Myasthenia gravis (MG) is a chronic autoimmune neuromuscular disease characterized by muscle weakness and fatigue. Other common symptoms of MG include eye drooping (ptosis), double vision (diplopia), difficulty in chewing, and difficulty in speaking (dysarthria). MG symptoms are typically evaluated by neuromuscular experts through in-person neurologic examinations. These assessments are time-consuming, require disease expertise, and capture only a snapshot in time. Motivated by this, we developed a multi-modal digital health technology (DHT) called BioDigit MG, for monitoring MG symptoms. BioDigit MG includes a set of tablet-guided speech and video-based assessments, electronic patient-reported outcomes relevant to MG, as well as a wearable sensor to measure physical activity and posture during activities of daily living. We assessed the real-world feasibility and acceptability of BioDigit MG by conducting a clinical study with 20 MG patients who used the developed DHT to collect data related to their symptoms. During the study, a total of 219 speech tasks and 119 videos were successfully collected by the DHT, achieving 100% reliability in data collection and transfer. To evaluate technology acceptance and usability, we conducted face-to-face interviews with 20 MG patients and 5 expert clinicians, each with over 13 years of practice. The patient and clinician participants found the DHT highly effective, easy to use, and well-suited to their needs. Efficient and reliable data transfer capabilities of BioDigit MG ensures that patient-generated data is promptly and securely delivered to healthcare providers. This seamless data transmission enhances remote monitoring, allowing clinicians to receive timely insights into patient symptoms and facilitating better and long-term management of MG.