Health Navigator launches £900,000 co-investment fund for NHS trusts and integrated care boards
Health Navigator's 'wave 2 innovation fund' will support NHS organisations to identify patients at risk of adverse and preventable medical events and help to reduce unnecessary hospital admissions
Healthtech company, Health Navigator (HN), has launched a new co-investment fund for NHS trusts and integrated care boards (ICBs).
The £900,000 offering is open to the first 10 NHS organisations to express an interest in HN’s proven AI-based programme, which has demonstrated significant system savings across hospital capacity, urgent and elective admissions, and improved patient safety scores and outcomes.
HN is referring to the new scheme as its ‘wave 2 innovation fund’, following its successful first trial conducted between 2015-2022.
HN’s offer is its ‘AI-Guided Clinical Coaching’ (AICC) programme, which combines data scientific and AI capabilities with digital services and is delivered by skilled healthcare professionals.
The model enables organisations to accurately identify patients at risk of adverse and preventable events such as uncontrolled disease progression, hospitalisation, and prolonged hospital stay.
And it is proven to help restore and recover ailing urgent and emergency care services.
A new model
HN has committed its feasibility study – a detailed analysis of healthcare consumption patterns including patient demographics and inequalities data, and worth up to £90,000 per integrated care system (ICS) – to be included as part of this wave 2 scheme.
The deadline to register is 31 June.
The news comes as recent figures showing a waiting list of over seven million people, in addition to record numbers of patients waiting more than 12 hours for treatment at A&E units.
In the earlier first-wave trial, a total of 1,800 patients across eight acute NHS trusts in England were identified by HN’s AI tool as being at high risk of using urgent and emergency care services in the next few months.
The launch of this new co-investment offer represents an important opportunity for the NHS to embrace AI and machine learning tools proven to have a positive impact on patients and the system alike
These patients were offered a personalised coaching service over the telephone.
Delivered by registered healthcare professionals, the service is designed to support patients with complex conditions and empower them to take control of their health, thus reducing A&E admissions and unplanned emergency care.
Quality of life
This moves away from a purely-reactive model and has the potential for more person-centred care.
The findings of the trial directly resulted in a 35% reduction in A&E attendances and a 30% reduction per patient in the average total hospital care cost at one of the trial sites in Staffordshire ICS.
Results from other sites show a similar positive impact, in some cases demonstrating up to 59% reduction in unplanned admissions for patients receiving the intervention.
In a clinical trial published in the British Journal of General Practice, one in four referrals to hospital identified by the AI tool could be prevented by supporting these high-risk patients, with nurse-led, virtual ward support.
Patients supported through this novel predictive and preventive clinical pathway also reported increased quality of life, improved physical health, and an increase in their ability to manage their health conditions.
And HN’s analysis shows a significant positive impact on survival rates of those patients enrolled onto its programme.
The full analysis is expected to be published in a peer-reviewed journal before the summer.
HN’s work in this area shows a model that can work and we are excited to scale our work with NHS partners and make an instant and lasting impact to health systems across the country
Dr Joachim Werr, founder and executive chairman of HN, said: “The launch of this new co-investment offer represents an important opportunity for the NHS to embrace AI and machine learning tools proven to have a positive impact on patients and the system alike.
“Our lengthy and robust trial with the Nuffield Trust has demonstrated that patient outcomes are improved by intervening in a timely and appropriate manner, giving them the confidence and support to live well, at home.
Relieving the pressure
“With the pressures on the NHS becoming an increasing year-round phenomenon, we are well equipped and can rapidly roll out this important technology to save lives and improve the urgent and emergency care system before winter comes around”.
Mark England, chief executive, added: “By arming ICSs with data and prediction technology, we can begin to manage health and care in a more-sustainable and impactful way that delivers better patient outcomes whilst saving costs.
“Those living with long-term conditions and advancing frailty will inevitably increase, so as part of this model we need to shift the focus to patient self management through proven methods such as health coaching.
“HN’s work in this area shows a model that can work and we are excited to scale our work with NHS partners and make an instant and lasting impact to health systems across the country.”
And, commenting on the impact the technology has had, Dr Paddy Hannigan, chairman of the Stafford and Surround CCG and clinical lead for Staffordshire and Stoke-on-Trent ICS Digital Programme, said: “HN’s offer was transformational for us in Staffordshire and it is applicable to every ICS across the country.
“Data can play a huge role for the NHS if it is collected, analysed, and acted upon in the right way.
“The key to our work with HN was the data-driven case finding.
“By using data to better forecast demand and predict outcomes we were able to manage the resources we had accordingly, and of those patients on the intervention, reduce hospital care costs by 30% per patient.”
If applied to the NHS as a whole, the technology and related clinical nurse-led services, could prevent 5-7% of all unplanned hospital care and save £2,200 per patient.
But it would require the technology to be made available to the 1-5% of patients with the highest risk of clinical crisis and unplanned care.