How AI is reshaping healthcare real estate demand

Augmenting, not replacing

Several AI advancements in healthcare are changing the role of clinicians and other healthcare staff, including AI-assisted diagnostics, real-time health monitoring, clinical note-taking and workflow management.

Nicola Lynch, health and education industry leader at PwC Australia, says removing the administrative burden can free clinicians to perform more valuable work.

“When implemented responsibly, AI can [help] deploy people, equipment and medicines more effectively,” she says.

“For patients, that means more engagement with clinicians and a better care experience. For frontline workers, it has the potential to ease stress, lower the risk of burnout and improve overall job satisfaction.”

However, JLL real estate economist Ronak Bhimjiani doesn’t believe clinicians will be replaced because healthcare is a different proposition from other sectors that have faced significant workforce displacement.

“JLL’s global research highlights that, unlike office-heavy sectors where AI may result in displacement depending on white-collar industry exposure, healthcare is seen as an ‘AI augmentation’ sector,” he says.

“AI is being used to support and scale clinicians rather than replace them, keeping core real estate growth drivers intact.”

Redesigning clinical space efficiency

Bhimjiani says the underlying drivers of healthcare real estate mean AI will help optimise how space is used rather than whether it’s necessary.

“AI will fundamentally redesign what happens inside the square meterage, rather than shrink the aggregate healthcare footprint,” he says.

“As AI-enabled administrative tools automate clerical work and regulatory documentation, the traditional back-of-house ‘administrative bulk’ of a clinic will shrink.

“However, this space could be converted back into high-value clinical treatment rooms, advanced diagnostic labs and care spaces. The net result is higher clinical output per square metre.”

Ramsay Health Care is one organisation exploring AI to improve operational efficiency.

Director of real estate and facilities Trent Morosin says technology is starting to influence how the team understands and manages its assets, improving decision-making on everything from space utilisation to capital expenditure.

“The largest opportunity is around better data and evidence to support decisions around our property portfolio, our assets and our capital decisions,” he says.

“I really see the opportunity to … move away from the siloed information pools to actually being able to synthesise data in one coherent location. That tends to give us a better understanding of how an asset is actually performing.”

That improved understanding then feeds into financial decisions, helping determine whether new developments or capital outlays are actually necessary.

“We can model utilisation, we can look at flows – both patient flows and operational flows – look at processes, maintenance requirement space; really understand how the asset is utilised now,” Morosin says.

“Can we make operational adjustments to better utilise the space before jumping into a need for more?”

Predictive AI maps demand

A particularly useful area for AI is forecasting location demand. Bhimjiani points to Australia’s ageing population as one of the primary drivers of where new healthcare facilities are located, with AI able to help inform site selection.

“ABS data indicates that Australia’s population is undergoing a massive ageing transition, with the 65-plus cohort currently making up roughly 17 per cent of the population but accounting for 45 per cent of all hospital visits,” he says.

“The [federal government’s] 2026 Intergenerational Report projects this proportion will increase to 25 per cent of the population by 2066.

“Predictive AI tools could enable healthcare operators to anticipate where the ageing population demand wave will occur, allowing them to establish facilities in specific growth corridors before the supply shortage becomes critical.”

Lynch says the advantage of using AI to anticipate demand is that it provides richer insights.

“Rather than rely on historical data, AI will also allow healthcare organisations to use deeper real-time insights to anticipate changing patterns of demand, coordinate how space is being used, decide which services are best placed to meet that need, and how care can be delivered earlier and more effectively,” she says.

With older generations increasingly looking to age in place, patients may not necessarily need to travel to hospital for lower-acuity care. Instead, home-based and community healthcare settings may become more prominent, supported by emerging technologies.

“AI-enabled tools, remote monitoring, wearable devices and virtual technologies can support more personalised and predictive care, while keeping patients connected,” Lynch says.

“If done well, this has the potential to improve patient outcomes and reduce pressure on stretched parts of the healthcare system.”

Bhimjiani believes this shift could change hospitals’ roles, allowing them to handle more complex cases and distribute lower-acuity ones among the broader healthcare network.

“Given technologically led advancements across healthcare, such as AI-assisted minimally invasive surgeries and predictive patient monitoring, patient recovery times could decline,” he says.

“Consequently, private day hospitals and specialised clinics will experience a surge in demand as procedures migrate away from capital-intensive overnight hospitals.”

Digital infrastructure challenges retrofits

Bhimjiani says a major challenge for healthcare real estate is that AI enablement requires more energy, power and digital infrastructure.

“The largest physical market implication will be the intense requirement for additional energy use,” he says. “For healthcare real estate, this means properties will need substantial upgrades to power supply, high-speed connectivity, including specialised cooling to support high-performance AI diagnostics.”

Morosin says requirements around structural capacity, electrical loads, cooling, data and connectivity are changing the way Ramsay assesses potential assets, particularly because technology and equipment typically have much shorter lifespans than the buildings themselves.

He adds that this challenge is especially acute with existing assets, as older hospitals weren’t necessarily designed for new technologies.

“Existing hospitals, for instance, aren’t really designed for today’s power and data connectivity,” he says. “There are physical constraints that we have within some of our buildings already, such as floor heights, structural capacity and existing building services.”

Healthcare settings can be difficult to retrofit because upgrades often need to happen alongside theatres, wards and clinical services, which need to remain operational despite the works taking place around them.

That said, Morosin believes this is where AI could prove beneficial, using existing building data to determine the feasibility of upgrades and capital expenditure.

“AI can help bring that together – whether it’s [business management systems, heating and cooling], electrical demand, asset performance – to really understand whether there’s an apparent infrastructure constraint on the building itself that requires capital investment,” he says.

The opportunity to optimise healthcare assets to meet future demands presents a new set of considerations for landlords and investors. Ultimately though, Lynch believes property should be designed with enough flexibility to adapt.

“A healthcare asset commissioned today will need to accommodate vastly different technologies, clinical practices and levels of care over its life,” she says.

“This makes adaptability and connectivity key considerations, and increasingly valuable, when landlords and investors are considering buying or building property.