Wearables are creating a new health data layer. Is healthcare ready to govern it?

Consumer wearables and health apps are creating a new data layer outside traditional healthcare infrastructure. As it becomes more relevant to prevention, remote monitoring, and long-term care, leaders need to answer a practical question: is healthcare ready to use it with confidence?

The challenge right now isn’t lack of data, but actually the opposite. Healthcare spent years trying to collect more patient data. Now, the pressure is to validate, integrate, and securely govern that information.

The next step for healthcare is to ensure that this data is reliable and useful enough to improve care genuinely.

From gadgets to infrastructure: The rise of consumer health data

Originally, smartwatches and fitness trackers were more like gadgets, easy-to-use tools that helped motivate consumers to manage their health and foster a healthy lifestyle. Yet the data they generated were more personal, lightweight, and mostly disconnected from clinical care.

But this is no longer the case. These devices can now gather real-time data and continuously monitor various metrics, including heart rate, sleep patterns, blood oxygen levels, movement trends, or glucose fluctuations. And the impact isn’t theoretical. A study on the Apple Watch hypertension notification feature revealed that 41.2% of individuals with undiagnosed hypertension received alerts, while only 7.7% of those without the condition were notified. While there’s clear potential for people who may benefit from further assessment, the results still need careful interpretation. These devices can support clinicians in care delivery, but human oversight and clinical diagnosis are irreplaceable.

This shift has transformed consumer wearables and apps into a comprehensive health data ecosystem that can support patient monitoring, preventive and personalised care models at scale, long-term health tracking, and facilitate early risk detection.

As a result, patient expectations about care delivery have also changed. They increasingly expect care to be personalised and proactive.

However, such a change created new challenges for healthcare providers. Bringing consumer-generated wearable data into care raises important questions about accuracy, ownership, and governance.

The real challenge isn’t collecting data, but making it useful

The healthcare industry has spent years trying to digitise patient information. Now it faces a new dilemma: an explosion of data that requires different approaches to validation, governance, and clinical integration.

In theory, this data could empower healthcare providers to identify risks sooner, monitor patients remotely, and deliver more tailored care. But in practice, most of that value still sits outside existing clinical workflows.

Data interpretation and integration

Tech companies producing different wearable devices control the software and hardware. But in many cases, they also control the algorithm behind the readings and how they measure data. Meaning the data is generated under conditions healthcare organisations don’t fully control. For different wearables, the methodology can vary, which means they can produce different results – and this can impact the interpretation of this data.

The other thing is when those components change, healthcare organisations need to understand whether the data feeding their workflows has changed with them. Governance therefore cannot stop at the initial integration. Organisations need a way to monitor device updates, API changes, and changes in how clinically relevant metrics are produced.

Data accuracy

A smartwatch may collect thousands of measurements between 2 appointments. But that does not mean that each reading should trigger action. Healthcare organisations need to decide which signals matter for a specific use case, how reliable those signals need to be, and what should happen when a reading crosses an agreed threshold.

There’s also the other part: the context behind the measurement. Was the device worn consistently? Was the signal interrupted? Has the manufacturer changed how the metric is calculated? Is the data useful for identifying a trend, or reliable enough to support a clinical decision? Defining which data deserves clinical attention can pose a serious challenge.

Data validation

Healthcare systems are built on validated clinical data collected under controlled conditions. Consumer-generated data comes from devices designed primarily for engagement. While the information can be useful for identifying trends and behavioural patterns, it may not meet the standards required for clinical decision-making.

Data ownership and governance

Ownership and governance can present other obstacles, which I often see in our work with healthcare and life sciences clients. Unlike clinical records, which typically sit within regulated environments, consumer health data often lives inside technology platforms. A patient’s information may pass through multiple apps, cloud services, and third-party integrations before reaching the provider. This creates uncertainty around who owns the data, who is responsible for protecting it, and how it can be used.

Data security

Security adds another layer of complexity. The more wearable devices connect to healthcare workflows, the larger the potential attack surface becomes. Unlike traditional medical data, consumer-generated data often falls outside regulatory safeguards like HIPAA when collected directly by a consumer app or wearable provider.

The result is a growing disconnect. Consumers are generating more health data than ever before. Yet, healthcare organisations often cannot fully operationalise it into existing clinical workflows, even though the majority of clinicians see real advantages from wearable data in treating their patients, as per an American Medical Association study.

My impression is that the primary challenge is data governance, not the devices themselves. Collecting as much information as possible is not an effective strategy. The most value will come from fostering trust, ensuring security, developing interoperability, and turning fragmented data streams into actionable insights.

That is the gap the healthcare industry is still trying to close.

Regulation is falling behind: fragmentation instead of clarity

As consumers are increasingly willing to share health data through wearables and apps, the regulatory frameworks remain uneven. Laws like HIPAA were built for healthcare systems managed by hospitals, providers, or insurers. Today, smartwatches, fitness trackers, connected health devices, and consumer apps generate growing volumes of health-related information outside HIPAA-covered healthcare environments. Although healthcare organisations often know how to govern clinical data, they lack clarity when consumer-generated data enters clinical workflows.

This creates a widening gap. Wearable data collected by consumer technology companies can sit under a different combination of state privacy laws, consumer health data rules, platform terms, and federal requirements depending on who collects it and how it is used.

Closing this gap is essential. Healthcare companies need certainty when it comes to consumer data, with strict and clear guidelines on how this data can be used, shared, and protected. Only then can clinicians and healthcare providers use this data safely and consistently.

Where wearables are actually delivering value

Despite their limitations, wearables and health apps are already making a noticeable impact in specific areas of healthcare and patient behaviour. The value today is less about clinical replacement and more about expanding visibility beyond the clinician’s office.

Some of the strongest use cases include prevention, remote patient monitoring, and patient engagement. Continuous data gives healthcare providers visibility they simply didn’t have before, helping them identify changes in patient health between appointments rather than reacting after problems escalate.

For patients with chronic conditions or those recovering at home, that visibility can support earlier intervention, reduce unnecessary hospital visits, and improve continuity of care. Just as importantly, wearable devices encourage healthier behaviours by making health metrics visible and actionable.

The real value I see isn’t in the data itself, but in turning continuous monitoring into timely, informed decisions. It’s not about asking “How to collect more data?” More importantly, healthcare organisations need to identify the signals that actually matter and integrate them into care without adding more complexity or workload for clinicians.

The technical integration is only part of the challenge. Once wearable data enters a healthcare environment, someone needs to decide what deserves attention. A system that forwards every abnormal reading to clinicians may technically work but still fail operationally. Too many alerts can add workload rather than improve care.

The goal should therefore be to turn continuous streams into meaningful clinical signals, with clear thresholds for when data should be surfaced, reviewed, or escalated.

What healthcare leaders should define before integrating wearable data

Consumer-generated health data has enormous potential to strengthen care. Wearables and health apps are already helping improve patient engagement, support remote monitoring, and provide valuable insights between clinical visits.

But before integrating consumer wearable data into clinical workflows, leaders should be able to answer these questions:

1. What clinical or operational decision will the data support?

   Start with the use case, not the volume of available data. Heart rate trends used for remote monitoring require a different level of reliability and oversight from step counts used for patient engagement.

2. Which wearable signals are reliable enough for that purpose?

   Healthcare organisations should define acceptable accuracy, missing-data thresholds, device limitations, and the circumstances in which a signal requires confirmation through clinical measurement.

3. What data should actually enter the healthcare system?

   Healthcare teams rarely need every raw measurement. Decide whether the system needs individual readings, trends, summaries, or clinically relevant exceptions.

4. Who acts when the data indicates a potential problem?

   An alert without ownership simply creates another queue. Define who reviews the signal, when it should be escalated, and when human confirmation is required.

5. How will changes in the device ecosystem be monitored?

   Consumer wearable platforms evolve independently of healthcare organisations. Changes to algorithms, APIs, device capabilities, or data formats should not silently alter clinical workflows.

So, what’s the next phase? Advancements in devices alone won’t define it. The key will be to build the trust, infrastructure, and governance needed to turn continuous data into even better healthcare outcomes – without increasing complexity for clinicians or risk for patients.