October 6, 2025

The Cost of Healthcare’s Data Explosion: How to Address Clinician Cognitive Overload

Medical professional working with complex healthcare data visualizations.

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Healthcare has always been a reactive industry.

We wait for people to get sick, they come in for a diagnosis, and we run tests and hopefully successfully treat the sickness. This approach has persisted for a simple reason: preventative screening across entire populations is extraordinarily inefficient.

Even for the most prevalent diseases, we’re looking at 10–20% of the population affected. That means 80% of the time, blanket screening examines healthy people, which isn’t cost-effective or a good use of clinicians’ time.

The methods we use to add some precision to this process come with trade-offs: We apply demographic-based criteria that, while practical, create massive gaps in care.

Take the UK’s National Health Service screening for abdominal aortic aneurysms. If you’re a 65-year-old male, you receive a letter and are asked to come in for a Doppler ultrasound. Based on the results, you’ll either be discharged or scheduled for follow-ups.

The problems with this approach are glaring. We know aneurysm prevalence increases with age, yet if you don’t have disease at 65, you’re ignored going forward, even as your risk climbs. Women aren’t screened at all because their slightly lower prevalence makes them “not cost-effective” to screen.

It’s a limited approach to healthcare.

Infographic: The Cost of Healthcare’s Data Explosion: How to Address Clinician Cognitive Overload

The Personalized vs. Demographic Paradox

We don’t treat anything else this way. Your car has sensors everywhere, constantly collecting data. When something needs attention, the check engine light illuminates.

You don’t service your engine every arbitrary number of miles; you do it when data tells you it’s needed. Planes, too, undergo constant monitoring, with corrective changes made based on real-time information.

Yet with human bodies, arguably far more complex and valuable than any machine, we’ve been stuck with demographic guesswork and reactive treatment. Until now.

The Promise of Personalized Health

We’re finally getting the opportunity to shift from demographic to risk-based screening. Smartwatches, wearables, and at-home devices are creating unprecedented access to real-time patient insights outside clinical settings.

We can gather continuous data about patient health, moving beyond the limitations of “you’re a male over a certain age, so we’ll check you for these diseases.”

This shift marks a change in how we approach healthcare. Instead of waiting for people to get sick or relying on broad demographic categorization, we can identify actual risk factors as they develop. The gateway to preventative, personalized healthcare is opening.

But there’s a problem, and it’s not the one you might expect.

The Cognitive Load Crisis

Clinicians are already massively overworked, with countless responsibilities on their plates. Now imagine adding a continuous stream of patient data to their workload. Asking them to sift through mountains of information to identify disease indicators isn’t just impractical; it’s impossible.

This creates an organizational challenge for medical companies: What is the right data? What’s the right presentation? When is the right time to surface it to clinicians?

The potential of connected health data is incredible. But if we just throw it at clinicians, we’ll overwhelm them and dilute any positive impact.

Too often, we fall into the trap of thinking that if data is good, more data must be better. We forget that clinicians are medical professionals, not data scientists. They can’t be expected to parse through thousands of data points under their existing time pressures.

When 12,960 Data Points Become Useless

A medical device company recently developed an innovative treatment for Parkinson’s disease patients. The treatment involved implanted devices that could both deliver therapeutic electrical signals to alleviate tremors and continuously monitor brain activity every 10 minutes.

Clinicians treating these patients had two levers to pull: adjusting the electrical stimulation or modifying medication. Finding the right balance between these interventions was key for patient outcomes.

The company’s initial approach was dumping all the collected data into a graph and presenting it to clinicians: “Here’s your patient’s brain signal data for the last three months since you last saw them.”

Six data points per hour, 144 per day, across 90 days equals 12,960 data points. They expected clinicians to look at this wall of information and determine what was wrong with their patient and what adjustments to make.

It was unfeasible.

The Human-Centric Solution

This is where Method’s approach becomes essential.

We don’t start with the data; we start with the humans who need to use it. We take a human- and business-centric approach, identifying the actual problems and working downward to solutions.

We began by asking: What do clinicians actually need to see? How do they determine if a patient is getting better or worse? How do they decide whether to adjust medication versus electrical stimulation?

Only after understanding these decision-making processes did we work backward to identify what information would support those decisions.

During our conversations with clinicians, we learned something important. They explained that brain activity follows natural rhythms: low during sleep, elevated during morning activities, dropping midday, then rising again in the evening.

What they needed wasn’t raw data but deviations from these patterns.

“Show me my patient’s natural rhythm,” they told us. “Then show me when they deviated from it. On days when the patient reported falls or increased tremors, what was happening compared to their baseline? That helps me make decisions.”

We repackaged the same 12,960 data points. Instead of an overwhelming graph, we created an overlay showing an average day in the patient’s life with clear indicators of when and how their patterns deviated from normal.

The data was identical, but the information presented was vastly different. Now, it aligned with how clinicians make decisions.

Bridging Business and Engineering

This human-centric approach to data presentation requires a unique combination of capabilities. You need teams that understand experience design, product design, data science, AI, and engineering. More importantly, you need to bridge the often-substantial gap between the business side of healthcare and the engineering side.

Many organizations struggle with this translation. Transitioning from clinicians’ needs and wants to tangible data points and the required infrastructure to support them is a complex process. Having the data or even the technology isn’t enough.

You need to understand the human context in which that data will be used.

The Missed Opportunities We Can No Longer Afford

The urgency of getting this right becomes clear when you look at what we’re currently missing. During my PhD research, I spent some time developing computer vision models that could analyze historic CT scans for missed opportunities to detect disease.

When you look at a CT scan of the chest, you can see the heart, lungs, spine, and numerous other structures. But if a patient comes in with suspected heart disease, clinicians aren’t looking at bone density, checking for lung abnormalities, or screening for other conditions. Asking clinicians to look for every possible issue in every scan isn’t feasible.

A recent UK audit examined patients who’d had heart attacks, reviewing their historical medical records to determine if earlier detection was possible. They found that clinicians caught the disease at the earliest opportunity only 25–30% of the time.

In 70% of cases, there were earlier signs of heart disease that were missed. These weren’t missed through negligence, but because the data wasn’t presented in a way that made those signs apparent and actionable.

Moving Forward: The Design-First Approach

The path forward isn’t just about collecting more data or building more sophisticated algorithms. We need to deliberately design how we present information to the people who need it most.

Healthcare organizations need to stop assuming that data has innate value. Start proactively bringing clinicians and decision-makers into the product design process. Ask them, “We have this data. How would it benefit you? What would make it useful in your workflow?”

This change requires a shift in thinking. Instead of asking, “How do we display all this data?” we need to ask, “What decisions do clinicians need to make, and what’s the minimum effective information that supports those decisions?”

The Gateway Is Open

We stand at a unique moment in healthcare. We have more data available than ever before, and we have a better ability to sift through and use that data effectively. We can leverage both real-time patient information and historical data in ways that were impossible just a few years ago.

But success won’t come from the technology alone. It’ll come from understanding that clinicians need partners who can translate between the world of data and the world of patient care. These partners must understand that the goal isn’t to showcase data but to improve outcomes.

The companies that succeed in connected health won’t be those with the most data or the most sophisticated algorithms. They’ll be the ones that understand a simple truth: in healthcare, more data isn’t better. Better data, designed for the people who will actually interpret it, is better.

Ready to turn your healthcare data from overwhelming to actionable? Method combines human-centered design with technical excellence to create data experiences that clinicians actually want to use.

Contact Method to learn how we can help you bridge the gap between data potential and clinical reality.

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