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.
The disconnect: Data exists, but integration and utilization lags
More than a billion people use sensor-based DHTs to generate health data on glucose levels, daily activity, sleep patterns, and a myriad of other health aspects strongly correlated with T2D and common comorbidities. Yet valuable insights derived from this data are underutilized in development and post-market settings to inform product differentiation at the cost of access to better patient outcomes.
Beyond this limited use, the lack of consistent integration with electronic health records (EHRs) means digital health technologies (DHTs) remain disconnected from the broader healthcare ecosystem. Sensor data’s full potential is untapped without frameworks to integrate PGHD into clinical research, care plans, value-based care arrangements, and budget impact models.
Angie Kalousek-Ebrahimi, senior director of Lifestyle Medicine at Blue Shield of California, highlights the importance of sensor data in optimizing T2D care, saying, “CGMs and wearables empower consumers with actionable health insights, yet the broader healthcare system has not fully leveraged these data streams to drive better outcomes and cost savings. To truly benefit, DHTs have a meaningful opportunity to establish their value by improving patient engagement and demonstrating measurable cost reductions.”
One of the most striking examples of the consequences of this data disconnect is the rise of GLP-1 receptor agonists. These medications have surged in popularity, fueled by high-profile marketing campaigns. But how do we determine which patients truly benefit? Without CGM data and other PGHD sources measuring outcomes that matter to patients and avoid unintended consequences, costly medical products may be prescribed without evidence that they will improve individual outcomes, leading to higher overall healthcare costs and scarcity of the drugs for those who could most benefit. Given the rapid adoption and rising costs of GLP-1s, payors, and providers must use real-world data to determine treatment effectiveness and prevent unnecessary spending that does not return to patients.
The path forward: Proving value through data
Pharmaceutical companies and innovators developing new therapies face the challenge of proving efficacy and demonstrating value beyond the stiff competition in an increasingly crowded market that now includes compounded products. In an increasingly challenging federal policy landscape, where tariff proposals could increase costs of supplies and drugs or coverage expansion could rein in costs and boost access, a more personalized approach to research and treatment is more important now than ever before.
Sensor-generated data enables stakeholders to show, with precision, how their treatments improve outcomes and reduce costs. The evidence-generation process can be more cost-efficient than traditional clinical trials, as digital health tools reduce the cost of evidence collection while delivering more actionable insights. Real-time sensor data helps manufacturers and payors assess treatment impact, optimize drug pricing, and ensure cost-effective care. This shift to targeted, data-driven interventions will reduce healthcare costs and improve outcomes.
The path forward for sensor-based data integration
A unified effort is essential to unlock the potential of DHTs and PGHD to improve care and reduce costs. Leaders across industries—pharmaceuticals, medical devices, digital health, payors, health systems, and regulators—must work together to collaborate on tangible tools and actionable recommendations.
We have the opportunity to change the trajectory of data-driven decision making in T2D but fast action and cross-disciplinary collaboration will be the key to improving our healthcare system.
Gabrielle Goldblatt is the Partnerships Lead, Care & Public Health at the Digital Medicine Society
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