Millennials have a Problem with the NHS – Can AI Solve it?

According to a survey by Prophet, the National Health Service is the “most relevant brand for UK consumers” at the moment. Edging out Apple and Spotify, the NHS’s relevancy is at #1, communicating positivity, recognition, and a general deep sense of importance.

Yet not everyone shares this sentiment wholeheartedly. Amongst the surveyed, British millennials have shown less appreciation for the NHS, compared to older generations. This is hardly surprising. For the past few years, reports have been signifying the disconnection between the NHS and the millennial demographic, both as patients and the soon-to-be biggest part of the workforce in the service.

The reasons for that are numerous, but the major ones are the lack of convenience and the inability to satisfy new demands, both in terms of service and technologies employed.

According to the 2019 Topol Review, people aged 23 to 39 share new expectations from their jobs, like flexibility and variety. As patients, they are more inclined to be dissatisfied with long waiting periods and brief appointments. More importantly, they are ready to share personal data for uninterrupted digitally-enabled services that are secure and ethical when it comes to data use.

Despite Brexit concerns looming over the economy and public sectors, the future of the NHS seems to be technology-heavy. The NHS artificial intelligence lab received large investments from the government, with intense focus on developing treatments for cancer, dementia, and heart disease.

Hospitals and researchers will work on algorithms to lower missed appointment numbers, better predict potential drug side-effects, perfect and expand medical image analysis AI, and improve overall efficiency rates.

While the concerns about data governance, cyber security, and ethical frameworks still need to be addressed, healthcare AI as a whole can fill the gap and provide the transitional link needed for millennials to better connect with the NHS on many fronts.

Here are some of the examples of big data, AI, and machine learning-based automation for streamlining healthcare services, which could provide extra convenience and potentially address issues millennials have with the NHS.

Data from personal sensors for diagnostics, remote monitoring, and automated processing. Today, medical decisions depend on laboratory test results in 70-80% of cases. Continuously updated data from self-monitoring sensors (glucose monitors, heart rate and BP meters, respiratory monitors), as well as cameras and smartphones can provide a basis for some of them, with new possibilities coming up every year.

This data—gathered, stored, and processed automatically—can not only be systematized on the go and potentially made available for medical professionals in real time, but also used for machine learning, AI-based predictions, automated diagnosis and treatment regimen corrections, significant status changes alerts, and so on. All for the sake of lessening the number of necessary physical appointments and greatly heightening the efficiency.

Combined with other technological advancements, such as medical telemetry, this can be of substantial use for both the NHS and patients, with such benefits as: 

Better patient education or monetary penalties have been proposed among the possible solutions to this problem; AI and machine learning has been employed to work out a better option. By the end of the year, University College London Hospitals is planning to release an AI system for detecting patients that are likely to skip appointments and automatically alerting medical staff, who could then send extra reminders via phone, email, or texts. Other AI projects are underway to increase efficiency and operational speed for medical facilities.

Some concerns have arisen alongside hopes, namely, about potential bias. As it often happens with risk predictions, involving personal data and machine learning algorithms can potentially lead to skewed and biased results pointing against people belonging to certain age groups, for example.

However, the researchers at the NIHR Biomedical Research Centre working on a project to prevent appointment skipping reported they don’t use gender, age, or ethnicity data for their AI, thus keeping such concerns at a minimum.

Convenience-seeking millennials expect the balance between ethics, effectiveness, security, and flexibility. If this balance is struck in all medical AI projects, the NHS AI lab and other initiatives will not only better engage millennials as patients, but as professionals, too.

The UK is considered one of the leaders in digital, AI, and robotics healthcare combined. In the next 20 years, nine out of ten jobs in the NHS will require digital skills. Millennials and Generation Z will be the ones to fill these roles.

To assure they do, employers will need to provide not only paychecks but opportunities, flexibility, efficiency, and digital augmentation. Ironically perhaps, while AI might be the future of the healthcare system, we’ll likely need more AI to pave the road to that future.