A Comprehensive Self-Medication Management System to Reduce Medication Errors Among People Living With Dementia:...

Area of Science:

• Gerontology and Geriatric Medicine
• Biomedical Engineering
• Artificial Intelligence in Healthcare

Background:

• Medication adherence is a significant challenge for people living with dementia (PLwD) and their caregivers.
• Current adherence tools often require manual interaction, lack clinical integration, and are unsuitable for individuals with cognitive decline.
• The HiDO Home Care System (HCS) is an AI-enabled self-medication device (SMD) designed to automate medication management.

Purpose of the Study:

• To evaluate the feasibility, usability, and performance of the HCS for at-home medication management in PLwD and caregiver dyads.
• To assess device setup, medication dispensing accuracy, task completion efficiency, and user satisfaction.

Main Methods:

• A pooled analysis usability study involving 35 caregiver-patient dyads at Rush University Medical Center.
• Participants completed device setup, automated medication dispensing, and simulated medication use with dual-camera verification.
• Quantitative measures included setup time, dispensing time, success rates, and System Usability Scale (SUS) scores; qualitative measures captured caregiver perceptions.

Main Results:

• All 35 dyads successfully completed dispensing tasks, with average first attempt times of 1:41 minutes.
• The HCS demonstrated consistent accuracy across users, achieving a mean SUS score of 70.2, indicating above-average usability.
• Caregivers reported potential stress reduction and suggested design improvements.

Conclusions:

• The HCS shows early feasibility, accuracy, and usability as an advanced SMD for PLwD, automating the medication safety chain.
• The device reduces caregiver workload and enhances patient safety and medication management.
• Further longitudinal studies are planned to assess clinical outcomes, caregiver burden, and cost-effectiveness.