Team Structuring in Healthcare Analytics

There’s a setup you still see in a lot of healthcare analytics teams that doesn’t really hold up anymore. One person cranks out reports in isolation, another quietly writes SQL like it’s a secret code, and someone else dives into dashboard building without quite knowing who asked for it or why. Everyone’s busy, everyone’s trying their best, but the way the team is arranged doesn’t really fit the nature of the work. It’s too fragmented. Too disconnected. And that mismatch can create all sorts of problems.

Nowadays, the work of an analytical team is unpredictable. One day you're unpicking a broken data feed, the next you're trying to explain a subtle trend in patient flow to someone who really only wanted a bar chart. Add in ageing infrastructure, pressure from every direction, and more acronyms than is entirely reasonable, and it becomes clear that structuring a team for this world is not a copy-paste exercise.

One approach that doesn't help much is the factory model. You’ve probably seen it: a team for data quality, a team for data manipulation, another for analysis, then a final team that ‘outputs’ something. It looks tidy on paper but behaves more like a traffic jam. Handovers often become delays, delays become frustration, and the whole thing starts to creak under the weight of its own process. And its usually those responsible for the output who get it in the neck from the customer.

Real teams, the kind that stay functional under pressure, don’t work like that. You need people who understand the whole pipeline, not just their bit of it. Everyone should have a sense of how raw data becomes meaning. They should also be able to cover for each other when illness, turnover or last-minute requests hit. That only happens when knowledge is shared around, not hoarded in silos.

Of course, people still need areas of focus. Specialists are good. But the specialism needs to sit within a shared map of the work. Otherwise, the finance analyst is baffled by clinical coding, the coding analyst is baffled by risk adjustment, and the whole thing spirals into finger-pointing or quiet despair.

Avoid building teams around just the tools. A Tableau or Power BI wizard who doesn’t understand admission cycles is less useful than a moderately skilled analyst who knows when the flu season starts and why it matters. And if you’re wondering whether to divide your team into specialties - well, maybe. But, again, only if you’ve got someone connecting the dots. Otherwise you’ll just have multiple echo chambers, each convinced they’re the only ones with the full picture.

Another bit that matters more than people often realise is the business-to-analytics translator. Not someone who just rewords things, but someone with enough experience to challenge fuzzy requests and reframe them in terms the team can actually use. They need to be senior enough to hold the line, respected enough to keep boundaries clear, and clear-headed enough to spot when a request isn’t ready for analysis. The business should ask the business question. That question should be shaped into a proper analytical one before anyone starts coding. Get this wrong and you’ll waste days churning out analysis that isn’t quite what anyone wanted. So before any dashboards are built or code is written, someone needs to translate the business intent into something analytically testable. And back again when the results come in. This doesn’t just save time, it means the analysis has a fighting chance of landing where it needs to.

And one more thing: don’t forget the ‘quiet linchpin’. Every good analytics team has one. It’s the person who doesn’t say much but somehow keeps everything from catching fire. If you don’t know who that is on your team, it probably means you’re new.

This stuff isn’t exclusive to healthcare. The same principles apply whether you’re looking at patient pathways or warehouse logistics. But in healthcare, the stakes feel stranger. The outputs aren’t just numbers on a page. They can change how someone is treated, or whether they get treated at all. So, structure matters. But not in the tidy, grid-on-a-slide-deck kind of way. More like plumbing. It’s invisible when it’s working, but catastrophic when it’s not. And if you’re still not sure whether your team’s shaped right, ask this: can they handle being wrong, learn fast, and still keep their sense of humour?

If all this sounds complicated, it’s because it is. Which is why at the Consortium for Healthcare Analytics (CHAIn), we don’t offer off-the-shelf models. NHS teams are too varied, too embedded in their own realities for that. Instead, we take time to understand how a team is built, where it sits in its development, and how well its structure fits the actual demands being placed on it.

Some teams are running ahead but need better internal handovers. Others are just forming and unsure who should be doing what. We look at both. We help teams see where the friction is, where things are working, and where something needs to shift. Our approach isn’t about bolting on training for the sake of it. It’s about shaping teams in a way that fits the analytical reality of the NHS, not an abstract framework built for someone else’s organisation. With over 60 years of combined experience, we’ve helped teams across the service develop structures that don’t just get the job done, but stay resilient when things get messy. Which they always do.

So if you’re unsure whether your team is shaped right, or if you’re quietly aware that something’s not quite clicking, give us a shout. We’re CHAIn. We don’t do factory models. We build teams that work.

hdavies@chainintelligence.org

jrvarlow@chainintelligence.org