27 - AI: The human condition

Interest in hyperautomation has been growing ever since Gartner first coined the term in 2019.

Applied to vast amounts of data, this technique – which involves the simultaneous use of digital operating systems, workflow, robotic process automation and artificial intelligence – can augment decision-making in a way never seen before.

Despite being relatively new, hyperautomation is expected to have far-reaching benefits in sectors such as banking, insurance, retail and telecoms. It promises to dramatically increase efficiency and productivity of businesses, enabling them to deliver seamless customer experiences while enhancing employee engagement and controlling costs.

While the term might not be so widely known in life sciences, hyperautomation could also radically change clinical trials, healthcare and patient outcomes for the better. In a clinical context, it is important to note that hyperautomation could mean the automated delivery of information to augment clinical decision-making – not automate the clinical decision-making.

The increasing use of wearables in clinical trials, for instance, allows researchers to collect more data from a more diverse set of participants. Safety and efficacy of drugs in patients can be monitored on a continuous basis. This removes some of the mechanical constraints imposed in clinical trials and enables a more representative set of patients to participate – not just those that can get to the study sites.

As data flows from patients’ real-life experience, clinicians will be able to see in much more detail, and much more continuously, how people are responding to the drugs. Importantly, they also gain insights into the social factors that determine the likelihood of patients sticking to their treatment plan so alerts can be sent to ensure better adherence to the protocol.

By applying artificial intelligence and machine learning – the key components of hyperautomation – data can be interpreted at speed to support real-time and effective decision-making. Greater efficiency reduces the time-to-market for new drugs, making innovative treatments available sooner.

The ability to access, interpret and share more data expands clinicians’ horizons. No matter how distinguished or dedicated medical professionals are, they’ve always been constrained by their own experience, knowledge and time. If they can aggregate data at a national or global level, however, they extend their view beyond what any one individual alone can know – paving the way for better and faster diagnoses, particularly when it comes to new or rare diseases.

A large part of medicine, and indeed our civilisation, has been based on human experience. Think of family doctors in the past who seemed to know their patients’ history when they walked through the door of the consulting room, rarely needing to look at their notes.

Now those days are largely gone. As our understanding of conditions improves, and treatments become more sophisticated, clinicians need insights that go beyond the scope of their experience. Whereas drugs were once something of a blunt instrument, they’re now highly targeted. Yet how could anyone know the distinct characteristics of every patient and how they’re likely to respond, especially in the limited time available to them?

New and ethical data models are being developed and refined at a rapid rate as the mass digitalisation of organisations continues – so it’s only a matter of time before advanced technologies like hyperautomation become more widely available to researchers and clinicians, enabling them to drive better health for more people.