Could lessons from NHS waiting lists help to boost life sciences in an election year?

An AI-driven risk methodology is helping to safely manage everything from waiting lists to hospital safety. But the approach doesn’t only belong to health.

It could now help to transform medicines strategies, target pharmaceutical research and enable life sciences policy commitments.

When one hospital in the North East of England reduced hospital-acquired acute kidney injury (AKI) by more than 80%, it duly made the headlines.

Recognised for achievements around a condition linked with many thousands of deaths, staff had used a predictive AI model to help to understand AKI risks, find patients early, and respond with swift and appropriate measures.

This innovative use of technology put into practice a concept known as ‘clinical risk adjustment’. The process is being used increasingly to understand the changing clinical risk of individual patients, as well as the safety, performance and effectiveness of services and interventions.

With wide-ranging applications, the results of applying clinical risk adjustment have continued to hit headlines.

Notably, pioneering surgical teams have used the approach as a forward-looking AI tool, to understand dynamic and complex risks facing patients on growing elective waiting lists.

Finding hidden high-risk patients in an entirely new way, and taking action to mitigate harm before it becomes reality, has seen remarkable results reported in academic papers and a study by NHS England.

And resulting insight into patient outcome trajectories is generating excitement with integrated care systems, informing how to leverage system-wide resources to manage demand more appropriately and effectively.

Clinical risk adjustment is publicly demonstrating its worth in healthcare systems around the world. It has the potential to change how we understand everything from maternity to surgical safety, and much more.

But this idea doesn’t only belong to health, especially in an election year that is witnessing policy commitments that transcend political boundaries, with promises to boost life sciences innovation.

This presents a new and unique opportunity to transform established business models in life sciences, based on a better understanding of the real unmet need in populations.

Party plans are emerging that could not only lead to billions of pounds of increased investment in pharmaceutical research and development, but which also emphasise the need for effective harnessing of data in pioneering research.

Calls are being made for the UK to lead the world in clinical trials for new life-saving technologies and medicines, and for those trials to become more diverse and accessible for patients.

Strong will to develop the UK’s life sciences ecosystem is also being demonstrated in existing strategy.

For example, with workstreams geared towards a future NHS that better anticipates and mobilises for the adoption and spread of innovation, the Innovation Ecosystem Programme, led by Cambridge University Hospitals chief executive Roland Sinker, is helping to foster partnership between the NHS and industry.

The programme should help to define and make a reality of the NHS’s role as an integral partner within an increasingly effective UK environment for life sciences.
This has the potential to enable a stronger platform for harnessing real-world data to inform and accelerate deployment of innovative med tech and medicines.

The real opportunity from this policy momentum is to ensure the right innovations that address real unmet need, reach the right patients, at the right time, in the right setting.

And as tools are sought to deliver on commitments, clinical risk adjustment could have a very sizeable role to play in transforming policy ideas into practical advancement for life sciences.

In healthcare, clinical risk adjustment means better understanding if patients are achieving outcomes expected for them. It can predict if they are at risk of suffering worse outcomes.

And it can identify what actions are needed to prevent avoidable harm and reduce unwarranted variation.

In life sciences, the methodology has enormous potential to use a similar understanding of individual clinical risk at-scale, in order to help to better define unmet need in populations, and better ensure medicines are likely to work for the patients they are intended for.

For life sciences, the application of clinical risk adjustment could help to overcome a number of key pervasive challenges, including:

Delivering medicines that reflect diversity in populations: Clinical risk adjustment offers the life sciences sector the opportunity to better understand the needs and risks of diverse populations, and to then create medicines that are more likely to work in the population groups they are intended for.

Tackling inequalities: The methodology could help the life sciences sector to partner with health systems to better recognise where inequity exists, and to understand what could be done about those inequalities.

And it could inform recruitment for clinical research to ensure that patients in trials represent the patients who might benefit from those medicines and med tech innovations in the future.

Making more from constrained resource: Life sciences companies could better target research to areas of unmet need, and gather evidence of a likely clear benefit to the population based on a better understanding of the patients who need them.

For health systems, clinical risk adjustment can inform which treatments they should spend their money on, based on a stronger understanding of the greatest needs in their population. And working together, both can design pathways to better ensure that patients get the treatment that is right for them.

Measure effectiveness of life sciences innovations: Importantly, clinical risk adjustment can also be used to monitor outcomes when an innovation is deployed, and to measure and demonstrate effectiveness of innovative treatments and new technologies in the real-world.

As 2024 continues, clinical risk adjustment has the potential to be a powerful trend for both the healthcare and life sciences sectors.

Historically, medicines have been developed for average patients. But we now have an opportunity to be more targeted.

Rather than focusing scarce resource solely on the incidence of ill-health or disease in a population, a data-driven approach could enable a sophisticated understanding of the pressing unmet patient needs in a population and the insight to respond to that need.

With that, comes genuine opportunities for ‘win, win, win’ scenarios – for life sciences companies, for healthcare systems and for patients.

Dr Sheuli Porkess, Chief Medical Officer at Precisia.
Go to lifescienceintegrates.com