Myasthenia gravis is a fluctuating disease. Symptoms shift across days and weeks, often resolving or evolving before the next clinic visit. By the time a patient sits in front of their neurologist, weeks of fatigue, flares, and functional changes have already passed, recalled imperfectly or not at all. Treatment decisions are made on partial information, disease activity is underestimated, and patients leave appointments feeling that what they actually live with did not make it into the room.

The HUMA MG platform was developed to address this. It enables patients to track MG-ADL scores, medications, and symptom changes continuously between visits, generating structured longitudinal data that clinicians can review alongside in-clinic assessment.

To understand whether patients can engage with the platform meaningfully on their own, HUMA MG was deployed at St George's University Hospitals NHS Foundation Trust in London. 66 patients were enrolled into a structured remote monitoring programme, and 43 completed an anonymous patient experience survey, a 65% response rate.

The results address the first question any clinician asks about a digital tool: will my patients actually use it. 89% rated the app easy or very easy to use. 82% reported being confident or very confident using smartphone apps. 83.7% used the platform independently, without assistance from family or care teams. 65.1% reported no technical difficulties. Module completion reached 97.3%, and 93% found the MG-ADL questionnaire straightforward to complete remotely. 72.1% reported moderate-to-high benefit for symptom monitoring, 60.5% reported at least moderate benefit in communicating symptoms to their healthcare team, 86% wished to continue using the platform beyond the study period, and 69.8% would recommend HUMA MG to other patients with MG.

Patients also pointed to where remote monitoring needs to evolve. They asked for clearer feedback from their clinical teams when data is reviewed, scoring frameworks that better reflect individual variation in MG presentation, and expanded support including education, community, and more direct routes to care.

These findings are now shaping the development of an extended MG-ADL framework, designed to capture a broader range of patient-reported symptoms and improve the clinical utility of remote data.

For clinicians, the implication is straightforward. Patients with MG can engage with structured remote monitoring at high rates, complete validated assessments independently, and derive meaningful benefit from doing so. The recall gap is not inevitable. With the right platform, it can be closed.