The fact is that generative AI happens to have the capacity where it can actually go on to transform the way patients have an interaction with the NHS when consuming healthcare.
If in case the challenges that are faced while adopting AI can be taken care of, this can help loads in terms of population health and also the productivity of the health system. This will lead to massive relief from the pressures of rising healthcare costs and the waiting list at hospitals.
So as to make sure that these perks are achieved at the NHS, which is already regarded as severely commercially constrained, there will surely be a need for a significant amount of funding in the digital health infra.
The relevance of artificial intelligence in today’s world of healthcare cannot be overlooked, as it goes a long way in customizing medical treatments, elevates research and development when it comes to new drugs, and at the same time also enables the administrative burden, which at present is undermining efficiency as well as productivity as far as healthcare providers are concerned.
Drug discovery along with AI
Another area where AI happens to be already making a massive impact happens to be the gamut of drug discovery as well as the development process. AI happens to be speeding many steps that are needed to create new drugs in terms of magnitude and hence consequently reducing the R&D costs when it comes to bringing the new drugs to the market. It also happens to be greatly making the post market surveillance process more seamless by way of enabling efficient collection as well as analysis of real-world data so as to pinpoint on the adverse drug reactions along with other safety issues.
AI and the productivity it goes on to offer
Giving the promise of better access as far as healthcare is concerned, rapid diagnosis of the condition as well as the development of new drugs to treat a broader range of conditions come with the concern that AI will go on to generate much more demand in a healthcare system that’s already kind of overstretched. It indeed comes with hope that over here too, AI will come to the rescue by way of reducing the majority of administrative burden and associated costs around healthcare, thereby freeing up the time of the clinicians when it comes to face-to-face interactions and hence, in a way, tackling a major cause of burnout in the NHS.
In addition to this, AI also promises to make sure to speed up the task shifting initiatives wherein it can go ahead and act as co-pilot, thereby helping patients manage their own health and the non-clinical staff take care of more specialized tasks that they would be comfortable managing on their own.
Customised Medicine
One of the most exciting impacts of AI happens to be the potential it has for changing the way one goes on to interact with the health system. AI will therefore be growingly able to interact with the range of wearables and applications, mix the data coming out of them with the existing health records, and also cross reference with the upgraded knowledge base, right from preventive healthcare as well as clinical medicine, to come up with a set of objectives for health.
It will therefore be able to blend them with what it has gone on to learn about personalities and also the consumption habits so as to translate the aims into an easily digestible set of recommendations with a medium that gets individually customized to most likely work.
It is well to be noted that the prior adoption of this kind of approach is already taken care of in the social care context within the NHS, wherein people are getting discharged from the hospital setup and being remotely tracked by way of using AI algorithms as well as wearable devices in their homes at the virtual wards that are created.
There may as well be some taking the form of gamification by way of making use of augmented reality, and for others, it may be animations that would go on to appeal to scientific curiosity or even interactive conversations due to a simulated version of a favorite celebrity. This kind of approach may as well help in engaging with preventive behaviors while at the same time picking up on developing serious health issues, guiding towards more appropriate treatments, and also preparing the treatment provider with the information they need so as to understand and address the condition.
So as to unlock the paradigm shifting health advantages, one has to overcome a series of issues before being comfortable letting the machines take care of the health. Most of these challenges are well discussed and happen to have in them the problem of model hallucination, wherein the AI goes on to come up with answers in case being questioned outside of the knowledge realm, the need for such probabilistic algorithms to deliver reproducible outcomes, and also the legal issues as to who happens to be accountable if in case something goes off the grid.
Taking care of biases that are encoded in the model training data as well as algorithms and, at the same time, staying at the top of the drift are most often underscored as critical issues that are faced by AI models. Moreover, one has to also think quite carefully as well as creatively in terms of the digital divide, how to make sure to prevent the new technology from further pushing inequalities in health, and how to elevate and at the same time also retain the humanity element in the healthcare systems. These happen to be the barriers that the NHS happens to be aware of and is kind of actively grappling with.
But the investment is indeed needed to upgrade the poor digital infra state that’s within the ambit of the NHS. The fact is that the NHS is now beginning to acknowledge the scale of the challenge that is posed and has gone on to make promising progress across all these fronts, and has, as a matter of fact, even announced another £3.4 billion budget for NHS technology as well as transformation. As the AI begins to get rolled out within the NHS, healthcare payment as well as reimbursement mechanisms will be required to be carefully thought through so as to make sure that the right incentives happen to be in place in order to encourage the adoption of production-enhancing opportunities as well as the cost savings that the AI goes on to offer.
It is well to be noted that the NHS has again gone on to be the pioneer on this front by introducing set of incentives in order to reward the usage of AI.
