# Enhanced Detection System for Healthcare-Associated Transmission of Infection

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $728,162

## Abstract

ABSTRACT
Despite recent progress in reducing the incidence of healthcare-associated infections (HAIs), the Centers for
Disease Control and Prevention estimated that 722,000 HAIs occurred in U.S. acute care hospitals in 2011,
resulting in 75,000 deaths. Current methods for detecting outbreaks in hospitals are rudimentary and likely to
miss some outbreaks altogether and result in substantial delays in detection of others.
There are two major
developments in healthcare that have the potential to revolutionize how healthcare associated outbreaks of
bacterial pathogens are identified and controlled in hospitals. First, the
Affordable Care Act mandates use of
the electronic medical record (EMR), which has led to its
widespread use in healthcare.
Second, the costs of
bacterial whole genome sequencing (WGS) have declined substantially, which is making its use by infection
programs increasingly feasible. In this application, we propose to establish and evaluate the impact of the
Enhanced Detection System for Healthcare Association Transmission (EDS-HAT) at the University of
Pittsburgh Medical Center (UPMC). EDS-HAT uses a combination of WGS and analysis of the EMR for
enhanced outbreak detection. Our specific aims are to 1a): Determine the utility of EDS-HAT to identify HAT
that is not identified through routine infection prevention practice, 1b): Improve the efficiency and reduce the
cost of EDS-HAT by using the EMR to restrict the use of WGS, 2a): Measure reductions in HAIs following
implementation of EDS-HAT, and 2b): Estimate the number of infections and deaths prevented and healthcare
costs averted by EDS-HAT. For Aim 1a, EDS-HAT will be performed retrospectively while routine infection
prevention practice (requests for molecular typing when an outbreak is suspected) continues, thus allowing a
direct comparison of the two approaches. For Aim 1b, we will improve the efficiency and reduce the cost of
EDS-HAT by using machine learning and data mining of the EMR to select isolates for WGS. For Aim 2a, we
will monitor changes in HAI rates both before and after implementation of EDS-HAT in real time, which will
occur at the beginning of year 3. Finally, for Aim 2b, we will perform clinical and budget impact analyses to
determine the overall impact of EDS-HAT. To accomplish these aims, we have assembled a team with
expertise in infectious diseases, outbreak investigation, infection prevention, microbial genomics and genomic
epidemiology, machine learning and data mining, economic analysis and modeling, epidemiology, and
biostatistics. EDS-HAT will likely lead to substantial reductions in infections, deaths, and healthcare costs and
can serve as a model for how HAT is detected in hospitals.

## Key facts

- **NIH application ID:** 9984252
- **Project number:** 5R01AI127472-05
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Lee H Harrison
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $728,162
- **Award type:** 5
- **Project period:** 2016-09-26 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9984252

## Citation

> US National Institutes of Health, RePORTER application 9984252, Enhanced Detection System for Healthcare-Associated Transmission of Infection (5R01AI127472-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9984252. Licensed CC0.

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