How providing unique risk-adjusted systems for individualised patient care can drive down avoidable harm, mortality, and cost in hospitals
Winter is always a challenging and unpredictable time for the NHS.
The cold weather triggers a wide range of respiratory illnesses affecting large swathes of the population, and the four-hour waiting time target is put to the test as A&E departments deal with the surge in demand.
Although trusts are working hard to keep up with increasing demand, they are operating at capacity levels beyond those which other international health systems would regard as acceptable.
But, despite media headlines to the contrary, for many hospitals, the number of patients who attend A&E over the winter is not the primary problem; it is the number of patients that need to be admitted.
The effect of winter bugs and respiratory problems on the older patient population, and the consequences of falls in difficult winter conditions, is a huge causal factor.
This results in both higher emergency demand and more pressure on remaining bed stock – including critical care settings such as intensive care and high dependency units – which can make the difference between life or death for the sickest patients.
“The NHS is already at a bed occupancy rate that is well above where it needs to be, a situation that is both problematic for staff and disruptive for patients,” says Stephen Mackenney, chief executive of precision healthcare and analytics specialist, C2-Ai.
The NHS is already at a bed occupancy rate that is well above where it needs to be, a situation that is both problematic for staff and disruptive for patients
“Beds have come under increasing pressure in the past few years as more people are referred to hospital and medically-fit patients are not discharged on time.
“And, as we know, when bed occupancy rockets, the NHS is less able to cope with seasonal problems, such as norovirus and winter flu.”
In its Winter Warning report, NHS Providers highlights that the sharp rise in delayed transfers of care (DTOCs) was a key factor behind the health and social care system’s struggle to cope under sustained pressure, despite extraordinary efforts from staff.
Harms that come from within hospital only exacerbate these problems.
Hospital Acquired Pneumonia (HAP) is one of these harms and, although rarely publicised, stretches the NHS year-round, with the strain only amplified during the winter months.
HAP is a form of pneumonia that develops in hospital, and it generally occurs 48-hours after admission or post intervention with those patients in intensive care on breathing machines particularly at risk of developing ventilator-associated pneumonia (VAP).
These types of pneumonia create huge disruption in hospitals, increasing length of stays, delayed discharges, and costing the NHS between £10,000-20,000 per patient. Not only this, but tragically between 3,000-6,000 people die from VAP every year – so identifying and helping to reduce respiratory problems would save many lives.
There is also a serious knock-on effect. Secondary harms like HAP, VAP and AKIs result in patients being escalated to critical care wards, to receive care and medication that when first admitted they did not need.
The escalation of these patients to ITUs then ‘bed-block’ others who need primary critical care, resulting in NHS trusts being put under further strain, and, sadly, to higher levels of mortality.
“It is because of figures like this that hospital systems often find themselves under the microscope when it comes to how effective they are at highlighting and managing avoidable harms,” said Mackenney.
“Most reporting systems look at a shortlist of harms that are easy to count, such as pressure ulcers, falls, and wound infections, and simply produce prodigious amounts of data about them.
“They also highlight single issues like Acute Kidney Injury (AKI), but focus on identifying and treating, rather than reducing and pre-empting.
“But, with the pledge of £250m from the Prime Minister to boost artificial intelligence and genomic testing in the NHS, we are seeing a shift in the type of hospital system required by a truly-modern-day NHS.
Innovative technology allows healthcare organisations to set a benchmark and gold standard for improving quality, reducing harm and variation in care, as well as delivering much-needed cost efficiencies
“Health secretary, Matt Hancock, further expands on this stating that: ‘We are on the cusp of a huge healthtech revolution that could transform patient experience by making the NHS a truly-predictive, preventive, and personalised health and care service’.
“Innovative technology allows healthcare organisations to set a benchmark and gold standard for improving quality, reducing harm and variation in care, as well as delivering much-needed cost efficiencies.
“Ai, in particular, can be used to highlight any potentially-harmful issues that need resolving and then demonstrate, by continual monitoring, that a hospital is safe.”
Harms like HAP and VAP, though not always avoidable, can certainly be reduced.
This means that if they were managed prospectively by a more-advanced, Ai-based hospital system; then the strain on hospitals could be pre-emptively reduced.
MacKenny said: “By using money pledged by the Government there is a potential for the implementation of more-effective hospital reporting systems that can highlight avoidable harms, their root causes, and the clinical solutions neededto address them, helping to drive much-needed efficiencies within our healthcare system.
“This, in turn, has the potential to better prepare NHS services, or at least help to manage expectation on them, going into the winter months.
C2-Ai systems, CRAB and COMPASS, as an example, harness artificial intelligence to go well beyond the level of standard reporting systems.
Providing unique risk-adjusted systems for individualised patient risk prediction, they help to avoid harm and mortality in hospitals.
MacKenny explains: “The systems work by using prevention as the best cure, identifying the systemic root causes of problems like HAP and AKI – and how to tackle them – and flagging those patients at the greatest risk of harm in advance.
“This ensures that they are on the correct treatment pathway, with optimal care, from the outset to reduce care variation and anticipate and avoid harm from occurring in the first place, thereby reducing avoidable costs and mortality rates.
By using money pledged by the Government there is a potential for the implementation of more-effective hospital reporting systems that can highlight avoidable harms, their root causes, and the clinical solutions neededto address them, helping to drive much-needed efficiencies within our healthcare system
“C2-Ai’s prospective and retrospective systems have been successfully deployed around the world, including in a number of NHS trusts.
“In the UK, this has resulted in over £20m cost savings and a 60% reduction in avoidable harm events within a year of deployment.”
C2-Ai systems harnesses artificial intelligence to go well beyond the level of standard healthcare reporting systems
Reliably identify past harms in a timely way
Receiving data and information immediately to minimise harms and mortality.
Most hospital systems work from Disease Resource Groups (DRG) coding when an issue is identified. However, DRG codes do not reflect the reality of how clinical departments are organised.
With the right technology it is possible to accurately identify and code issues in a way which identifies the root cause and the cases that need to be reviewed. This will then enable clinical teams to drill down and resolve issues in a far-more-efficient, targeted and effective way.
Identifying avoidable harm allows hospitals to save lives and costs in terms of re-admissions, admin, medications, bed blocking etc – especially important during the stretched winter months.
Reporting needs to also highlight avoidable harms as part of a comprehensive solution within truly evidence-based safety.
When a genuine risk-adjusted methodology is used, an accurate picture of performance can be obtained, set against national and international benchmarks; and thus, any underlying issues can be identified and resolved.
The best systems – especially those backed by Ai to do the complex groundwork – present the problem and the solution in a nutshell so that you can focus on making the improvements and reducing risk of further mistakes.
Preventing harm is the best cure.
If a hospital system can identify patients at greatest risk of harm in advance this will ensure they are on the correct treatment pathway resulting in patient care being optimised from the outset.
The best systems – especially those backed by Ai to do the complex groundwork – present the problem and the solution in a nutshell so that you can focus on making the improvements and reducing risk of further mistakes
This reduces care variation, anticipating and avoiding harm before it even occurs.
Extrapolating from our current figures, if a hospital reporting system can identify those patients at the greatest risk of harms, such as ventilator-associated or hospital-acquired pneumonia, before they occur the NHS would save an extra 5,000 lives per year and around £1billion – as well as being better equipped to deal with winter pressures.