In this video interview, Mohammed Saeed, MD, PhD, chief medical officer of Solera Health, explores how AI models capable of analyzing continuous wearable data streams alongside broader patient information could detect subtle warning signs of deterioration that no clinician could identify alone.
In a recent video interview with Applied Clinical Trials, Mohammed Saeed, MD, PhD, chief medical officer of Solera Health, discussed how wearable devices and continuous remote monitoring are giving clinicians unprecedented visibility into patient health outside the clinic, enabling earlier interventions and more proactive care management. He opened by emphasizing that wearable data captures something fundamentally unavailable from traditional clinical encounters: a real-world picture of how a patient's health holds up to everyday stresses. That unique insight, he noted, is becoming increasingly central to identifying patients who need earlier support before their conditions worsen.
Saeed went on to explain how continuous remote monitoring is shifting care delivery from reactive to proactive, allowing care teams to spot early warning signs of deterioration and make timely adjustments well before a patient reaches the emergency room. Whether that means a simple medication change over the phone or a more urgent escalation, the ability to intervene early is, in his view, the core value proposition of remote monitoring.
He was equally candid about the obstacles that remain, pointing to information overload as one of the most pressing operational challenges. With wearables generating continuous, high-velocity data on top of already overwhelming EMR workloads, providers face a growing risk of alert fatigue. When false positives become routine, he warned, clinicians can become dangerously desensitized to alarms that genuinely matter.
Saeed also addressed the critical role of FDA clearance in establishing the provider trust necessary for wearables to become a genuine part of clinical care, and stressed that reimbursement models must evolve to reflect the real time providers invest in reviewing remote monitoring data. He closed with a forward-looking perspective on how AI could parse massive, multi-source data streams and detect patterns too subtle for human observation, including changes in speaking patterns as early predictors of heart failure, potentially reshaping clinical oversight across a wide range of conditions.
