Blood pressure, body temperature, hemoglobin A1c levels and other biomarkers have been used for decades to track disease. While this information is essential for chronic disease management, these and other physiological measurements are typically only captured periodically, making it difficult to reliably detect significant changes early.
Additionally, biomarkers extracted from blood require uncomfortable blood draws, can be expensive to analyze, and again, are not always timely.
Historically, the continuous monitoring of an individual’s vital signs meant they had to be hospitalized. But that’s no longer true. Digital biomarkers, collected from wearable sensors or through a device, offer healthcare providers a wealth of traditional and new data to accurately monitor and even predict a patient’s disease trajectory.
With cloud-based servers and sophisticated, yet inexpensive, sensors both on and off the body, patients can be monitored at home more effectively than in the hospital, especially when sensor data are analyzed with artificial intelligence (AI) and machine learning technology. .
Opportunities for digital biomarkers
A major opportunity for digital biomarkers is to treat neurodegenerative diseases such as mild cognitive impairment, Alzheimer’s disease and Parkinson’s disease.
Neurodegenerative diseases are a major target for the development of digital biomarkers due to a lack of easily accessible indicators that can help providers diagnose and manage these conditions. A definitive diagnosis of Alzheimer’s disease today, for example, typically requires positron emission tomography (PET), magnetic resonance imaging (MRI), or other imaging studies, which are often expensive and not always accurate or reliable.
Cost savings and other benefits
Digital biomarkers have the potential to unlock significant value for healthcare providers, businesses, and most importantly, patients and families, by detecting and slowing the development of these diseases.