Ashleigh Dueker, Programme Director for Care Intelligence at digital care technology company Person Centred Software, (PCS), explores how predictive insight is helping care teams identify rising risk earlier and act with greater confidence.
Falls remain one of the leading reasons older people attend A&E, and in the hard winter months this reality becomes even more stark. They cost the NHS an estimated £2.3 billion every year, with fractures and fragility injuries adding a further £1.1–£4.4 billion annually. Beyond the financial strain, these incidents bring pain, distress and loss of independence for residents and their families, while adding pressure to an already overstretched system. But what if we could predict falls — and radically reduce both their severity and frequency — through predictive insight?
As Programme Director of Care Intelligence at PCS, I’ve seen first-hand how digital care is evolving. We’re exploring how to turn the wealth of data we hold into genuine foresight, starting with falls risk as a major area of focus. Supporting over 8,400 care providers, we hold the largest structured social care dataset in the sector. With more than 18.5 billion care observations, we are now able to turn this data into foresight in a way we have never seen before.
Every PCS app — from care planning to medication management to rostering — feeds into a unified data layer that increasingly powers something far more valuable: intelligence. Our IQ platform already transforms this data into actionable insight. Providers can benchmark their performance against similar homes nationwide, evidence quality improvements to families and regulators, and identify where interventions are making a measurable difference.
The logical next step is moving from understanding what happened to anticipating what might. Predictive insight represents a fundamental shift in how care can be delivered, flagging when risk is rising and suggesting that something needs attention now, rather than after an incident has occurred. As care operators know well, most falls don’t come out of nowhere. There are usually warning signs — subtle, cumulative changes that only become obvious in hindsight. A resident may be more tired than usual due to increased nighttime movement. Fluid intake may have dipped. A new medication may be unsettling them. A urinary infection may have been treated only days earlier. Each factor alone may not feel alarming, but together they can significantly increase the risk of a fall. So the question I increasingly find myself asking isn’t simply how we reduce falls with injury, but what is the cost of not knowing sooner?
When we talk about the economics of intelligence‑led care at PCS, we believe the benefits are far‑reaching. The NHS benefits when avoidable acute admissions are prevented. Local authorities benefit when escalation to higher‑cost care can be delayed or avoided. Providers benefit because care becomes less reactive and outcomes improve. Most importantly, preventing an acute fall or minimising a sudden decline means less distress and more time living well for residents — giving families greater confidence that changes are being noticed and acted on earlier.
Of course, scepticism around predictive care is understandable. Too often, technology has been presented as a cure‑all that will fix the unfixable or replace the human touch. We know that data intelligence does not replace leadership, training or compassionate care, nor does it solve structural funding challenges. What it can do — and is already doing — is helping care teams spot risk earlier. It flags when risk is rising and suggests that something warrants attention now. In that sense, predictive insight isn’t a replacement for care; it’s an additional tool and an early warning system.
Predictive tools are not prescriptive. They do not make decisions. Accountability for care will always sit with the brilliant and dedicated professionals delivering it. What predictive insight does is augment expertise, not replace it. It brings patterns to the surface sooner, allowing experienced staff to apply their judgement earlier and with greater confidence. Crucially, predictive insight also creates faster feedback loops. Teams can see whether changes to care plans, hydration assessments or monitoring reduce risk — and adjust if they don’t. It becomes another tool in the toolkit, helping staff act with more clarity rather than more pressure.
In the care sector, challenges are unique and complex. That’s why we are approaching this cautiously, with testing, validation and close collaboration with care teams, because predictive care only works if it genuinely supports the people using it. Not seeing risk doesn’t make it disappear; it simply means the cost shows up later — in A&E departments, hospital beds and human suffering.
This is about giving care teams a better chance to intervene earlier, reduce harm and ease pressure where they can. In a system as stretched as ours, that feels like an opportunity we should not miss — and a responsibility we take seriously.
