The burden of chronic disease adversely affects the lives of tens of millions of patients and their families. Their lives are plagued by frequent exacerbations, often resulting in hospitalizations.
Improving the lives of such patients is challenging because many of them have multiple chronic diseases and because care teams often don’t have the timely information they need to detect that early signs of exacerbation when it would be possible reverse the process with a relatively modest intervention. Many patients with CHF, for example, often are admitted to the hospital without having had any contact with their clinicians for weeks or months. We know that those admissions are often preceded by subtle changes days or more in advance of the time the patient becomes symptomatic.
Sentrian was formed to address these challenges, bridging the gap between patients and clinicians when the patient is home and away from the care team. Patients are supplied with monitoring devices that collect and transmit data relevant to the patient’s condition, such as heart rate, blood pressure, oxygen levels and activity, to make it available for evaluation. The monitoring data, coupled with other health care information, provides a set of “big data” that can be analyzed to detect patterns to identify which patients are likely to deteriorate with enough warning to allow the care team to change the course.
Using big data analytic techniques, augmented by clinician-directed machine learning, the Sentrian platform is intended to learn from experience to refine health deterioration rules for specific patients to reduce false positives and improve healthcare outcomes for each patient. Providing actionable information to the clinical team as well as the patient and the patient’s family improves the ability of all to improve management of the patient’s health. The patient and family get an improved quality of life with fewer interruptions. Health care costs are simultaneously controlled by reducing avoidable hospitalizations.