Timely Ordering of Pharmacogenetic Testing

Condition(s) studied

Investigational drug(s) / intervention(s)

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.

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.

Eligibility

Primary outcome measure(s)

• Proportion of Patients with Pharmacogenetic Testing — Day 1 to 3 months
  The primary outcome will be the proportion of patients with pharmacogenetic testing offered among those who receive a medication with a pharmacogenetic indication within three months of prediction time. Testing must be offered prior to receipt of the first targeted medication.

Trial sites (1)

More The Hospital for Sick Children trials in Canada

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