Underused Estate: The Hidden Lever in NHS Green Plan

NHS Green Plans have placed estates firmly at the centre of sustainability, with clear expectations around reducing environmental impact while continuing to support safe, effective care. Much of the conversation has understandably focused on improving how buildings perform – energy systems, infrastructure upgrades and more efficient operations.

Alongside this, attention is beginning to shift to the shape of the estate itself. Not every part of the footprint carries the same level of demand, and that difference is becoming harder to ignore as sustainability expectations tighten. 

Activity within NHS environments is rarely uniform. A space that feels stretched at one point in the day can sit lightly used for much of the week. Administrative areas have shifted with hybrid working. Shared spaces tend to experience uneven demand rather than sustained use. None of this is unusual, but it is not always visible in a way that supports confident decision-making.

At the same time, expectations around data are evolving. The recent NHS England Digital Maturity Assessment has brought greater visibility to how trusts adopt and apply digital tools, with clearer benchmarking. This reflects a wider shift towards using data more consistently to support operational and strategic decisions. In that context, areas such as space management are increasingly being viewed through a more data-driven lens. 

Where utilisation meets sustainability

Buildings are designed and operated to support capacity. Heating, cooling and ventilation systems do not scale neatly with day-to-day variation in use; they are configured to maintain conditions regardless. This means that the environmental footprint of a space is often tied to its availability, not its actual level of activity.

When utilisation is observed over time, rather than at isolated moments, it becomes easier to see how demand is distributed. Peaks tend to be shorter than expected. Some areas carry steady demand, while others fluctuate or remain lightly used for extended periods.

From a sustainability perspective, this shifts the conversation. It is no longer only about how efficiently buildings run, but also about how closely the estate aligns with real patterns of use. Where that alignment is off, energy demand and the cost attached to maintaining it continues in the background, largely unchanged.

Turning visibility into practical decisions

When space is looked at in detail, it becomes easier to pinpoint where change is possible without creating knock on effects elsewhere. Some areas absorb far less activity than their footprint suggests. Others operate closer to their limits, but only at specific times. Seeing those differences side by side changes how decisions are framed. It moves the conversation away from general reductions and towards targeted adjustments.

This might mean reconfiguring a cluster of rooms that are consistently underused, rather than reviewing an entire floor, or adjusting how shared spaces are supported across the week instead of treating demand as constant. In some cases, it simply means recognising that two areas with similar layouts behave very differently and should not be managed in the same way.

This is where utilisation becomes useful in a more practical sense. Not as a headline figure, but as a way of narrowing focus. It allows estates teams to work with what is already happening in the building and make changes that feel proportionate rather than disruptive. Platforms such as OpenSensors support this by providing a continuous, anonymised view of how space is used, helping teams identify where small, well-placed changes can have a meaningful impact before larger decisions are considered.

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Yodit Stanton

Yodit has designed and built large scale data systems for various sectors and has played a key role in leading development teams to run critical trading and machine learning infrastructure for FTSE500 companies such as, Deutsche Bank, Man Investments, Nomura and Lehman Brothers. With over two decades of experience as a Data and Machine Learning Engineer, Yodit specialises in predictive modeling for real time systems, social network analysis and middleware development.

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