
By GABRIELLE GOLDBLATT
Highly relevant, high-resolution data streams are essential to high-stakes decision making across industries. You wouldn’t expect an investment banker making deals without full market visibility or a grocery store to stock shelves without data on what’s selling and what’s not—so why are we not leaning more into data-driven approaches in healthcare?
Sensor-based measures, data collected from wearables and smart technologies, often continuously and outside the clinic, can drive more precise and cost-effective treatment strategies. Yet, in many cases, they’re not used to the fullest potential – either because they’re not covered by insurance or they’re treated as an add-on rather than an integral input to disease management. As a result, we lack sufficient clarity of the true value of treatments, making it difficult to discern which are high quality and which drive up the already sky-high cost of healthcare in the U.S.
Take type 2 diabetes (T2D), for example, which impacts upwards of 36 million Americans. Many people with diabetes also face comorbidities like cardiovascular disease, obesity, and kidney complications, which increase treatment complexity and costs. The range of treatments available to manage and treat T2D has grown significantly in recent years, from established therapies like metformin and insulin to newer options like virtual care programs and GLP-1 receptor agonists, which offer benefits that may extend to comorbidities.
This expanded treatment landscape promises to improve the standard of care, but it also makes it difficult for treatment options to stand out in an increasingly crowded market. This leads to treatment gaps, worsening comorbidities, and an annual burden of over $400 billion on the healthcare system.
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