Computerized Decision Support for High Cholesterol

(FH-ALERT Trial)

Overseen By Gregory Piazza, MD, MS

Age: Any Age

Sex: Any

Trial Phase: Academic

Sponsor: Brigham and Women's Hospital

Disqualifiers: Familial hypercholesterolemia diagnosis

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

Compensation: Varies

Number of Visits: Unknown

450 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests whether a computer alert (alert-based computerized decision support) can help doctors recognize Familial Hypercholesterolemia (FH), a genetic type of high cholesterol. The trial aims to determine if the alert assists doctors in identifying this condition in patients who might otherwise remain undiagnosed. Patients visiting specific clinics with certain cholesterol-related markers may qualify for this trial. The trial consists of two parts: initially, doctors will not receive alerts, but later, they will receive on-screen notifications about possible FH cases. As an unphased trial, this study offers patients the chance to contribute to innovative research that could enhance early detection of FH.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What prior data suggests that this computerized decision support is safe?

Research shows that doctors generally favor the alert-based computerized decision support tool. They find it helpful for identifying patients with familial hypercholesterolemia, a hereditary form of high cholesterol. Studies indicate that the tool integrates well into the healthcare routine and has been refined based on doctors' feedback.

No reports of harm or negative effects have emerged from using this computer alert. It appears on a doctor's computer screen to assist in identifying patients who might have this condition. As a software tool rather than a drug or medical device, it presents minimal safety concerns, making it a safe option for both patients and healthcare providers. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the alert-based computerized decision support system for high cholesterol because it promises a new way to identify Familial Hypercholesterolemia (FH) early. Unlike traditional treatments that focus on managing cholesterol levels with medications like statins, this system enhances the diagnostic process by using real-time alerts in electronic health records to notify clinicians of possible FH cases that might otherwise go unnoticed. By improving early detection, this method could lead to earlier interventions and better health outcomes for patients, potentially reducing the risk of heart disease related to high cholesterol.

What evidence suggests that this computerized decision support is effective for identifying Familial Hypercholesterolemia?

This trial will evaluate the effectiveness of an alert-based computerized decision support tool in identifying and managing familial hypercholesterolemia (FH). Research has shown that computerized alerts can help doctors identify patients with FH, a genetic form of high cholesterol. One study found that using these alerts in electronic health records increased the number of FH diagnoses, allowing doctors to catch the condition more often. Doctors generally support these tools because they make it easier to spot FH during regular check-ups. Another study showed that these alerts can also help manage cholesterol levels better by guiding treatment decisions. Overall, computerized alerts have shown promise in improving the detection and treatment of high cholesterol related to FH. ^{1 2 3 4 6}

Are You a Good Fit for This Trial?

This trial is for clinicians who manage patients with high cholesterol. It aims to see if a computer alert helps in recognizing Familial Hypercholesterolemia, a genetic disorder causing high cholesterol.

Inclusion Criteria

Seen in the BWH Endocrinology, Cardiovascular Medicine, and Primary Care Clinics

Dutch Lipid Clinic Network score of at least 3 points

Exclusion Criteria

A Familial Hypercholesterolemia diagnosis already documented in the EHR medical history, visit history, or problem list

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Pre-Alert

In the Pre-Alert phase of 6 months, patients meeting enrollment criteria will be identified but the clinician will not be notified regarding the possible diagnosis of Familial Hypercholesterolemia.

6 months

Alert

In the Alert phase of 18 months, an on-screen alert through the Electronic Health Record will notify the ambulatory care clinician of record that the patient has a 'definite,' 'probable,' or 'possible' diagnosis of FH but has not been recognized as such.

18 months

Follow-up

Participants are monitored for safety and effectiveness after treatment

6 months

What Are the Treatments Tested in This Trial?

Interventions

• Alert-based computerized decision support

Trial Overview The intervention being tested is an alert-based computerized decision support system designed to assist clinicians in identifying Familial Hypercholesterolemia among patients.

How Is the Trial Designed?

Group I: Alert Experimental Treatment 1 Intervention

In the Alert phase of 18 months, an on-screen alert through the Electronic Health Record will notify the ambulatory care clinician of record that the patient has a "definite," "probable," or "possible" diagnosis of FH but has not been recognized as such.

Group II: Pre-Alert Active Control 1 Intervention

In the Pre-Alert phase of 6 months, patients meeting enrollment criteria will be identified but the clinician will not be notified regarding the possible diagnosis of Familial Hypercholesterolemia.

Who Is Running the Clinical Trial?

Brigham and Women's Hospital

Lead Sponsor

Trials

1,694

Recruited

14,790,000+

Published Research Related to This Trial

A pediatric cardiovascular disease (CVD) risk factor tool was positively received by clinicians, indicating its potential to improve the evaluation and prevention of CVD in children and teens.

Feedback from pediatricians led to important revisions for the tool, such as enhancing user-friendliness on mobile devices and refining recommendations, which are crucial for effective clinical decision support.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Usability Testing and Adaptation of the Pediatric Cardiovascular Risk Reduction Clinical Decision Support Tool. Williams, PA., Furberg, RD., Bagwell, JE., et al.[2023]

In a study of 506 clinical decision support (CDS) alerts over 14 months, 78% of prescribers modified their orders in response to alerts designed to prevent high-risk medication prescriptions, indicating a high overall action rate.

The study revealed that relying solely on override rates does not accurately reflect the effectiveness of CDS alerts, highlighting the importance of ongoing evaluation and improvement of these alerts to enhance patient safety.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Impact of an electronic health record alert on inappropriate prescribing of high-risk medications to patients with concurrent "do not give" orders. Smith, K., Durant, KM., Zimmerman, C.[2022]

The study at Jurong Health Campus showed a 59.6% reduction in interruptive Best Practice Advisory (BPA) alerts after implementing optimization strategies, which significantly improved clinician response rates to alerts.

Despite increasing the number of unique BPAs from 54 to 360, the optimized alerts led to a 74% reduction in alerts from seven specific BPAs, saving an estimated 3600 hours of provider time annually and enhancing overall alert compliance.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Optimizing Best Practice Advisory alerts in electronic medical records with a multi-pronged strategy at a tertiary care hospital in Singapore. Ng, HJH., Kansal, A., Abdul Naseer, JF., et al.[2023]

Citations

1. clinicaltrials.gov clinicaltrials.gov/study/NCT06743659

Study Details | NCT06743659 | Computerized Decision ... FH-ALERT is a single-center, time-series trial of a patient- and provider-facing EPIC BPA (alert-based CDS tool) to increase diagnosis, LDL-C lowering, and ...

2. sciencedirect.com sciencedirect.com/science/article/pii/S1933287425005045

The Impact of Electronic Health Messaging on Diagnosis ... ... data from the Epic EHR at the University of ... Our results align with a recent study showing that a clinical decision support tool ...

3. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC10305024/

Clinician Perspectives on Clinical Decision Support for ... Overall, clinicians favored implementing the FH CDS in clinical practice and provided feedback that led to iterative refinement of the tool.

4. researchgate.net researchgate.net/figure/a-The-final-best-practice-alert-or-BPA-deployed-in-the-EHR-b-The-final-in-basket_fig1_343162508

(a) The final best practice alert or BPA deployed in the EHR ... 13,14 Bangash et al. investigated integrating a clinical decision support tool for familial hypercholesterolemia into the workflow of healthcare providers.

5. clinicaltrial.be clinicaltrial.be/fr/details/37830?active_not_recruiting=1&completed=0&enrolling_by_invitation=1&only_active=0&only_eligible=0&only_recruiting=0&per_page=100

Randomized Controlled Trial of Alert-Based Computerized ... The following single-center, 400-patient, randomized controlled trial of an EPIC Best Practice Advisory (BPA; alert-based computerized decision ...

6. researchgate.net researchgate.net/publication/392106854_Translating_Evidence-Based_Guidelines_Into_Clinical_Decision_Support_Tools_to_Improve_Identification_and_Management_of_Familial_Hypercholesterolemia

Translating Evidence-Based Guidelines Into Clinical ... A Clinical Decision Support Tool for Familial Hypercholesterolemia Based on Physician Input. Article. Full-text available. Jun 2018. Ali A ...