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

Trial Summary

What is the purpose of this trial?

The goal of this clinical trial is to learn if a computer alert can aid clinicians in identifying patients with a genetic type of high cholesterol, called Familial Hypercholesterolemia. The main question it aims to answer is whether the computer alert increases recognition of this high cholesterol disorder.

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 data supports the effectiveness of the treatment Alert-based computerized decision support for high cholesterol?

Research suggests that alert-based clinical decision support systems can change clinician behavior more effectively than systems that require active initiation by users. This implies that automated alerts, like the EPIC Best Practice Advisory, may help improve the management of conditions such as high cholesterol by prompting timely interventions. ^{1 2 3 4 5}

Is the computerized decision support system safe for humans?

The computerized decision support system, including alert-based tools like the Best Practice Advisory (BPA), is generally considered safe as it helps prevent inappropriate prescribing and improves patient care by alerting healthcare providers to potential medication issues. However, excessive alerts can lead to 'alert fatigue,' where important alerts might be ignored, so optimizing these alerts is crucial for maintaining safety. ^{1 4 5 6 7}

How is the Alert-based CDS tool treatment for high cholesterol different from other treatments?

The Alert-based CDS tool is unique because it uses automated alerts within electronic medical records to help doctors make better decisions about treating high cholesterol. Unlike traditional treatments that rely on medication or lifestyle changes alone, this tool provides real-time guidance to healthcare providers, potentially improving adherence to treatment guidelines and reducing the risk of alert fatigue by optimizing the alert system. ^{1 2 8 9 10}

Eligibility Criteria

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

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

Treatment Details

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.

Participant Groups

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+

Findings from Research

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]

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]

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]

References

1. United States 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. [2023]

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

Electronic alerts versus on-demand decision support to improve dyslipidemia treatment: a cluster randomized controlled trial. [2008]

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

A randomized trial of the effectiveness of on-demand versus computer-triggered drug decision support in primary care. [2021]

4. England pubmed.ncbi.nlm.nih.gov

Retrospective descriptive assessment of clinical decision support medication-related alerts in two Saudi Arabian hospitals. [2022]

5. Ireland pubmed.ncbi.nlm.nih.gov

Best practices for preventing malfunctions in rule-based clinical decision support alerts and reminders: Results of a Delphi study. [2021]

6. England 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. [2022]

7. Canada pubmed.ncbi.nlm.nih.gov

Inappropriate prescribing practices: the challenge and opportunity for patient safety. [2019]

8. Ireland pubmed.ncbi.nlm.nih.gov

Pilot study to validate a computer-based clinical decision support system for dyslipidemia treatment (HTE-DLP). [2022]

9. Canada pubmed.ncbi.nlm.nih.gov

Usability Testing and Adaptation of the Pediatric Cardiovascular Risk Reduction Clinical Decision Support Tool. [2023]

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

Clinical Decision Support Systems and Prevention: A Community Guide Cardiovascular Disease Systematic Review. [2023]