# Methods for Profiling Hospital Performance Based on Healthcare-AssociatedInfections

> **NIH AHRQ R01** · HARVARD PILGRIM HEALTH CARE, INC. · 2020 · $327,218

## Abstract

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.

## Key facts

- **NIH application ID:** 10096583
- **Project number:** 1R01HS027791-01
- **Recipient organization:** HARVARD PILGRIM HEALTH CARE, INC.
- **Principal Investigator:** Rui Wang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $327,218
- **Award type:** 1
- **Project period:** 2020-09-30 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10096583

## Citation

> US National Institutes of Health, RePORTER application 10096583, Methods for Profiling Hospital Performance Based on Healthcare-AssociatedInfections (1R01HS027791-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10096583. Licensed CC0.

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