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Smart lights linked to fewer care home falls

AI smart lights in care homes were linked to up to 75 per cent fewer hospital visits after falls, according to an NHS evaluation.
The study examined 87 rooms across seven care homes providing residential, nursing, dementia and assisted living care.
Researchers compared six months of baseline data with six months after installing Nobi Smart Lights, AI-enabled ceiling-mounted devices designed to detect falls and alert staff within seconds.
The lights also turn on automatically when residents get out of bed, helping reduce the risk of night-time falls. Some homes reported zero fall-related hospital admissions during the evaluation period, while ambulance call-outs fell by up to 65 per cent.
Staff reported greater confidence when responding to unwitnessed incidents and said they spent less time reconstructing events or completing documentation.
Better visibility also helped staff distinguish genuine falls from controlled descents, where someone lowers themselves to the floor intentionally or slowly, allowing more incidents to be managed safely inside the care home.
The evaluation was carried out by the Suffolk and North East Essex Integrated Care Board.
“The Nobi light gives me peace of mind because Mum does fall a lot,” said the daughter of a resident at a participating Suffolk care home.
“I felt guilty about her going into a home, but now I know staff are alerted instantly and can be there straight away.”
The work formed part of the Integrated Care Board’s Digitising Social Care Programme, which supports care providers to adopt digital tools.
Implementation was delivered in partnership with Porters Care, one of Nobi’s UK partners, with support from Suffolk County Council and participating care providers.
Using NHS reference costs, the evaluation estimated £89,000 in avoided emergency care costs over six months, equivalent to a projected return on investment of around 196 per cent over three years.
Roeland Pilgrims, chief executive and co-founder of Nobi, said: “This independent NHS evaluation shows how intelligent care technology can deliver measurable improvements for residents, care teams and the wider health system.
“By giving staff timely, reliable insight, we can help reduce avoidable hospital admissions while improving safety, dignity and peace of mind.”
David Knowles, managing director of Porters Care, added: “These findings show the real-world impact of smart technology in care homes.
“By improving how falls are detected and understood, Nobi helps teams make clearer decisions and avoid unnecessary hospital admissions, while keeping residents safe.”
Further independent NHS-led evaluations are underway in other regions of the UK.
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AI can predict Alzheimer’s with almost 93% accuracy, researchers say

Alzheimer’s AI can predict the disease with nearly 93 per cent accuracy using more than 800 brain scans, researchers say.
The system identified anatomical changes in the brain linked to the onset of the most common form of dementia, a condition that gradually damages memory and thinking.
The findings build on years of research suggesting AI could help spot early Alzheimer’s risk, predict disease and identify patients whose condition has not yet been diagnosed.
Benjamin Nephew, an assistant research professor at the Worcester Polytechnic Institute in Massachusetts, said: “Early diagnosis of Alzheimer’s disease can be difficult because symptoms can be mistaken for normal ageing.
“We found that machine-learning technologies, however, can analyse large amounts of data from scans to identify subtle changes and accurately predict Alzheimer’s disease and related cognitive states.”
The study used MRI scans, a type of detailed brain imaging, from 344 people aged 69 to 84.
The dataset included 281 scans showing normal mental function, 332 with mild cognitive impairment, an early stage of memory and thinking decline, and 202 with Alzheimer’s.
The scans covered 95 of the brain’s nearly 200 distinct regions and used an AI algorithm to predict patients’ health.
Being able to use AI to help diagnose Alzheimer’s earlier could give patients and doctors crucial time to prepare and potentially slow the progression of the disease.
The analysis showed that one of the top predictive factors was brain volume loss, or shrinkage, in the hippocampus, which helps form memories, the amygdala, which processes fear, and the entorhinal cortex, which helps provide a sense of time.
This pattern held across age and sex, with both men and women aged 69 to 76 showing volume loss in the right part of the hippocampus, suggesting it may be an important area for early diagnosis, the researchers noted.
However, the research also found that the way brain regions shrink differs by sex.
In females, volume loss occurred in the brain’s left middle temporal cortex, which is involved in language and visual perception. In males, it was mainly seen in the right entorhinal cortex
The researchers believe this could be linked to changes in sex hormones, including the loss of oestrogen in women and testosterone in men.
These conclusions could help improve methods of diagnosis and treatment going forward, Nephew said.
More than 7.2m Americans are living with Alzheimer’s, according to the Alzheimer’s Association.
More research is being done to reveal other impacting factors.
Nephew said: “The critical challenge in this research is to build a generalisable machine-learning model that captures the difference between healthy brains and brains from people with mild cognitive impairment or Alzheimer’s disease.”
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