The two big challenges for healthcare services are a growing global population and an increase to life span. The World Health Organization predicts 40 million new jobs in the health sector by 2030, but this is still an estimated 9.9 million short of the staffing levels needed.
AI and machine learning in healthcare can reduce the demands on health and care professionals. This would allow them to see more people and provide a higher standard of care, but it goes further.
Thousands of medical staff leave the health industry due to stress and burnout. Introducing AI to complement their work will better alleviate the burden on staff, keeping their experience and expertise in the sector.
How is AI used in healthcare
AI began in the 1980s as something called ‘rules-based learning’ but this is how spreadsheets function and a little too basic for the modern day. So, the question was asked: how can AI be used in healthcare?
40 years on we have improved, moving on to machine learning; computer algorithms that can read patient data and medical research. We are in the very early stages of AI in healthcare, and there are two main focus points at present: image analysis and clinical documents (patient records and medical info primarily).
With AI, computer software can check images to spot patterns and connections. Many would be spotted by a GP or consultant, but AI can do this more efficiently, leaving medical professionals free to care for their patients.
AI can spot patterns over extended periods of time and support earlier interventions by giving health professionals the information they need to make the right decisions regarding each and every patient, as well as cohorts of people under their care. It’s a digital helping hand at a time when our dedicated medical professionals have shown they are fighting a losing battle against the sheer numbers of people needing their help.
Data analysis is also being pursued, with an emphasis on electronic patient records to make it easier for doctors to take notes, to read notes, and for the important information to leap out and present itself to medical professionals.
The more data that can be taken from clinical documents, the more accurate the AI can be in assessing patients, but a big hope is to reduce the burden on medical and admin staff so they can better use their time.
Other approaches on the horizon include:
- medicine and drug development
- illness prediction (including individual genetics)
- emergency call analysis (analysing voice and background noise).
AI in Healthcare examples
AI programs are helping clinicians to be more efficient with their work and more effective in the healthcare they provide, and there are several examples already in use.
Chatbots are a tool to handle basic conversations, providing immediate responses to inquiries without needing a person on the other end giving answers. This can allow 24/7 coverage and is cheaper, saving cash for other care. Virtual nursing is similar, logging patient concerns and offering answers before escalating to medical professionals.
Perhaps one of the most exciting examples is AI in surgery; assisting surgeons and clinical staff. Robots already perform some precision surgery at present but in limited cases. In future, more cases with better robotics and better AI will allow surgeons to operate robotically from anywhere in the world.
The Access Group provides two software options:
Patient Insight gives healthcare professionals an easy-to-read timeline of an individual’s history showing risks, allergies and medications, meaning it’s easy to see a quick overview without needing to read every progress note. This use of artificial intelligence works to the advantage of healthcare professionals to save them time and mean they can provide better, more informed care, quicker.
Access Assure, is an assisted living tool designed to maintain independence whilst providing peace of mind to the client and their family – as well as keeping care staff updated about the client’s wellbeing. With the Access Assure Home Hub, sensors track the client’s activity and learns the person’s behaviours. Trends can then be read from repeat behaviours to help predict any health concerns, such as a decline in mobility.
How is AI being used in the NHS?
The role of AI in healthcare is simple; to make life easier for healthcare professionals and to improve the quality – and availability – of care for the public. The NHS is beginning this transition.
In 2019, Simon Stevens asked tech companies to help develop AI in the NHS. He wanted it to become a “world leader” in artificial intelligence and machine learning. This is the function of the NHS Artificial Intelligence Laboratory, and with the Artificial Intelligence in Health and Care Award, funding is being given to very promising AI projects. AI projects.
Examples include Barts Health NHS Trust, where they are using AI to fully automate cardiac MRI scans and the University of Huddersfield, whose AI is assessing data to speed up ADHD diagnosis. The University Hospitals Bristol and Weston NHS Foundation Trust are also using SMARTT (Safe, Machine Assisted, Real Time Transfer). This is an AI tool making decisions on when is safest to move people from intensive care, allowing for better patient care and bed management.
The NHS has already established an AI Virtual Hub, an Ethics Initiative, and a Dictionary of Terms, as well as their AI Lab Skunkworks for training workshops and short-term projects. A national Covid-19 imaging database has been established too, whilst individual trusts work on solutions such as bed management and patient records to improve the administration side of operations.
Ethical issues of AI in healthcare
There are, of course, ethical issues about AI in healthcare. Artificial intelligence algorithms are as biased as their maker and rely on as much data as possible, but also the correct evidence-based factors when creating the AI to process medical data. This is why NHSX established the Ethics Initiative; CEOs, regulators, and tech firm bosses all in a room together with the likes of the Information Commissioner and the Centre for Data Ethics. All experts, all discussing the concerns and threats to patient care, privacy, and safety.
AI challenges in healthcare will never go away, which is why institutions such as the NHS must ensure that people always come first, and artificial intelligence is used to support healthcare professionals, rather than replace them.
The impact of AI in healthcare
Healthcare is about the human touch. When talking about the impact of AI in healthcare, some may presume a negative, but this is technology designed to complement and enhance our stretched medical services to continue to deliver the high-quality care that they’ve been doing giving for over 70 years.
Artificial intelligence uses machine learning for precision medical care, but it needs vast swathes of data to be effective and lacks human empathy and creativity. This is where collaboration comes into play. AI needs supervised learning, and healthcare professionals need quicker access to resources.
Getting this collaboration right will take time. Data input to teach the algorithms takes more time, and the more you build up these algorithms the harder it can be to understand. Plus, with things going more and more digital, there’s an increased risk of cyber security threats to medical research and more importantly personal data.
This brings us to the advantages, which many believe outweigh the risks…
Advantages of AI in healthcare
AI in healthcare can help everyone. The benefits of AI in healthcare are potentially huge; automating repetitive tasks, analysing data 24/7 rather than relying on human attendance, preventative and proactive care, tailored treatment plans per person to reduce the risk of hospital admission, reduced human error, integration with wearable tech… the list goes on.
AI can revolutionise healthcare in the years and decades ahead. For doctors and hospital staff it means less time spent on speculation, less time spent digging for information, and more time spent providing person-to-person care with a higher accuracy thanks to being better informed. Better outcomes and person-centred care brought about by having the right information at the point of care.
AI could also be a big cost saver. Insider Intelligence reports that 30% of healthcare costs are related to admin work. By streamlining manual processes and making them more efficient, time and resources can be better invested and re-utilised for the benefit of all involved. The same cost-saving principles can be applied to drug discovery, which AI can streamline, saving both money and time, looking to bring new medicines to market quicker, which could improve the experience, treatment and even prognosis of people receiving care.
“AI offers a great way of improving the quality of screening by having the AI operate as a second observer.” - Imperial College London
And we all know that it can always be worth getting a second opinion.
