# Health Information Technology for Surveillance of Health Care-Associated Infections

> **NIH AHRQ K08** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2020 · $160,164

## Abstract

PROJECT SUMMARY/ABSTRACT
Health care-associated infections (HAIs) affect one in every 20 hospitalized patients and account for $10 billion
dollars in potentially preventable health care expenditures annually. Current efforts at detection of HAIs are
limited to manual chart review which hinders the generalizability and scalability of HAI detection. My goal in
seeking a Mentored Clinical Scientist Career Development Award is to acquire the necessary training, practical
experience, and knowledge to develop a health services research career as a principal investigator focusing on
leveraging novel health information technology (HIT) tools to improve the measurement of surgical health care
quality, safety, and effectiveness. To continue my progress towards this goal, the objective of this project is to
address the challenges of HAI detection by developing a robust and portable automated HAI surveillance toolkit.
This toolkit will combine structured electronic health record (EHR) data with rich information locked in clinical
notes using machine learning and natural language processing (NLP) to identify HAIs after surgical procedures.
Our overall hypothesis is that combining structured variables from the EHR supplemented with NLP will improve
our ability to identify HAIs after surgical procedures. To test the central hypothesis and accomplish the objectives
for this application, I will pursue the following three specific aims: 1) Determine the EHR data elements indicative
of postoperative HAIs and evaluate the performance of a novel HAI surveillance algorithm; 2) Identify the
presence of postoperative SSIs from clinical notes using an automated portable NLP-based algorithm; 3) Apply
user-centered design to create a high fidelity prototype of a surgical quality dashboard incorporating our HAI
case detection methodology. This contribution is a significant first step in a continuum of research that utilizes
the large amounts of data in the EHR combined with novel HIT methods to improve the measurement of surgical
health-care quality, safety, and effectiveness. This approach is significant because the tools developed in this
proposal have potential to serve as a prototype for identification and monitoring hospitals adverse events and
could be replicated on a national scale. The proposed research is innovative in its approach using a combination
of structured and unstructured data in the EHR along with novel machine learning and NLP tools to create a
generalizable surveillance toolkit for the detection of HAIs. This proposal is responsive to the AHRQ Special
Emphasis Notice (NOT-HS-13-011) specifically addressing the use of HIT to improve quality measurement. I
have assembled a mentoring team who all internationally recognized experts with long and successful track
records of funding and trainee mentorship. This project will provide the means to place me on a trajectory towards
a health services research career focused on improving the measurement of surgical...

## Key facts

- **NIH application ID:** 9928343
- **Project number:** 5K08HS025776-03
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** BRIAN T BUCHER
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $160,164
- **Award type:** 5
- **Project period:** 2018-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9928343

## Citation

> US National Institutes of Health, RePORTER application 9928343, Health Information Technology for Surveillance of Health Care-Associated Infections (5K08HS025776-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9928343. Licensed CC0.

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