Methods for Profiling Hospital Performance Based on Healthcare-AssociatedInfections

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Project Summary/Abstract Healthcare-associated infections (HAIs) affect 1 in 31 hospitalized patients and are a significant cause of potentially preventable patient harm. The Centers for Medicare and Medicaid Services (CMS) incorporates colon surgical site infections (SSIs) and other HAI rates in metrics that are used to rank hospitals on their quality of care. The reliance of national policy on hospital rankings underscores the need for robust methodology that can properly distinguish meaningful differences in care as opposed to differences in patient populations or random variation. The proposed work aims to develop improved methods for hospital profiling and addresses three methodological gaps leveraging detailed administrative and clinical data from a network of 189 community hospitals. Profiling hospital performance requires risk-adjustment, which entails selecting patient-level characteristics that predict SSI risks while accounting for clustering within hospitals. However, variable selection procedures are limited for clustered data due to challenges in handling the complex dependence structure. Aim 1 proposes to develop a new variable selection framework for high-dimensional clustered data, accommodating missing covariates. Concerns have been raised about the reliability of rankings for hospitals with a low surgical volume. Aim 2 proposes to develop analytic tools that can be used to determine, for a particular setting, the required surgical volume for a user-specified threshold of the rate of misclassifying into the worst-performing quartile. Methods that aim to improve the reliability of hospital rankings will also be developed by pooling information from multiple years or from multiple indicators. Aim 3 proposes to develop valid methods for comparing different ranking systems and for identifying hospital characteristics that contribute to the differences. User-friendly software will be developed to facilitate the implementation of new methods. The methods development will be guided by an HCA colon SSI dataset and the AHRQ HCUP’s NIS database (2014-2016). The methods can be applied broadly to HAIs and outcomes of other important conditions such as sepsis. The proposed research is significant, because success in addressing these issues will improve the ability to distinguish differences in HAI rates across hospitals that are truly meaningful versus an artifact of different patient populations, or that might otherwise be masked by the low surgical volume. Innovation lies in the development of new methods and tools for better risk-adjustment, to increase the reliability of hospital rankings, and for comparing ranking systems. The results of the proposed research will help inform decision-making on the ongoing pay-for-performance programs, and ultimately improve our capacity to prevent HAIs and improve quality of care.