Timely Interventions for Children with ADHD through Web-Based Monitoring Algorithms

Area of Science:

• Pediatric Health
• Digital Health
• Behavioral Science

Background:

• Remote patient monitoring offers potential for continuous health tracking.
• Children with Attention-Deficit/Hyperactivity Disorder (ADHD) often require frequent monitoring and care adjustments.
• Existing methods for detecting adverse events in pediatric ADHD patients can be reactive.

Purpose of the Study:

• To evaluate an automated trigger algorithm for detecting potential adverse events in children with ADHD during remote monitoring.
• To assess the impact of triggered alerts on clinical decision-making and care plan modifications.
• To compare ADHD symptom severity in patients with and without triggered alerts.

Main Methods:

• An automated trigger algorithm was developed using parent-reported ADHD rating scales.
• The algorithm was embedded within an electronic patient monitoring system for remote data collection.
• Clinician alert resolution outcomes were categorized, and Vanderbilt ADHD rating scale scores were compared between alerted and non-alerted patients.

Main Results:

• 8% of parent reports (146/1738) triggered alerts for 98 patients.
• 76% of alerts (111/146) required immediate clinician review.
• 46% of actionable alerts (31/68) resulted in a care plan change before the next scheduled visit.
• Alerted patients showed significantly worsened ADHD severity compared to non-alerted patients (β = 5.8, p < 0.001).

Conclusions:

• The automated trigger algorithm effectively identified potential adverse events in children with ADHD.
• The system facilitated timely interventions and care plan adjustments between clinical visits.
• This technology enhances remote patient monitoring for pediatric ADHD management.