NCT06791382

AI-Driven Prediction of Hospital-Acquired Infections With EHR

Predicting Hospital-Acquired Infections Using Electronic Health Records: An AI-Assisted Approach

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,000,000 (estimated)
• Sponsor: The Eye Hospital of Wenzhou Medical University · Academic / Other
• Sex: All
• Age: 0 Years – 90 Years
• Healthy volunteers: Accepted

Summary

This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying and diagnosing infection, leveraging multimodal health data.

Detailed description

Hospital-acquired infections (HAIs) are a significant cause of morbidity and mortality in healthcare settings. Early identification and prevention of HAIs are crucial for improving patient outcomes, reducing healthcare costs, and preventing the spread of infections. In clinical practice, healthcare providers often need to integrate a wide range of patient data, including medical history, laboratory test results, medication usage, surgical procedures, and clinical observations, to assess infection risks and prevent HAIs. As infection control and precision medicine become increasingly important, the challenge remains to predict and prevent infections, especially in patients with subtle or asymptomatic risk factors. Recent advancements in artificial intelligence and data analysis techniques have shown great promise in improving the accuracy and efficiency of infection prediction and prevention. This study aims to develop an AI-assisted decision-making system by integrating multimodal data from electronic health records, lab results, clinical observations, and patient demographics. The objective is to enhance the early identification of patients at risk for HAIs, streamline clinical workflows, and optimize infection control measures. Ultimately, this system seeks to reduce the incidence of hospital-acquired infections, improve patient safety, and enhance overall healthcare quality.

Conditions

• Hospital-acquired Infections

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	AI-Based Diagnostic and Prognostic Model	This intervention involves an AI system that integrates multimodal data, including patient medical history, laboratory test results, clinical observations, and treatment data, to predict the risk of hospital-acquired infections (HAIs). The system uses deep learning algorithms to provide real-time, accurate predictions, enabling early identification of patients at risk for infections. By analyzing historical health data, the model aims to predict potential infection developments, improving early detection, prevention strategies, and patient outcomes in hospital settings.

Timeline

• Start date: 2023-02-01
• Primary completion: 2025-05-01
• Completion: 2025-05-01
• First posted: 2025-01-24
• Last updated: 2025-04-17

Locations

2 sites across 1 country: China