No abstract

An empirical Bayes approach is used to derive a Stein-type estimator of a multivariate normal mean when the components have unequal variances. This estimator is applied to estimating the probability that a fire alarm reported from a particular street box signals a structural fire rather than a false alarm or other emergency. The approach is to group alarm boxes into relatively homogeneous neighborhoods and to make empirical Bayes estimates of the "probability structural" for each box in the neighborhood from yearly (1967-1969) Bronx data. A dispatching rule based on the estimates is evaluated on 1970 data.

We present alternative methods for estimating hospital-level mortality rates to those used by the Health Care Finance Administration for Medicare patients. We use an empirical Bayes model to represent the different sources of variation in observed hospital-specific mortality rates and we use a logistic regression model to adjust for severity differences (in patient mix) across hospitals. In addition to providing a principled derivation of a standard error for the commonly used estimator, our fully model-based formulation produces much more accurate estimates and resolves the severe problem of multiple comparisons that arises when extreme estimates are used to identify exceptional hospitals. We estimate models for each of four disease conditions using the national Medicare mortality data base which does not contain patient severity descriptors, and mortality data from national samples which do include patient severity descriptors. We find substantial between-hospital variation in the unadjusted death rates from the national data base. Mortality rates differ substantially with patient severity in our models, but the sample sizes are too small to yield reliable estimates of the between-hospital variation in adjusted mortality rates.

The skin cancer toolkit is an accessible online learning resource for improving confidence with skin cancer referral amongst GPs. Although we were unable to identify any immediate changes in skin cancer diagnoses or appropriate referral behaviours, research is required to evaluate its longer term effects on outcomes.

We analyze the claims database of a large malpractice insurer covering more than 8,000 physicians and 9,300 claims. Applying empirical Bayes methods in a regression setting, we construct a predictor of each physician's underlying propensity to incur malpractice claims. Our explanatory factors are physician demographics (age, sex, specialty, training) and physician practice pattern characteristics (practice setting, procedures performed, practice intensity, special risk factors, and characteristics of hospital(s) on staff of). We divide physicians into medical and surgical/ancillary specialty categories and fit separate models to each. In the surgical/ancillary specialty group, physician characteristics can effectively distinguish between more and less claims-prone physicians. Physician characteristics have somewhat less predictive power in the medical specialty group. As measured by predictive information, physician characteristics are superior to 10 years of claims history. Insofar as medical malpractice claims can be thought of as extreme indicators of poor-quality care, this finding suggests that easily gathered physician characteristics can be helpful in designing targeted quality of care improvement policies.