ESTATE MAINTENANCE
Specifically, sites with higher backlogs tend to
Non-inpatient sites 800 600 400 200 0 0 500 Total backlog intensity (£/m2
1000 1500 2000 2500 )
This evidence base then creates a much better case to be presented to senior stakeholders. Carefully considering how risks are assessed including the impact on clinical services, the likelihood of failure, and what mitigations have already been done, creates a foundation for an argument for investment in a much better way than a fear-based approach around high-risk scores that are not underpinned by evidence. Finally, any plan and methodology must be designed
on the assumption the plan will change. New items will arise, Trust circumstances will change, and a well-thought- out strategy that accommodates the fact that things will change will lead to results far more likely than a strategy that assumes an environment like an NHS Trust will remain static. A first step would be to combine available data, such as six facet surveys or risk databases, and integrate it into a sensible, quantifiable risk analysis, in accordance with NHS Risk based methodology for managing backlog.4
For example, in this scatter plot we observe a clear positive correlation between backlog and EUI for non- inpatient sites.
exhibit higher EUI and lower Green energy %.
Graph comparing non-inpatient sites with maintenance backlog intensity.
Once risks are quantified,
taking into consideration predicted budget allocations, a deliverable multi-year programme of works is produced. This programme will focus on risk reduction first, but impact on service delivery and costs are taken into consideration as well. Projects are sequenced within available budgets, including Trusts’ priorities for each financial year. We have found this works well and this methodology helps to minimise any potential discipline bias when risk rating. Risks which translate into short-term deliverable projects are considered for year- end last minute funding allocations. Risks do not remain static over time. Smaller risks which
are not remediated on time can grow until they become a widespread problem in the hospital. Increasing maintenance or energy costs or systems failures which can close entire wards, disrupting patient care and clinical delivery, are some examples of the potential consequences. For example, a pump failure which results in increased legionella counts, with subsequent cost of legionella filters and testing, increases risk for patients and longer hospital stays. Similarly, budgets or priorities can change year after
year. For example, new financial constraints can lead to lower budgets, newly discovered risks, or need to integrate newer clinical areas, can lead to a re-prioritisation of works, which were not envisaged in the beginning. This is why multiple iterations and scenarios can be
run from the data, which can be used to present possible scenarios and risk growth to decision makers, or to ensure further funding in a clear manner. This enables estates teams to present a clear, evidence-based strategy to the Board.
Blanca Beato Arribas
Blanca Beato Arribas is an associate at Hoare Lea with over 20 years’ experience in building services, specialising in healthcare environments. With a strong NHS background, she supports organisations in improving safety, operational resilience, and regulatory compliance across complex estates. Her expertise in hospital ventilation, particularly isolation rooms, is supported by a PhD and contributions to research and industry guidance. She works closely with multidisciplinary teams to deliver practical, risk-aware solutions that support continuity of care.
September 2026 Health Estate Journal 71
EUI (kWh/m2
)
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