As someone who works in data management, governance and architecture, I recently had an experience that left me simultaneously reassured about my health and intrigued by what the process revealed about data.
The good news first.
I recently attended an NHS Health Check. The process was straightforward and professional, comprising three stages:
- Blood tests
- Clinical review of the results
- A meeting with a nurse to discuss the findings and receive a report
I’m pleased to report that everything looked healthy and my cardiovascular “heart age” came out two years younger than my actual age. I’ll happily take that win.
But while the healthcare experience was positive, the data experience told an interesting story.
What worked well?
Let’s start with what works.
The NHS has clearly invested heavily in digitisation. My blood test results and many health metrics were available through the NHS App, giving me direct access to my health information.
The overall process was also smooth, with appointments running efficiently and information available to the healthcare professionals involved.
From a data perspective, this showed that a substantial amount of useful information was already being captured and made available through digital channels.
Where did the data experience start to break down?
The cracks started to show during the consultation with the nurse.
As additional health information was collected, the nurse needed to switch between multiple applications to capture various scores, assessments and risk indicators.
To a patient, this might simply appear slightly cumbersome. To someone who works in data architecture, it immediately raises questions:
- How many systems are involved?
- Is data being duplicated?
- Is information being synchronised?
- Is there a common data model?
- How many interfaces are required to move data between systems?
I couldn’t answer those questions from one Health Check. But whenever users have to navigate multiple systems to complete what appears to be a single process, it’s worth asking how effectively the underlying data and architecture support that process.
Why did a digital process still need a paper report?
Then came the report.
A nine-page report. Printed. Single-sided. Unable to be emailed.
Initially, I found this baffling. In 2026, why was a process containing so much digital information still ending with a printed report?
Then it started to make more sense.
The report I received didn’t contain all the information that had already been captured digitally.
Several fields either failed to populate or displayed messages such as “No events found”, including information relating to cholesterol, alcohol consumption and other health assessments.
The nurse therefore had to write information onto the paper report manually.
One example brought the issue into particularly sharp focus. In the printed report I was given, my heart attack or stroke risk appeared as 88%. The nurse crossed this out by hand and replaced it with my actual score of 6%.
I don’t know why the incorrect figure appeared, so I wouldn’t attempt to diagnose the underlying cause.
But at that point, the printed report was no longer just a report. It had become a workaround.
Is this really a technology problem?
It would be easy to blame technology.
I can’t know from the outside exactly what is happening behind those systems. But from a data management perspective, the symptoms are very familiar.
The NHS clearly holds the data. The NHS App can display much of it. Clinicians can access it. Yet, from my experience, some of that information wasn’t flowing through consistently to the final report.
That raises familiar questions about whether information is unavailable, disconnected, inconsistently represented or poorly integrated between systems.
It’s a pattern we see across many organisations:
- Data exists.
- Processes exist.
- Systems exist.
- But they aren’t always connected effectively enough to deliver the desired outcome.
Adding another application doesn’t necessarily solve that problem. Understanding how data is managed, governed and moved across the organisation is often a better place to start.
What does this look like from a data management perspective?
My experience raised questions across several areas of data management.
Master Data Management
Patients, appointments, assessments and clinical activities need to be identified and represented consistently across different systems.
I can’t know whether Master Data Management was the issue in this particular case. But when information exists in one part of an organisation and cannot be brought together reliably elsewhere, it’s reasonable to ask how consistently core data is being identified, managed and shared.
Data Governance
If a report says “No events found” when the information has already been captured elsewhere, who owns the accuracy and availability of that data?
Clear ownership and accountability for data elements, metrics and reporting outputs are fundamental to making data work across an organisation.
Data Architecture and Integration
Information available in one system wasn’t consistently reaching another.
Good data architecture should enable information to move between operational systems, digital services and reporting tools without requiring people to manually fill the gaps.
The goal should be simple: Capture once. Use many times.
Not: Capture once. Re-enter later. Write it on paper.
Data Architecture and Integration
Data and process are inseparable.
In this case, the nurse was compensating for gaps in the digital process through manual intervention. The service still worked, but it required a person to bridge the gaps between systems and information.
That’s an important distinction. The people weren’t the problem. They were making the process work despite the limitations around them.
What could small data inefficiencies mean at scale?
What also struck me was how quickly a small inefficiency could become significant at scale.
There are around 770,000 people of each single year of age between 55 and 59 in England (ONS). If, hypothetically, 770,000 people went through exactly the same Health Check process I experienced and each received the same nine-page printed report, that would amount to nearly seven million pages.
Of course, that isn’t a measure of actual NHS Health Check printing. Not everyone will have the same experience, and processes may vary.
But that’s really the point.
A few sheets of paper for one person don’t seem particularly significant. Multiply small manual processes, duplicated activities and disconnected information across a population of this scale, and the potential operational impact becomes much easier to see.
And the paper itself isn’t the real issue. It’s simply a visible symptom of something bigger: when digital information doesn’t flow reliably through a process, people create manual workarounds to compensate.
Why does good data management matter?
The NHS Health Check remains an excellent initiative. The healthcare professionals involved were excellent and the experience gave me useful information about my health.
What stayed with me wasn’t really the paper. It was what the paper represented.
The data had already been captured. The people delivering the service were doing their jobs well. But gaps between data, systems and process meant manual effort was still needed to make the whole thing work.
It’s a small example, but one that we see often in many organisations and industries.
Organisations rarely struggle because they don’t have enough data. The bigger challenge is making the data they already have trusted, connected and usable across the organisation.
Sometimes all it takes to spot the opportunity is a routine health check.
What can organisations learn from this example?
My experience was just one Health Check, but it illustrates some useful questions for any organisation managing data across multiple systems:
- Is information captured once and then available wherever it’s needed?
- Are people manually re-entering information that already exists elsewhere?
- Can users trust that reports contain the right information?
- Is ownership clear when information is missing or inconsistent?
- Does the architecture support the business process, or are people working around it?
- Are small inefficiencies being multiplied across thousands or millions of interactions?
These aren’t technology questions. They’re data management questions.
So, what did my NHS Health Check teach me about data management?
This experience showed how an organisation can capture valuable information digitally but still rely on manual workarounds when data, systems and processes don’t connect effectively. It’s a small example of a much bigger enterprise challenge: having data isn’t the same as being able to use it consistently.
Is disconnected data creating unnecessary work in your organisation?
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