For more than a century, healthcare has been built around episodic encounters: an annual visit, a blood pressure reading in clinic, a lab draw every few months. As Dr. Ami Bhatt, Chief Innovation Officer at the American College of Cardiology, notes, that “once-a-year snapshot” rarely reflects who a patient truly is or how their health is changing in real time.
Continuous sensing technologies—from smart rings to glucose monitors—are challenging this paradigm. Instead of a single “normal” value like a 98.6°F temperature, these tools construct a personalized baseline for each individual, grounded in weeks and months of data. The deviation from that baseline, not a generic threshold, becomes the signal that something is changing.
Tom Hale, CEO of Oura Health, describes this shift through the company’s “symptom radar,” which combines temperature, heart rate variability, and resting heart rate to warn users when they may be getting sick—often days before symptoms peak. The importance of that information is not abstract; it’s behavioral. It gives people time to rest, hydrate, or adjust plans and potentially avert a full-blown illness.
The same logic is extending into chronic conditions, particularly in cardiometabolic and kidney disease. Health is no longer a series of isolated encounters; it is a continuous narrative that needs to be understood over time.
A persistent concern in healthcare is not a lack of data but a glut of it. Clinicians face inboxes full of numbers they don’t have time to interpret. As Dr. Lucien Ide, CEO of Rimidi, puts it, “I don’t know a single doctor saying, ‘If only I had more data, I would be a better clinician.’” The problem is not volume—it’s meaning.
This is where continuous monitoring must evolve from raw streams to curated insights. Early experiences in diabetes care with continuous glucose monitors (CGMs) are instructive. When Dexcom first tested CGM technology, they conducted a study where patients wore sensors that recorded continuous glucose levels. In the first week, the data was hidden. In the second week, the display was turned on. Within a day, patients changed their behavior on their own—adjusting food, activity, and routines—without new physician instructions, simply because they could finally “see” what was happening.
The lesson is clear: patients and clinicians don’t need more numbers; they need interpretable, contextualized guidance.
Clinicians increasingly expect wearables and remote monitoring companies to deliver something akin to a consult note, not a data dump: “Tell me what matters in clinical language, and filter out the rest.” This expectation is reshaping product design and business models.
As continuous sensing becomes more widespread, a deeper shift is emerging: from consumer engagement to patient agency. Patients are not just buying devices; they are learning to co-manage their health.
The panelists describe a new “architecture of participation,” in which patients learn, reflect, and act based on their own data. A person may discover that the same meal produces very different glucose responses depending on stress, sleep, or exercise. That insight is not trivial—it’s the foundation of consistent, small behavior changes that compound over time.
Yet there is an equally important “architecture of collaboration”: the way these insights are shared within the healthcare ecosystem. That collaboration must include:
Dexcom, for example, has invested years in integrating CGM data into EHRs so that endocrinologists can view meaningful summaries without leaving their primary software. Oura and Dexcom are also exploring combined insights—sleep and metabolic data together—to help patients ask better questions and clinicians reach the right diagnosis more quickly.
Importantly, engagement cannot be about generating anxiety. The goal is to use people’s attention—often already captured by their phones—to create education, reassurance, and guided action, not alarm fatigue.
Today’s payment systems are still largely built for episodic, fee-for-service care, even as technology enables continuous, preventive models. Moving from “sick care” to true health care requires aligning incentives around outcomes, not encounters.
Dr. Ide emphasizes that return on investment (ROI) must be defined relative to the population and the timeframe:
Policy is beginning to catch up. The Centers for Medicare and Medicaid Innovation’s ACCESS program is exploring ways to reimburse companies that can help practices remotely measure and improve cardiometabolic health at home—an explicit move toward value-based, population-level care.
On the industry side, companies like Dexcom are broadening their concept of ROI beyond survival and safety to include engagement and user experience. Their newer products are designed not only to prevent hypoglycemia in type 1 diabetes, but also to help those with prediabetes or early metabolic risk learn how nutrition, sleep, and stress shape their health. Similarly, Oura’s focus on “the power of prevention” is about reducing the “I” in ROI—lowering costs and reducing future disease burden—through sustained behavior change.
Underlying all of this is a simple but underleveraged prescription: “DWYM”—do what your mother told you. Eat well, sleep well, move more, manage stress. Continuous sensing, coupled with thoughtful design and reinforcement, is finally giving health systems and individuals the tools to do that at scale.
Continuous monitoring will only transform health if it reaches those who stand to benefit most—often the very populations least likely to afford or access new technologies. The digital divide is as much about economics and trust as it is about connectivity.
The panelists shared several approaches to closing that gap:
Rimidi’s work with Boston Medical Center offers a compelling example. High-risk pregnant women with hypertension were remotely monitored during the pandemic, avoiding repeated in-person visits at a time of high vulnerability. Engagement rates did not differ by ethnicity or primary language—evidence that, with thoughtful design, connected health can bridge rather than widen disparities.
Oura’s partnerships with Medicare Advantage plans similarly demonstrate that when devices are covered as a plan benefit, older adults can and do use them to move more, sleep better, and eat more thoughtfully. These early results are qualitative but promising, and they point to a future where digital tools are embedded in public programs, not just premium consumer segments.
As AI and large language models start to interpret continuous health data and suggest interventions, governance becomes more than a compliance issue—it is a trust issue. Hale argues for a dual approach: self-governance by companies that understand their technologies and rapid, adaptive guidance from regulators to ensure safety and standards.
At the same time, AI is poised to become an essential layer in clinical decision-making. For conditions like diabetes, where treatment options now include multiple drug classes and complex combinations, AI can help interpret CGM data to support therapy optimization and even de-intensification when appropriate. Large language models can also:
The panelists’ closing hopes for the future are strikingly human: bring joy back to the practice of medicine, make prevention the default, and restore the human dimension of care even as it becomes more digital.
Perhaps the most important reframing comes from Dr. Bhatt: we should stop calling it “digital health.” It is simply health. Continuous sensing, AI-driven insights, and remote monitoring are no longer separate sectors; they are integral components of modern care. The leaders who will shape the next decade will be those who can translate streams of data into humane, equitable, and sustainable systems—where people live their lives, largely not thinking about their health, yet knowing they are continuously cared for.