estimating sensitivity and specificity of a group of raters or a set of new diagnostic tests in situations in which gold standard evaluation is expensive or invasive. Numerous authors have proposed latent modeling approaches for estimating diagnostic error without a gold standard. Albert and Dodd showed that, when modeling without a gold standard, estimates of diagnos...

We present a general theory of estimation of analysis error covariances based on 9 cross-validation as well as a geometric interpretation of the method. In particular we use the variance 10 of passive observation–minus-analysis residuals and show that the true analysis error variance can be 11 estimated, without relying on the optimality assumption. This approach is used to obtain near 12 optim...

The great majority of medical diagnoses are made using automatic, efficient cognitive processes, and these diagnoses are correct most of the time. This analytic review concerns the exceptions: the times when these cognitive processes fail and the final diagnosis is missed or wrong. We argue that physicians in general underappreciate the likelihood that their diagnoses are wrong and that this te...