Measuring and forecasting quality in English hospitals

Hospital performance metrics, often in the form of risk-adjusted hospital mortality rates, are increasingly being made available in the public domain to compare hospitals. Despite the proliferation of these metrics, uncertainty remains regarding their validity and reliability given the noise surrounding their underlying measures. The paper considers a quality measure of hospital performance developed by McClellan and Staiger which smooths within hospitals and over time, while remaining computationally straightforward. The McClellan and Staiger method improves on others by incorporating different measures of outcome, eliminating systematic bias arising from the heterogeneous mix of hospital outputs and the noise that is inherent in other measures of quality. The technique also allows the forecasting of future quality. Using English hospital episode statistics for the years 2000-2005 for acute myocardial infarction and hip replacement, we use this technique to return quality measures based on hospital fixed effects estimated from yearly cross-sectional patient level data, and vector auto-regressions estimated over time, which then combine information from different time periods and across conditions to produce robust hospital quality measures. Our results suggest that this method is well suited to measure and predict provider quality of care in the English setting.