Best Practices Reminders for Hospital Acquired Infections
(CLABSI AI Trial)
What You Need to Know Before You Apply
What is the purpose of this trial?
This trial aims to determine if a special tool for Infection Preventionists (IPs) can lower bloodstream infection rates caused by central lines in hospitals. The tool uses machine learning to predict potential infections and assists IPs in reminding clinical teams of best prevention practices. The trial divides hospitals into two groups: one receives early access to the tool, while the other receives it later. The study focuses on hospitals with the highest infection rates. As an unphased trial, this research offers an opportunity to contribute to innovative advancements that could significantly enhance patient safety in hospitals.
Do I need to stop my current medications for this trial?
The trial information does not specify whether you need to stop taking your current medications.
What prior data suggests that this protocol is safe for reducing hospital acquired infections?
Research has shown that strategies to prevent infections, such as reminders for best practices, are generally safe for people. These methods do not involve drugs or medical procedures but focus on improving hospital routines to prevent infections.
Studies have found that following standard infection control steps can significantly reduce infections in healthcare settings. These steps consist of rules and actions that maintain hospital cleanliness and safety. For example, regular cleaning and specific patient handling techniques can prevent the spread of germs.
In this trial, the Infection Preventionist Led Best Practices Reminders aim to enhance these measures by using a computer program to predict possible infections. Early results suggest that using data in this way can help control infections without compromising patient safety.
Overall, this approach is well-received because it primarily involves changing hospital staff practices rather than introducing new treatments or medications to patients.12345Why are researchers excited about this trial?
Researchers are excited about this trial because it explores a new way to tackle hospital-acquired infections using an Infection Preventionist Led Best Practices Reminders approach. Unlike standard treatments that often focus on antibiotics or reactive measures after infections occur, this method proactively integrates a machine learning model to predict potential infection risks, particularly central line-associated bloodstream infections (CLABSI). By providing "EARLY" access to this predictive model, hospitals can enhance their infection prevention strategies, potentially reducing infection rates and improving patient outcomes. This approach represents a shift from traditional reactive treatments to proactive prevention, which may revolutionize how hospitals manage and prevent infections.
What evidence suggests that this method is effective for reducing hospital-acquired infections?
Research has shown that efforts to prevent infections in hospitals can greatly reduce the number of infections patients acquire. For example, studies have found that ensuring proper handwashing can cut infection rates by 40% to 70%. Using quality management tools (QMTs) also helps lower these infections in adults. Additionally, following set procedures for infection control and using specific practices have proven effective in reducing hospital-related infections. In this trial, hospitals will receive reminders about best practices, and some will gain early access to a prediction model to help keep infection rates down.12678
Who Is on the Research Team?
Chris Dale, MD, MPH
Principal Investigator
Swedish Medical Center
Are You a Good Fit for This Trial?
This trial is for hospitals looking to reduce central line-associated bloodstream infections (CLABSIs). Hospitals must be willing to implement a machine learning model and follow best practice reminders led by Infection Preventionists.Inclusion Criteria
Exclusion Criteria
Timeline for a Trial Participant
Screening
Participants are screened for eligibility to participate in the trial
Intervention
Deployment of a machine learning model to predict CLABSI risk and provide targeted education and interventions
Follow-up
Participants are monitored for safety and effectiveness after intervention
Interim Analysis
Interim analysis to evaluate efficacy or harm using O'Brien-Fleming group-sequential design
What Are the Treatments Tested in This Trial?
Interventions
- Infection Preventionist Led Best Practices Reminders
Find a Clinic Near You
Who Is Running the Clinical Trial?
Swedish Medical Center
Lead Sponsor
Providence Health & Services
Collaborator