The next generation of digital health products will not win by measuring more. They will win by making better decisions easier.
Healthcare has never had more data.
Wearables continuously measure heart rate, sleep, activity and oxygen saturation. Connected medical devices can monitor patients outside the clinic. Diagnostics generate increasingly sophisticated datasets. Remote monitoring platforms can follow patients for weeks, months or years.
And AI promises to extract even more information from all of it.
Yet there is an uncomfortable question that MedTech companies need to ask:
What actually happens because the data exists?
Because collecting data is not the same as creating value.
A connected device can generate thousands of measurements and still fail to improve a single clinical decision.
A dashboard can display dozens of parameters and still leave a clinician wondering what they should do next.
An app can provide a patient with extraordinary insight into their physiology and still be abandoned after three weeks.
The real design challenge in connected healthcare is therefore changing.
It is no longer simply:
What can we measure?
It is:
What should somebody do differently because we measured it?
That distinction could determine which connected health products become embedded into healthcare and which simply create more digital noise.
Healthcare does not need another dashboard
One of the traps in connected device development is assuming that because data are available, they should be displayed.
More sensors create more measurements.
More measurements create more graphs.
More graphs create more dashboards.
And somewhere along the way, somebody assumes that more information equals more value.
It does not.
The World Health Organization’s digital health strategy specifically emphasises translating data, research and evidence into action and supporting digital solutions that contribute to informed decision making. That is an important distinction. The objective is not data collection itself, but better decisions and ultimately better outcomes. [1]
The challenge becomes even more significant when the user is a healthcare professional.
Clinicians are not sitting in front of a connected device waiting for more information to arrive. Digital products have to compete for attention within an already complex clinical workflow.
Every additional screen, alert, metric and interpretation creates cognitive demand.
The best connected healthcare products therefore do something counterintuitive.
They hide complexity.
They collect sophisticated data in the background but present the user with something much simpler:
What has changed?
Does it matter?
What should I do?
How urgently should I do it?
That is where data begin to become useful.
The product is not the sensor
Connected device programmes often begin with technology.
We can measure this.
We can connect that.
We can add Bluetooth.
We can build an app.
We can train an algorithm.
But technological capability is not necessarily a product proposition.
A better starting point is the decision the product is trying to improve.
Imagine a connected device monitoring a patient at home.
The engineering question might be:
Can we reliably collect the measurement?
The product question is much broader:
Can we turn that measurement into an intervention that improves care?
That immediately introduces another set of questions.
Who receives the information?
When do they receive it?
What constitutes a meaningful change?
What should trigger an alert?
What information does the clinician need to make a decision?
What does the patient need to understand?
What happens when the data are incomplete?
What happens when something is abnormal?
What happens at 2am?
And perhaps most importantly:
Does this make the existing pathway better, or simply add another layer to it?
These are not secondary UX questions.
They are fundamental product development questions.
Actionable data has to be designed
Data do not naturally arrive in an actionable form.
They have to be transformed.
A useful way to think about connected healthcare is as a progression:
Signal to data to information to decision to action to outcome.
Most connected devices are technically very good at the first two stages.
Increasingly, AI can help with the third.
But commercial and clinical value is usually created further along the chain.
If a wearable detects a physiological change, the value is not necessarily the measurement itself.
The value might be identifying deterioration earlier.
If a rehabilitation device measures movement, the value is not necessarily recording thousands of movement parameters.
It may be telling a physiotherapist that recovery has plateaued and the programme needs adjusting.
If a diagnostic device continuously monitors a biomarker, the value may not be showing the patient another graph.
It could be identifying when intervention is necessary and when it is not.
This changes how the product needs to be designed.
Instead of asking:
How do we display all this data?
The development team asks:
What is the minimum information required to make the right decision?
That is a much more powerful design brief.
UX in healthcare is becoming decision architecture
User experience is sometimes treated as the final layer applied to a digital health product.
Build the technology.
Develop the software.
Then make the interface intuitive.
That approach misses much of the value of UX.
In connected healthcare, UX should influence the architecture of the product itself.
The FDA’s current human factors guidance makes this point very clearly. Human factors and usability engineering consider users, use environments and user interfaces, with the objective of enabling medical devices to be used safely and effectively. The FDA specifically considers how users perceive information, interpret it, make decisions and subsequently interact with the device. [2,3]
That means good UX is not simply attractive screens.
It is understanding human behaviour.
It is prioritising information.
It is reducing cognitive load.
It is designing alerts that deserve attention.
It is understanding how information moves between patients and clinicians.
It is designing for the real environment in which the product will be used.
And increasingly, it is determining when technology should stay quiet.
More alerts can create less value
Connected devices make it technically easy to notify somebody whenever something changes.
That does not mean they should.
A system that constantly demands attention can quickly become a system that users learn to ignore.
Good connected product development therefore requires aggressive prioritisation.
Not every change needs an alert.
Not every measurement needs to be visible.
Not every piece of information needs to reach every user.
Patients, clinicians, carers and administrators may require completely different interpretations of exactly the same underlying dataset.
The sophistication lies in presenting each user with the right information at the right moment.
This is particularly important as AI becomes embedded into connected medical devices.
AI can identify increasingly complex patterns within large datasets, but identifying a pattern is only useful if the product can translate it into something clinically or behaviourally meaningful.
Otherwise AI simply produces another layer of information.
Start with the outcome, not the technology
At Maddison, we believe some of the most valuable work in connected healthcare happens before a development programme becomes large, expensive and difficult to change.
It starts by interrogating the opportunity.
What problem are we really solving?
Whose problem is it?
What decision are we trying to improve?
What evidence will demonstrate value?
How will this fit into the existing workflow?
What does the patient actually need to understand?
What does the clinician actually need to see?
What can we remove?
These questions are best answered by bringing industrial design, UX/UI, human factors, engineering and commercial thinking together early.
Then building.
Testing.
Learning.
And iterating quickly.
Not spending months debating what users might want around a conference table.
Put prototypes into their hands.
Watch what happens.
Challenge assumptions.
Simplify relentlessly.
Find the points where the physical device, digital interface, data and workflow either come together or fall apart.
This focused development approach can be particularly valuable for larger MedTech organisations.
Established businesses often have extraordinary clinical knowledge, engineering capability and technology portfolios. What they do not always have is a small, multidisciplinary team that can step outside existing structures and rapidly explore what a new product could become.
That is where Maddison can add disproportionate value.
We can work alongside internal teams to explore opportunities, prototype new connected experiences and solve difficult product challenges without creating another layer of organisational complexity.
The connected device disappears
Perhaps the ultimate measure of a successful connected healthcare product is that the technology becomes almost invisible.
The patient does not think about sensors.
The clinician does not think about data architecture.
Neither thinks about algorithms, connectivity or interoperability.
They simply experience a product that helps them understand something important and make a better decision.
That requires considerable technical sophistication.
But it also requires restraint.
The future of connected healthcare will not be defined by which devices generate the most data.
It will be defined by which products can transform complex data into simple, trusted and actionable experiences.
Connected devices create data.
Great product design turns it into value.
References
- World Health Organization. Digital Health and Global Strategy on Digital Health 2020 to 2027. WHO.
- US Food and Drug Administration. Applying Human Factors and Usability Engineering to Medical Devices, Final Guidance, August 2026.
- US Food and Drug Administration. Human Factors Considerations, Medical Devices, 2026.
- World Health Organization. Digital Transformation Handbook for Primary Health Care: Optimizing Person-Centred Point of Service Systems, 2024.
- World Health Organization Regional Office for Europe. Health Data Governance in the Age of Artificial Intelligence: Policy Imperatives for the WHO European Region, 2025.