Opportunities and Barriers for AI Adoption in the NHS
Analysts and people alike are increasingly aware that we are on the brink of the 4th IndustrialRevolution (Barr 2018). Healthcare is not an exception, with AI and Machine learning expected to play a prominent role in the coming years. In this article, I will consider some of the main potential opportunities and barriers to AI adoption in the NHS. Opportunities
1) NHS and Government openness to new technologies
Although Government departments are typically risk-averse (Mcfaden 2016), there has been an acknowledgement of the need to embrace technologies and the benefits they can bring. Simon Stevens, the chief executive of the NHS, has called on tech firms to lead the charge for making UK Healthcare a World Leader in the use of AI and machine learning in healthcare provision (NHS News 2019). In addition, the NHS Long Term Planhas advocated the role technology can play in helping them achieve their targets of reducing doctor workload, boosting efficiency and improving care (NHS 2019, p.6).Achieving these goals is becoming more of a necessity due to growing budgetary constraints and population size. Alongside the willingness to work with Tech companies to improve patient outcomes, the UK Government is investing heavily in improving the infrastructure necessary for such collaborations. In 2019 the Government pledged £250m to encourage the NHS to adopt AI, and in 2018 opened up 5 new Medical Centres across the country to research ways to use AI to diagnose diseases (Telegraph 2018; BBC 2019).
2) Availability of Data
Even the least tech-savvy individual understands that technology is reliant on data to function effectively. Health tech functions in very much the same way, with massive amounts of data being required for machine-learning algorithms to learn medical practice. Luckily for tech firms, the NHS holds massives amount of data on its patients,which in turn can be fed to algorithms to improve their functionality. Therefore, the opportunity exists for collaborations between the NHS and Tech firms to use this data to maximise the benefits of AI and machine learning technologies.
1) Data Security
While I’ve mentioned the potential benefits of using NHS patient data for AI, a number of risks surrounding the use of personal data also exist. Many will be aware of the suspicion surrounding Tech Companies use of personal data. Consequently, there are concerns about the NHS sharing data with private firms and the transparency of the process of data sharing (Steveton et al. 2019). For instance, the collaboration between the Royal Free Hospital and Google’s division Deep Mind was criticised for their handling of data of 1.4 million patients (PWC 2017). Therefore, it is vital that suitable Data Protection procedures are developed to deal with these collaborations.
2) Skill Gap
Another barrier to consider is that the NHS workforce simply isn’t ready to embrace these technologies. Although tech-savvy software developers may preach the benefits of health tech, in many cases the NHS neither has the infrastructure or the knowledge amongst the workforce to utilise some of the technologies. 69% of NHS staff have yet to undertake any form of training relating to how to use new technologies in their daily jobs(Hughes 2019). This epitomises the current skills gap in the NHS. Without training, AI adoption in the NHS is likely to be slowed down.
It is clear that AI and Machine learning are going to play a large role in healthcare for years to come. AI and Machine Learning technologies can improve efficiency, patient outcomes and quality of care when adopted right. However, in these early stages, there are still a number of barriers to overcome!