MedPath

Evaluating the Efficacy of Pediatric Lipid Screening Alerts

Not Applicable
Completed
Conditions
Hypercholesteremia in Children
Hypercholesterolemia
Hypercholesterolemia, Familial
Hyperlipidemia in Children
Registration Number
NCT04118348
Lead Sponsor
Geisinger Clinic
Brief Summary

The purpose of the study is to evaluate prospectively the impact of different system alerts on the prescription of lipid panels to pediatric Geisinger patients (9-11 years old), as per the now-universal guidelines. This will help quantify the relative effectiveness of different alerts and combinations of alerts on provider prescribing behavior and patient uptake of screening.

Detailed Description

Patients who are eligible for this study will be randomized into one of four groups via an Epic electronic medical record (EMR) randomization algorithm run automatically at the time of the visit:

1. Control group (6-month delay before their providers will receive an alert)

2. Health maintenance topic (HMT)

3. Best practice alert (BPA)

4. Best practice alert and health maintenance topic (BPA+HMT)

Geisinger Health System will introduce Epic's Storyboard panel (a novel way of summarizing patient information in the EMR) approximately one month into this study. The analysis plan will therefore test for the potential impact of this change.

The providers will be prompted to discuss and order screening lipid study that is non fasting at the time of the visit with the patient, based on the alerts above. Some families will have an alert in their MyGeisinger portal stating that a health maintenance test is due and to discuss with their provider.

Outcomes will be reviewed and classified as followed,

Outcomes will include lipid screening orders by providers (yes/no) and screening completions by patients (yes/no). The following descriptive results will also be provided:

1. Lipid screening ordered

2. Lipid screening ordered and completed

3. Lipid screening ordered but not completed

4. Lipid screening declined with reason why

5. Alert not acted on at all

Analysis will account for the nesting of patients within providers; this will include provider as a random effects variable in a series of multilevel binomial logistic regression models, to account for potential correlation with patients. If the intraclass correlation coefficient is low, only the patient-level logistic regression models will be conducted. In the first model, the passive control will serve as the reference group, to test whether each of the active alert conditions have a significant impact on the outcomes. In the second model, the BPA-only condition will serve as the reference group, to test whether HMT and BPA+HMT offer significant improvements in performance. Finally, the third model will use the HMT-only condition as the reference, to test whether BPA+HMT has a significantly greater impact on the outcomes. Storyboard X Condition interactions will be tested within the models, and if any are significant, the series of models will be conducted separately on patients prior to, and after, implementation of Storyboard in Epic, to test whether and how results replicate in the different contexts.

Recruitment & Eligibility

Status
COMPLETED
Sex
All
Target Recruitment
13340
Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

Study Type
INTERVENTIONAL
Study Design
FACTORIAL
Primary Outcome Measures
NameTimeMethod
Lipid Panel Order1 day

Provider ordered a lipid panel to an eligible patient during the patient's first visit within the study period (binary variable).

Lipid Panel Screening1 week

Patient completed a lipid panel screening within seven days of the patient's first visit (binary variable). This screening is not linked to any order made at the patient's first visit (i.e., Outcome Measure 1).

Secondary Outcome Measures
NameTimeMethod

Trial Locations

Locations (1)

Geisinger Health System

🇺🇸

Danville, Pennsylvania, United States

Geisinger Health System
🇺🇸Danville, Pennsylvania, United States

MedPath

Empowering clinical research with data-driven insights and AI-powered tools.

© 2025 MedPath, Inc. All rights reserved.