From data overload to clinical insight: What healthcare needs next

Every year, more connected devices are putting more health data within reach. Global wearable shipments reached 145.7 million units in the first quarter of 2026 alone, up 4.3% year over year. From heart rate readings and nights of sleep to blood pressure checks, the industry has a habit of treating more measurement as progress.

It isn’t, on its own. The goal was never simply to collect more data. It was to give clinicians information they can use to make better decisions, spot changes earlier and understand what actually matters for the patient. More data only helps if it makes that easier.

Data doesn’t create value sitting in an app

Much of the data generated by consumer wearables still sits outside clinical pathways. It stays inside a consumer app, disconnected from the electronic health record and from the care team that would actually act on it. Clinicians can’t manage a dozen separate platforms, one per device brand, on top of the systems they already use. Devices that don’t integrate into existing workflows are therefore much harder to use at scale in clinical care, no matter how good the sensor inside them is.

Interoperability is therefore becoming increasingly important as remote monitoring expands. EHR integrations and standards such as FHIR can help health data move into existing clinical workflows rather than creating another interface for healthcare professionals to manage. A risk score or a trend is only useful if it reaches the clinician in a form they can actually use.

But getting the data in front of a clinician is only part of the challenge. It also needs to tell them something useful.

For instance, a blood pressure reading on its own tells you very little. Was it high because of stress, timing, or a genuine deterioration? What matters clinically is the pattern over time: is water retention creeping up in a heart failure patient, is blood pressure drifting despite medication, or is sleep quality declining before a patient reports feeling worse?

That’s the difference between a sensor and a clinical signal. Getting from one to the other means filtering noise, accounting for how and when a measurement was taken, and reducing a stream of numbers down to the few things a clinician actually needs to see.

The same principle applies to risk assessment. Machine learning and other analytical approaches can combine multiple physiological signals into indicators that are easier to interpret, provided they remain clinically meaningful and explainable. The objective should not be to replace the underlying data with another black box, but to make complex information more actionable.

Clinicians don’t need more data for the sake of it. They need information that helps them decide when to act. One study of remote blood pressure monitoring found that clinicians took action on 62.1% of EHR alerts for persistently elevated readings, while 37.9% resulted in no change to the care plan. In some cases, the alert had not accounted for other relevant information, such as improving blood pressure trends or recent medication changes.

This is why simply flagging that a measurement has crossed a threshold is not always enough. The information also needs to reflect the wider clinical context.

Numbers alone miss half the story

Objective measurements tell you what’s happening physiologically. They don’t tell you why, or how a patient is actually experiencing it.
That’s why patient-reported context has to sit alongside physiological data, rather than in a separate system a clinician has to check independently. A blood pressure trend paired with a symptom questionnaire, for example, can provide considerably more context than the measurement alone.

This becomes particularly important as remote monitoring expands into areas such as heart failure, hypertension and obesity management, where symptoms, behaviour and treatment response all help explain what the numbers mean. Combining objective and patient-reported information gives clinicians a fuller picture and makes it easier to determine whether something actually requires attention.

But context is only useful if the underlying measurement can be trusted. A technically accurate measurement is not automatically clinically validated for a specific use, nor does it necessarily meet the regulatory requirements needed for use in a clinical pathway.

Under the EU Medical Device Regulation, manufacturers need clinical evidence appropriate to a device’s intended purpose to demonstrate that it meets relevant safety and performance requirements. For technology providers, this means building clinical evidence, privacy and security into the development process from the start, rather than treating them as requirements to address once the product is already built.

The technology already exists. The discipline doesn’t yet.

Connected health technology has been part of healthcare for more than a decade, and the industry has already made significant progress in how health data is collected and used. The next step isn’t simply adding more sensors or collecting more measurements.
It’s building better ways to use the data we already have: identifying what matters, putting it in the right clinical context and making sure it is properly validated.

The challenge for the industry is therefore shifting from data generation to data interpretation. As remote monitoring becomes more widespread, the value of connected health will depend less on how many measurements a device can produce and more on whether those measurements can be translated into reliable, clinically relevant information.

More data is not inherently better. The real opportunity lies in making sure the data that reaches clinicians is meaningful, contextual and actionable.

About the Author

Antoine Pivron is Vice President of Withings Health Solutions, the B2B division of Withings, where he leads the global business across the US and Europe. Withings Health Solutions supports more than 300 healthcare partners and over one million patients enrolled in remote monitoring programmes worldwide.

Up next: MedTech World Europe 2026 | Malta

The conversation around connected health, clinical data and the role of technology in healthcare will continue at MedTech World Europe 2026, taking place from 11–13 November at Mediterranean Conference Centre, Valletta, Malta. The event will bring together healthcare and MedTech leaders to discuss the technologies, challenges and opportunities shaping the sector.

Explore the programme and book your ticket to MedTech World Europe 2026.