We examined the implications of reliability adjustment on hospital mortality with surgery.
We used national Medicare data (2003–2006) for three surgical procedures: coronary artery bypass grafting (CABG), abdominal aortic aneurysm (AAA) repair, and pancreatic resection.
We conducted an observational study to evaluate the impact of reliability adjustment on hospital mortality rankings. Using hierarchical modeling, we adjusted hospital mortality for reliability using empirical Bayes techniques. We assessed the implication of this adjustment on the apparent variation across hospitals and the ability of historical hospital mortality rates (2003–2004) to forecast future mortality (2005–2006).
The net effect of reliability adjustment was to greatly diminish apparent variation for all three operations. Reliability adjustment was also particularly important for identifying hospitals with the lowest future mortality. Without reliability adjustment, hospitals in the “best” quintile (2003–2004) with pancreatic resection had a mortality of 7.6 percent in 2005–2006; with reliability adjustment, the “best” hospital quintile had a mortality of 2.7 percent in 2005–2006. For AAA repair, reliability adjustment also improved the ability to identify hospitals with lower future mortality. For CABG, the benefits of reliability adjustment were limited to the lowest volume hospitals.
Reliability adjustment results in more stable estimates of mortality that better forecast future performance. This statistical technique is crucial for helping patients select the best hospitals for specific procedures, particularly uncommon ones, and should be used for public reporting of hospital mortality.