Background:
Patients managing complex medication regimens often struggle with adherence, symptom tracking, and communication with providers. These challenges are especially pronounced in polypharmacy, psychiatric care, and chronic disease management, where dose changes and symptom patterns are poorly captured between visits. Fragmented data and recall bias limit effective treatment adjustments and complicate prior authorization processes.

Objective:
To develop and evaluate DoseMate, a mobile application that enables patients to track medication regimens and daily symptoms while generating structured, exportable data for clinician use. The platform incorporates AI-powered analytics to help surface trends, contextualize symptom fluctuations, and support shared decision-making.

Methods:
DoseMate allows patients to input exact dosages, monitor symptoms in real time, and receive reminders tied to variable schedules. The system converts medication doses to real-world units (e.g., mg or pill fraction) and flags discrepancies in adherence or response. Machine learning algorithms will be trained on patient data to identify patterns and propose insights for provider review. Integration with external EHR or prior authorization systems is under investigation. The project is currently being advanced through the NSF I-Corps program, with a focus on market discovery and commercialization strategy. As part of this effort, we are conducting 20 stakeholder interviews and delivering targeted business pitches to refine product-market fit.

Results:
Early user interviews suggest strong interest among patients and providers in improving treatment adherence, enhancing symptom documentation, and streamlining administrative workflows. Data collection and model development are ongoing.

Conclusion:
DoseMate may offer a scalable, user-friendly platform to address the growing need for personalized, data-driven support in outpatient medication management. By enabling real-time tracking and AI-generated insights, DoseMate has the potential to improve adherence, reduce provider burden, and enhance treatment precision across a variety of chronic conditions.