# Use of electronic data to improve risk adjustment for antibiotic utilization metrics

> **NIH AHRQ R01** · UNIVERSITY OF MARYLAND BALTIMORE · 2020 · $304,190

## Abstract

Each year in the United States at least 2 million people become infected with antibiotic-resistant bacteria and
at least 23,000 people die as a direct result. Overuse of antibiotics is a key factor driving the emergence of
antibiotic-resistant bacteria. This has led federal agencies to recommend acute care facilities have
antimicrobial stewardship programs and record antimicrobial utilization data using the National Quality Forum
(NQF)-endorsed Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network
(NHSN) Antimicrobial Use Measure. Many believe that it is inevitable that antimicrobial utilization data will be
used to judge performance of hospitals and as a pay-for-performance outcome. The gap in knowledge is that
current risk adjustment methods used by the CDC for antimicrobial utilization data are sub-optimal because
methods to adjust for patient comorbid conditions do not exist. Our long-term goal is to improve the quality and
validity of publicly reported metrics for healthcare quality including healthcare-associated infection and
antimicrobial utilization data. The overall objective of this proposal is to determine which comorbid conditions
should be used for risk adjustment of antimicrobial utilization data. Our central hypothesis is that comorbid
conditions identified by ICD codes that are easily obtained electronically from hospitals across the United
States can be used to improve risk adjustment of antimicrobial utilization metrics. The rationale for this
proposal is the need for further advancement in risk adjustment methodology for antimicrobial utilization
metrics. Now is the ideal time to establish appropriate risk adjustment measures for antimicrobial use because
CMS has not yet incorporated antibiotic use into its value-based purchasing system. Comorbid conditions are a
logical starting point as they have been proven to be significant predictors of other infectious disease outcomes
and are easy to obtain. We plan to test our central hypothesis and, thereby, accomplish the objective of this
proposal by pursuing the following specific aims: Aim 1: Perform a cohort study of adult patients admitted to
multiple hospitals across the United States to determine which electronically obtained comorbidities are risk
factors for different antibiotic utilization metrics. Aim 2: Demonstrate that risk adjustment using comorbid
conditions affect hospital rankings of antimicrobial utilization. The expected outcome of this research is the
identification of comorbid conditions using ICD codes for that can be used to risk adjust antimicrobial utilization
data. The implementation of these by the CDC and CMS will lead to more valid publically available data. The
significance of our research is that it will identify easily available electronically comorbid conditions that could
be used to better risk adjust these antibiotic utilization metrics. The proposed research is innovative in that no
one has explored the use of ICD code...

## Key facts

- **NIH application ID:** 9981718
- **Project number:** 5R01HS026205-03
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** ANTHONY D HARRIS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $304,190
- **Award type:** 5
- **Project period:** 2018-09-30 → 2021-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9981718

## Citation

> US National Institutes of Health, RePORTER application 9981718, Use of electronic data to improve risk adjustment for antibiotic utilization metrics (5R01HS026205-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9981718. Licensed CC0.

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