Clinical Decision Support for Respiratory Infections

Recruiting at 3 trial locations

Overseen By Sumaiya Tasneem

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: NYU Langone Health

Disqualifiers: Chronic lung disease, Immunosuppression, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 1

347 Participants Needed

Trial Summary

Do I need to stop my current medications to join the trial?

The trial information does not specify whether you need to stop taking your current medications. It focuses on evaluating a tool for prescribing antibiotics for respiratory infections.

What data supports the effectiveness of the iCPR system treatment for respiratory infections?

Research shows that clinical decision support systems (CDSSs), like the iCPR system, can improve the use of antibiotics for respiratory infections by helping healthcare professionals make better prescribing decisions, leading to more appropriate and reduced antibiotic use. ^{1 2 3 4 5}

How is the iCPR system treatment different from other treatments for respiratory infections?

The iCPR system is unique because it integrates clinical prediction rules (CPRs) into an electronic health record, allowing registered nurses to lead the decision-making process for treating low-acuity acute respiratory infections. This approach aims to reduce inappropriate antibiotic prescribing, which is a common issue with traditional physician-driven models. ^{6 7 8 9 10}

What is the purpose of this trial?

This study evaluates the effects of a novel integrated clinical prediction tool on antibiotic prescription patterns of nurses for acute respiratory infections (ARIs). The intervention is an EHR-integrated risk calculator and order set to help guide appropriate, evidence-based antibiotic prescriptions for patients presenting with ARI symptoms.

Research Team

Devin Mann, MD

Principal Investigator

NYU Langone Health

Eligibility Criteria

This trial is for patients aged 3-70 with sore throat and 18-70 with cough, seen at participating clinics. Nurses prescribing treatment must work at least half-time, be licensed, use the clinic's EHR system regularly, and see a sufficient number of patients to maintain skills.

Inclusion Criteria

I am between 3-70 years old with a sore throat, or 18-70 with a cough.

I visited a clinic for a cough or sore throat.

My clinic employs at least one full-time registered nurse.

Exclusion Criteria

I do not have chronic lung disease or a weakened immune system.

I can use English software for self-monitoring without issues.

I am unable or unwilling to give my consent for participation.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Training and Implementation

Clinic personnel receive online and in-person training on the iCPR tool, followed by implementation of the intervention

4-6 weeks

1 in-person training session, 1 follow-up training session

Evaluation

Evaluation of the iCPR tool's effectiveness in reducing antibiotic prescriptions and its adoption by nurses

6 months

Follow-up

Participants are monitored for safety and effectiveness after treatment

12 months

Treatment Details

Interventions

• iCPR system

Trial Overview The study tests an integrated clinical prediction tool in electronic health records (EHR) that helps nurses decide when antibiotics are needed for acute respiratory infections. It aims to ensure prescriptions are evidence-based.

Participant Groups

Group I: iCPR group Experimental Treatment 1 Intervention

Clinic personnel (Providers and Nurses) will receive online training that includes: 1) an overview of the project; 2) iCPR workflows including triage; 3) CPR component review and risk categories; 4) history and physical examination components of the CPRs. The online training will be followed by in-person training to reinforce the online training and teach additional skills. In-person training sessions led by study team will last approximately 60 minutes, and consist of four basic components: 1) a review of the iCPR ARI protocol and tools; 2) on-screen walk-throughs of common scenarios employing the new tools; 3) physical examination technique practice with simulated patients; A 60-minute in-person follow-up nurse training will take place 4-6 weeks after implementation of the intervention.

Group II: Control no intervention group Active Control 1 Intervention

standard care will continue as usual.

Who Is Running the Clinical Trial?

NYU Langone Health

Lead Sponsor

Trials

1,431

Recruited

838,000+

National Institute of Allergy and Infectious Diseases (NIAID)

Collaborator

Trials

3,361

Recruited

5,516,000+

Findings from Research

The TREAT decision support system significantly improved the rate of appropriate antibiotic treatment in patients with suspected bacterial infections, achieving 70% compliance compared to 57% by physicians, while also using less broad-spectrum antibiotics and reducing costs by half.

In a randomized trial involving 2326 patients, intervention wards using TREAT had a higher rate of appropriate treatment (73% vs. 64%) and showed significant reductions in hospital stay length and total antibiotic costs, highlighting its efficacy in optimizing antibiotic use and minimizing resistance.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Improving empirical antibiotic treatment using TREAT, a computerized decision support system: cluster randomized trial. Paul, M., Andreassen, S., Tacconelli, E., et al.[2022]

References

1. England pubmed.ncbi.nlm.nih.gov

Improving empirical antibiotic treatment using TREAT, a computerized decision support system: cluster randomized trial. [2022]

2. England pubmed.ncbi.nlm.nih.gov

Clinical algorithm reduces antibiotic use among children presenting with respiratory symptoms to hospital in central Vietnam. [2023]

3. Switzerland pubmed.ncbi.nlm.nih.gov

E-Health Tools to Improve Antibiotic Use and Resistances: A Systematic Review. [2020]

4. United States pubmed.ncbi.nlm.nih.gov

Implementation of a computerized patient advice system using the HELP clinical information system. [2020]

5. England pubmed.ncbi.nlm.nih.gov

Decision support during electronic prescription to stem antibiotic overuse for acute respiratory infections: a long-term, quasi-experimental study. [2018]

6. United States pubmed.ncbi.nlm.nih.gov

Creating clinical decision support systems for respiratory medicine. [2020]

7. United States pubmed.ncbi.nlm.nih.gov

Combining decision support methodologies to diagnose pneumonia. [2019]

8. England pubmed.ncbi.nlm.nih.gov

Evaluation of a computerized diagnostic decision support system for patients with pneumonia: study design considerations. [2019]

9. England pubmed.ncbi.nlm.nih.gov

Reducing prescribing of antibiotics for acute respiratory infections using a frontline nurse-led EHR-Integrated clinical decision support tool: protocol for a stepped wedge randomized control trial. [2023]

10. Netherlands pubmed.ncbi.nlm.nih.gov

A probabilistic and decision-theoretic approach to the management of infectious disease at the ICU. [2019]