‘Digital mimics’ among 16 projects launched to help NHS with winter pressure | Science & Tech News

Researchers are using artificial intelligence (AI) to “digitally mimic” households struggling with the impact of the cost of living crisis to simulate effective ways to help. Best.

It is one of more than a dozen projects spanning from data analytics to machine learning that have been launched to help combat the winter pressures facing the NHS.

It comes as the health service faces the strain of large numbers of flu and COVID cases, a large backlog exacerbated by the pandemic, and waiting times for ambulances and care. emergency and routine care is increasing.

16 projects, initiated by Health Data Research UK (HDR), are expected to produce results by the end of March.

Health Secretary Steve Barclay said the aim was to instill the “spirit of innovation” that led to the rapid rollout of a coronavirus vaccine, with the government providing £800,000 in funding.

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While many projects seek to use technology to relieve pressure on hospital staff, others seek to address some of the root causes of the troubles facing the NHS.

One such initiative using existing data and AI to “digitally mimic” home environments and simulate interventions could improve people’s health standards at home – particularly. especially children.

Dr Martin Chapman, from King’s College London, explains: “Living in cold, damp and moldy homes leads to chest diseases in children and mental health problems in adolescents. years, and rising energy costs mean more people than ever before are living in poverty due to high temperatures.

“We are investigating the effect of interventions such as supporting energy bills on the health of young people by using AI to digitally simulate their home environment and assess effects of simulated interventions.

“This will help guide future policy changes to improve health, reduce inequality and thereby reduce pressure on NHS services.”

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What are some projects?

Using the same infrastructure that powers the Siren, collated and released regular public data on COVID at the height of the pandemicA sub-study of winter pressure will see it expand to include the flu and a common childhood illness called Respiratory syncytial virus (RSV).

Another project aims to use AI to help clinicians more easily identify patients at higher risk.

By analyzing patient data, an AI model can recommend the most suitable area for patients, who is at risk of immediate deterioration, and when someone should be discharged.

Also focusing on discharge time is a project called DS4SmartDischarge.

This uses machine learning (the process of teaching a computer to do something on its own) to help the computer classify patients based on their risk of different discharge outcomes.

Another group that includes paramedics, hospital leaders, and the Society for Acute Medicine is also using machine learning to help build a model that identifies patients in need of urgent care during the day.

Patients will be triage based on data such as blood pressure, medications and bedside tests, helping staff make decisions within four hours of their arrival at the hospital.

Project leader Professor Elizabeth Sapey said the work would help “reduce inequalities in care and relieve pressure on emergency services”.

‘Quick response to increasing pressure’

Although the projects come too late compared to the current crisis plaguing the health service, it is hoped that they will produce results that will help it cope better in the long term.

Professor Cathie Sudlow, chief scientist at HDR, said they would focus on “key pain points” in the NHS.

“By using existing data, research teams and infrastructure, these projects can respond quickly to growing pressures on the NHS,” she added.

Each project has been collaborated with analysts in the Ministry of Health, which funds the plans; National Bureau of Statistics; and the UK Health Security Agency.

Once the findings are released in March, it is expected they will be published by the end of the year.