In the absence of other information about the quality of surgery at the hospitals near them, Medicare patients undergoing selected cardiovascular or cancer procedures can significantly reduce their risk of operative death by selecting a high-volume hospital.

For many procedures, the observed associations between hospital volume and operative mortality are largely mediated by surgeon volume. Patients can often improve their chances of survival substantially, even at high-volume hospitals, by selecting surgeons who perform the operations frequently.

Regional differences in Medicare spending are largely explained by the more inpatient-based and specialist-oriented pattern of practice observed in high-spending regions. Neither quality of care nor access to care appear to be better for Medicare enrollees in higher-spending regions.

No abstract

Medicare enrollees in higher-spending regions receive more care than those in lower-spending regions but do not have better health outcomes or satisfaction with care. Efforts to reduce spending should proceed with caution, but policies to better manage further spending growth are warranted.

Objective To develop a method of identifying patients at high risk of readmission to hospital in the next 12 months for practical use by primary care trusts and general practices in the NHS in England. Data sources Data from hospital episode statistics showing all admissions in NHS trusts in England over five years, 1999-2000 to 2003-4; data from the 2001 census for England. Population All residents in England admitted to hospital in the previous four years with a subset of "reference" conditions for which improved management may help to prevent future admissions. Design Multivariate statistical analysis of routinely collected data to develop an algorithm to predict patients at highest risk of readmission in the next 12 months. The algorithm was developed by using a 10% sample of hospital episode statistics data for all of England for the period indicated. The coefficients for 21 most powerful (and statistically significant) variables were then applied against a second 10% test sample to validate the findings of the algorithm from the first sample. Results The key factors predicting subsequent admission included age, sex, ethnicity, number of previous admissions, and clinical condition. The algorithm produces a risk score (from 0 to 100) for each patient admitted with a reference condition. At a risk score threshold of 50, the algorithm identified 54.3% of patients admitted with a reference condition who would have an admission in the next 12 months; 34.7% of patients were "flagged" incorrectly (they would not have a subsequent admission). At risk score threshold levels of 70 and 80, the rate of incorrectly "flagged" patients dropped to 22.6% and 15.7%, but the algorithm found a lower percentage of patients who would be readmitted. The algorithm is made freely available to primary care trusts via a website. Conclusions A method of predicting individual patients at highest risk of readmission to hospital in the next 12 months has been developed, which has a reasonable level of sensitivity and specificity. Using various assumptions a "business case" has been modelled to demonstrate to primary care trusts and practices the potential costs and impact of an intervention using the algorithm to reduce hospital admissions.

N THE FACE OF THE FINANCIAL, practical, and ethical challenges inherent in undertaking randomized clinical trials (RCTs), investigators often use observational data to compare the outcomes of different therapies. These comparisons may be biased due to prognostically important baseline differences among patients, often as a result of unobserved treatment selection biases. Unmeasurable clinical and social interactions in the diagnostic-treatment pathway, and physicians' knowledge of unmeasured prognostic variables, may affect treatment decisions and outcomes. Physicians are frequently risk averse in case selection, performing interventions on lower-risk patients despite greater clinical benefit to higher-risk patients. [1][2][3] In some cases, especially when data are collected on detailed clinical risk factors, these differences can be controlled using standard statistical methods. In other cases, when unmeasured patients characteristics affect both the decision to treat and the outcome, these For editorial comment see p 314.

Background-Previous studies have documented racial disparities in treatment for acute myocardial infarction (AMI) among Medicare beneficiaries. However, the extent to which unobserved differences between hospitals explain some of these differences is unknown.