Technology-driven root cause analysis and clinical enhancement for clostridioides difficile infection (TRACE-CDI), a digital thread to TRACE hospital-acquired infections

The National Institute of Allergy and Infectious Diseases awarded Datanova Scientific LLC $306,969 on July 21, 2026, under the Allergy and Infectious Diseases Research program (CFDA 93.855) to develop TRACE-CDI, a digital platform for automated root cause analysis of hospital-acquired Clostridioides difficile infections.

The project delivers a scalable, technology-driven system that integrates electronic health records and hospital data to streamline root cause analysis and improve infection control strategies for healthcare-acquired C. difficile infections, which drive prolonged hospital stays, increased costs, and regulatory penalties. Aim 1 engages infection preventionists, quality improvement personnel, and antimicrobial stewardship teams through qualitative interviews and focus groups to understand current root cause analysis practices and inform user-centered design of the platform. Aim 2 develops an ontology modeling critical hospital data elements—including patient risk factors, environmental exposures, healthcare personnel contacts, clinical procedures, and administrative factors—that automates fault tree analysis to map pathways contributing to hospital-acquired C. difficile infection.

Performance of work occurs in South Carolina. The period of performance runs from July 21, 2026, through January 31, 2027. This is a Project Grant, an assistance type supporting biomedical research in infectious diseases and related areas through competitive peer review.

Abstract

Clostridioides difficile (C. Diff) is a leading cause of hospital-acquired infections (HAIS), significantly impacting patient morbidity, mortality, and healthcare costs. Hospital-acquired C. Diff infections (HA-CDIS) result in prolonged hospital stays, increased healthcare expenses, and regulatory penalties. Despite efforts to control and prevent HA-CDIS, current root cause analysis (RCA) processes remain resource-intensive, manual, and often omit system-level factors that contribute to infection risk. This project aims to develop trace-CDI, a scalable, automated platform that leverages electronic health records (EHR) and other hospital data to streamline RCA for HA-CDIS and improve infection control strategies. In aim 1, the project will engage healthcare professionals, including infection preventionists, quality improvement personnel, and antimicrobial stewardship teams, to understand current RCA practices for HA-CDIS. Through qualitative interviews and focus groups, we will gather insights on the RCA investigation process. These insights will inform the user- centered design of the trace-CDI platform, ensuring that IT integrates seamlessly into hospital workflows and meets the practical needs of its users. We will explore the resources, data requirements, and team dynamics involved in conducting rcas, which will guide the design and functionality of the platform. Aim 2 will focus on developing an ontology that models critical hospital data elements for RCA of CDIS, including patient risk factors, environmental exposures, contacts with healthcare personnel, clinical procedures, and non-clinical administration. This ontology will form the backbone of trace-CDI, enabling the automation of fault tree analysis (FTA) to map the pathways that contribute to HA-CDI. By integrating data from various hospital sources, trace-CDI will provide a comprehensive view of infection risk factors at both patient and system levels. In aim 3, we will develop and validate the trace-CDI prototype. We will create a digital thread architecture that traces the acquisition and spread of C. Diff infections through hospital and community entities, using advanced data fusion and machine learning techniques. The prototype will validate the accuracy, feasibility, and commercial potential of this project. Trace-CDI provides an automated approach that will reduce manual effort, improve the efficiency of RCA investigations, reduce human error, and provide a more accurate, data-driven approach to identifying risk factors and infection pathways. Overall, trace-CDI will revolutionize the way hospitals investigate and prevent HAIS, providing a scalable tool for healthcare systems of all sizes. The platform's ability to integrate system-level data, streamline investigations, and identify infection control gaps will enhance hospital compliance with regulatory reporting requirements and support broader infection prevention efforts. With its potential to be applied to other HAIS, trace-CDI offers a significant opportunity to improve patient safety and reduce healthcare costs.