Electronic Clinical Decision Support Tool for Atrial Fibrillation

(AF SWCRT-CDS Trial)

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Oregon Health and Science University

Must not be taking: Oral anticoagulants

Disqualifiers: Valvular disease, Pregnancy, Severe bleeding, others

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

Compensation: Varies

Number of Visits: Unknown

36 Participants Needed

Trial Summary

What is the purpose of this trial?

Atrial fibrillation (AF) is the most common arrhythmia in the world, with significant morbidity and mortality. With appropriate oral anticoagulation, the risk of stroke due to atrial fibrillation decreases by 64%. Although atrial fibrillation is commonly diagnosed and treated in the Emergency Department (ED), oral anticoagulation is significantly underprescribed. Underprescribing has been attributed to a lack of empowerment and deferral of prescribing to longitudinal care clinicians. Using a convergent parallel quantitative-qualitative design (mixed-methods), we propose a stepped-wedge cluster randomized trial design with the implementation of a clinical decision support (CDS) tool in adults with new-onset AF that are OAC-naïve and at significant risk for stroke. In parallel, we will use qualitative approaches to evaluate clinician facilitators and barriers to tool utilization as well as patient satisfaction and engagement with the tool.

Will I have to stop taking my current medications?

The trial does not specify if you need to stop taking your current medications. However, it focuses on patients who have not been prescribed oral anticoagulants in the last 3 months, so if you are currently on such medication, you may not be eligible.

What data supports the effectiveness of the Clinical Decision Support (CDS) tool treatment for atrial fibrillation?

Research shows that Clinical Decision Support (CDS) tools can improve patient care outcomes by helping healthcare providers make better decisions using real-time data. These tools have been effective in improving the management of various conditions, suggesting they could also enhance atrial fibrillation care. ^{1 2 3 4 5}

Is the Electronic Clinical Decision Support Tool for Atrial Fibrillation safe for humans?

Clinical decision support tools, like the one for atrial fibrillation, are generally considered safe as they help healthcare providers make better decisions by organizing and presenting health data. They are designed to prevent medication errors and adverse drug events, which can improve patient safety. ^{1 5 6 7 8}

How is the Clinical Decision Support tool treatment for atrial fibrillation different from other treatments?

The Clinical Decision Support (CDS) tool for atrial fibrillation is unique because it integrates with electronic health records to help healthcare providers follow complex guidelines for stroke prevention and anticoagulation therapy. Unlike traditional treatments, this tool aids in decision-making by organizing and presenting health data, improving adherence to guidelines and potentially enhancing patient outcomes. ^{1 9 10 11 12}

Research Team

Bory Kea, MD, MCR

Principal Investigator

Oregon Health and Science University

Eligibility Criteria

This trial is for adults over 18 with new-onset atrial fibrillation diagnosed in the emergency department, who haven't been prescribed oral anticoagulants recently. It's not for those with severe liver or kidney disease, recent major surgery, certain bleeding conditions, or if they're pregnant.

Inclusion Criteria

I was diagnosed with atrial or paroxysmal fibrillation between 1/11/2022 and 12/31/2025.

I am over 18 years old.

Exclusion Criteria

I have a heart valve problem.

I was hospitalized or had a stroke or death event at my first visit.

I have been prescribed blood thinners in the last 3 months or am managed by a blood thinner clinic.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Implementation

Implementation of the clinical decision support (CDS) tool in the Emergency Department for patients with new-onset atrial fibrillation

Stepped-wedge design over 4 years

Ongoing visits as per ED protocol

Evaluation

Evaluation of clinician experiences and patient satisfaction with the CDS tool

Concurrent with implementation phase

Follow-up

Participants are monitored for safety and effectiveness after CDS tool implementation

4 weeks

Treatment Details

Interventions

• Clinical Decision Support (CDS) tool

Trial Overview The study tests a Clinical Decision Support tool designed to help doctors decide when to prescribe blood thinners to prevent strokes in patients with atrial fibrillation. The trial randomly assigns periods of time when different EDs will start using the tool.

Participant Groups

Group I: Link-Out Experimental Treatment 1 Intervention

Clinicians will be trained on the web-based portal of the CDS tool and shown where the Link to the tool will be available in the EHR.

Group II: BPA + Link-out Experimental Treatment 1 Intervention

Clinicians will be trained on how a BPA is triggered when a patient is diagnosed with AF. The alert will pop up within the EHR with the Link-out to the web portal.

Group III: BPA + FHIR Experimental Treatment 1 Intervention

Instead of a link to the web-based portal, the BPA will contain a link to the FHIR-integrated CDS tool portal. FHIR will automatically pull EHR data about the patient into the CDS tool portal. Data include demographic information, comorbidities in the active problem list, past medical and surgical history, and social history. Clinicians will also receive training before the implementation of this step.

Who Is Running the Clinical Trial?

Oregon Health and Science University

Lead Sponsor

Trials

1,024

Recruited

7,420,000+

Findings from Research

Clinical decision support (CDS) tools integrated into electronic health records can significantly enhance patient care outcomes by providing real-time data analysis for clinicians during critical decision-making processes.

The implementation of CDS tools in a community hospital setting has shown positive outcome data, indicating their effectiveness in improving healthcare organization performance, especially as financial reimbursement models shift towards patient care outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical decision support. How CDS tools impact patient care outcomes. Anton, BB., Schafer, JJ., Micenko, A., et al.[2019]

The study involving 60 registered nurses demonstrated that clinical decision support (CDS) tools significantly improved the adoption of best practices and reduced care planning time compared to a control group, indicating their effectiveness in enhancing evidence-based decision-making.

While all CDS formats (text, text + graph, and text + table) showed similar adoption rates, the study found that RNs with higher numeracy and graph literacy performed better in terms of care planning time, suggesting that the effectiveness of CDS tools may vary based on individual skills.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Toward Meaningful Care Plan Clinical Decision Support: Feasibility and Effects of a Simulated Pilot Study. Keenan, GM., Lopez, KD., Yao, Y., et al.[2022]

A clinical decision support system (CDSS) helps healthcare providers by detecting potential drug interactions and errors, which can lead to improved patient safety and reduced clinical errors.

The implementation of a CDSS can save time and money while also decreasing morbidity and mortality rates, highlighting its effectiveness in enhancing patient care and supporting nursing informatics.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical decision support in critical care nursing. Lyons, A., Richardson, S.[2019]

Clinical decision support (CDS) tools significantly improve the prescription of appropriate medications and dosages, with a pooled risk ratio of 1.48 based on 6 studies, indicating they are effective at the point of medication ordering or dispensing.

CDS tools also enhance laboratory monitoring and treatment responses to abnormal test results, showing a pooled risk ratio of 1.40, but evidence for their effectiveness in reducing adverse drug events remains inconsistent, suggesting further research is needed.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Effect of Laboratory Test-Based Clinical Decision Support Tools on Medication Errors and Adverse Drug Events: A Laboratory Medicine Best Practices Systematic Review. Whitehead, NS., Williams, L., Meleth, S., et al.[2022]

References

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

Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF): A cluster randomized trial of a computerized clinical decision support tool. [2020]

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

Clinical decision support in electronic prescribing: recommendations and an action plan: report of the joint clinical decision support workgroup. [2018]

3. England pubmed.ncbi.nlm.nih.gov

A trial of automated safety alerts for inpatient digoxin use with computerized physician order entry. [2019]

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

Clinical decision support. How CDS tools impact patient care outcomes. [2019]

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

Toward Meaningful Care Plan Clinical Decision Support: Feasibility and Effects of a Simulated Pilot Study. [2022]

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

Clinical decision support in critical care nursing. [2019]

7. England pubmed.ncbi.nlm.nih.gov

The Effect of Laboratory Test-Based Clinical Decision Support Tools on Medication Errors and Adverse Drug Events: A Laboratory Medicine Best Practices Systematic Review. [2022]

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

Do computerized clinical decision support systems improve the prescribing of oral anticoagulants? A systematic review. [2021]

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

A clinical decision support tool for improving adherence to guidelines on anticoagulant therapy in patients with atrial fibrillation at risk of stroke: A cluster-randomized trial in a Swedish primary care setting (the CDS-AF study). [2019]

10. England pubmed.ncbi.nlm.nih.gov

An interrupted time series study of electronic health record clinical decision support for providers caring for patients with atrial fibrillation at increased stroke risk. [2023]

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

Clinical decision support for stroke prevention in atrial fibrillation (CDS-AF): Rationale and design of a cluster randomized trial in the primary care setting. [2022]

12. Netherlands pubmed.ncbi.nlm.nih.gov

Integrating Clinical Decision Support into EMR and PHR: a Case Study Using Anticoagulation. [2017]