Hey Health Techies!

Healthcare may be one of the most data-rich industries in the world… and somehow still one of the least connected.

We have more patient data than ever before: wearable data, genomic data, lab data, imaging, pharmacy records, AI-generated insights, and endless documentation inside electronic health records. Yet clinicians and patients still struggle to get the right information without having to jump through a ton of hoops.

So why is this still so hard to solve?

One of the biggest misconceptions in healthcare tech is that healthcare’s data problem is simply a “technology problem.”

It’s not.

Healthcare absolutely has a technology problem… but it also has an incentives problem, a workflow problem, a regulation problem, and sadly a trust problem too.

People outside the industry are often surprised to learn that health data is still incredibly fragmented. Your primary care office has one version of your story. Your specialist has another. Your pharmacy has another. Your wearable device is collecting entirely different data. Your lab results live somewhere else. Imaging data often sits in separate systems altogether. And if you’ve changed health systems, moved states, or switched insurance plans? Good luck.

Even within the same hospital system, data fragmentation is common.

And contrary to popular belief, this isn’t because healthcare organizations simply “don’t want to innovate.” The problem is much deeper than that.

Healthcare data was never originally designed to function as a unified ecosystem.

Most healthcare infrastructure evolved around operational needs — billing, documentation, compliance, scheduling, reimbursement, and legal protection — not seamless interoperability. Electronic health records were largely built to optimize transactions and workflows inside individual organizations, not necessarily to create portable, longitudinal patient histories that move cleanly across the healthcare system.

So even when organizations can exchange data, the information often arrives incomplete, poorly structured, duplicated, delayed, or buried inside PDFs and unstructured clinical notes.

And then there’s the standardization problem.

Healthcare isn’t just dealing with “a lot of data” (though let’s be clear…this problem is an article in itself), it’s dealing with thousands of ways to document the same thing.

One clinician may document diabetes one way. Another may use different terminology, coding, abbreviations, or workflows entirely. Lab values can use different reference ranges. Medication histories may be outdated. Problem lists are notoriously messy. Even something as simple as blood pressure data can become difficult to normalize across systems at scale.

This is part of why interoperability has remained such a massive challenge despite years of industry focus.

Standards like FHIR (Fast Healthcare Interoperability Resources) have absolutely moved the industry forward. But implementing interoperability across thousands of hospitals, insurers, labs, pharmacies, vendors, and legacy systems is incredibly complicated — especially when many organizations are running on infrastructure that predates modern cloud computing entirely.

And unlike most industries, healthcare data carries uniquely high stakes.

A broken recommendation algorithm on Netflix is annoying, but broken data exchange in healthcare can directly impact patient safety.

Healthcare organizations have to navigate HIPAA compliance, consent management, cybersecurity threats, liability concerns, fragmented regulations, and growing concerns around AI governance — all while clinicians are already overwhelmed with administrative burden and burnout.

This is one reason the phrase “Health data is medicine” has gained traction among some healthcare technology leaders, including SEQSTER CEO Ardy Arianpour.

Because increasingly it has become obvious that despite all of the hurdles, the ability to connect, contextualize, and operationalize health data is foundational to modern care itself, and the need to figure it out feels urgent.

Not just for AI. Not just for research. But for the basic functioning of healthcare delivery.

Companies like SEQSTER are trying to tackle this by building infrastructure that aggregates and harmonizes patient-consented data across multiple sources into a more longitudinal patient record — including electronic health records, wearables, genomics, labs, imaging, and other datasets. Can you even imagine a world where you actually have a neatly packaged up timeline of health events, orders, procedures, and symptoms?

What’s particularly interesting is the shift toward patient-mediated interoperability.

Historically, interoperability has relied heavily on institutions exchanging data with one another. But healthcare organizations often operate in silos — sometimes because of technical limitations, sometimes because of business incentives, and sometimes because organizations are understandably cautious about privacy, compliance, and liability.

Patient-consented data sharing introduces a different model where the patient becomes part of the interoperability layer itself.

Because healthcare doesn’t just have a data access problem, we also have a data usability problem.

Having massive amounts of healthcare data means very little if the information is fragmented, low quality, inconsistently labeled, impossible to contextualize, or disconnected from clinical workflows.

This becomes especially important right now.

Both because we’re in the AI era and because we’re in an era where patients have more insight into their health data than ever, but still crave timely, actionable advice.

Everyone wants to talk about foundation models, copilots, and generative AI in healthcare. But the reality is that healthcare AI is only as useful as the infrastructure underneath it.

Garbage in, garbage out still applies.

If healthcare organizations can’t trust the underlying data, they won’t trust the AI systems built on top of it either.

And trust is everything in healthcare.

That’s why the organizations likely to win over the next decade won’t necessarily be the ones with the most data, they’ll be the ones that can make the data usable, trustworthy, actionable, and clinically meaningful and provide the transparency and insights that patients are so badly craving.

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Until next time,

Lauren