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Timely Ordering of Pharmacogenetic Testing

NCT06902688 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 275

Last updated 2026-03-05

No results posted yet for this study

Summary

The goal of this trial is to learn if a machine learning (ML) model can help optimize drug therapy in the pediatric population. The main question\[s\] it aims to answer are whether a machine learning model predicting receipt of a targeted medication within the next three months:

* Increases the offering of pharmacogenetic testing prior to receipt of a targeted medication
* Increases the number of patients with pharmacogenetic results prior to receipt of a targeted medication
* Increases the number of patients who have alteration in medication choice or dose based on pharmacogenetic results

This trial only focuses on the prediction and provision of participants with a high-risk of receiving a medication with a pharmacogenetic indication in the next three months.

Conditions

  • Machine Learning
  • Prediction Models
  • Pediatrics
  • Precision Medicine

Interventions

OTHER

ML-based intervention

A ML-based model will predict and identify participants that are at high-risk of receiving a targeted medication within three months after their hospital admission date.

Sponsors & Collaborators

  • The Hospital for Sick Children

    lead OTHER

Principal Investigators

  • Lillian Sung, MD, PhD · The Hospital for Sick Children

Study Design

Allocation
NA
Purpose
SUPPORTIVE_CARE
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
6 Months
Max Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-06-10
Primary Completion
2026-06-30
Completion
2027-06-10

Countries

  • Canada

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06902688 on ClinicalTrials.gov