Rethinking Healthcare Provision with AI

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As data and information proliferate at an exponential pace, many industries have turned to artificial intelligence to make sense of this information and deliver meaningful insights that can enhance performance. This is certainly the case for the healthcare sector, where the technology augurs nothing less than a paradigm shift towards ‘industrial medicine’ and a thorough optimization of care pathways towards better disease management.

 

“Whether we call it ‘personalised,’ ‘precision,’ ‘stratified,’ or ‘tailored,’ the age of evidence-based medical treatments is squarely upon us. The primary driver for this (r)evolution has been the dramatic technology-driven increase in our understanding of the underlying molecular and genetic bases for common diseases with artificial intelligence right at the centre of casting new light on these processes,” enthuses Pierre Meulien, executive director of the Innovative Medicines Initiative (IMI).

“The grand vision of the healthcare of the future is none other than to transition away from merely ‘treating’ disease more towards ‘intercepting’ and ‘pre-empting’ illness through the enhanced knowledge that algorithmic medicine and digitalization afford us,” agrees Kris Sterkens, company group chairman for EMEA at Janssen. “Machine learning and automation have become handmaidens in enabling us to decipher health data to not only devise far more targeted personalised precision therapies that align with a patient’s individual makeup, but also to nudge the needle back from treating late-stage disease to earlier phases of the illness through greater accuracy of diagnosis.”

More effective disease management, in turn, portends to take costs out of the system in an era when many health apparatuses around the globe are coming under intense financial strain. According to calculations by Accenture, AI and machine learning applications can potentially generate some $150 billion in annual savings for the US healthcare economy alone by 2026!

 

The Advent of Industrial Medicine

This brave new world of healthcare provision, however, envisages a wholesale transformation in the modus operandi of hospitals and clinics whereby healthcare delivery will be systematized to function more akin to an assembly line, and where treatment provision can be ‘down-skilled’ with AI-enabled, protocol guided nurse practitioners and physician assistants acting as primary points of care. “The implications for the medical community – healthcare clinicians, payers and practitioners – are absolutely profound and far reaching,” admits Meulien. “The train for this new approach might already have left the station but many actors within the current system still need to adapt to the new way of doing things for patients and health systems to reap the full benefits,” he warns.

Unsurprisingly many healthcare professionals have been wary of the consequences of machine learning fearing that their jobs might become obsolete as machines take over. However, tech giants have been quick off the mark to dispel such concerns. “We are always quick to mitigate fears about the future uses of machines and to stress that we are focused on augmenting human activity – not replacing it. Therefore, far from seeking to substitute physicians for machines, we are actually trying to empower medical professionals by optimizing their workflow to support enhanced decision-making,” explains IBM Watson Health’s managing director for EMEA, Mark O’Herlihy.

“Technologies like artificial intelligence actually produce a qualitative evolution because they can lighten the load of healthcare professionals who have become overburdened with data and administration, freeing them up time-wise to focus more on the patient. I would go as far as to call it ‘empathetic’ technology,” ventures Sterkens. “You bolster the human factor by automating the elements where it doesn’t make sense for the clinician to be spending his or her time. Far from substituting the healthcare professional it unleashes and unshackles him. It augments the human factor,” he insists.

 

Enlightened Pharmacovigilance

Pharmacovigilence is perhaps one of the great examples of how AI can liberate health professionals while simultaneously offering patients decidedly better outcomes. “Machine learning accuracy shall achieve levels that will allow for the processing of selected adverse event reports without human touch… Instead of focusing on the resource-intensive manual and repetitive tasks of processing adverse event reports, pharmacovigilance departments can now focus on more analytic tasks with greater potential to improve the lives of patients through improved benefit-risk assessment and risk management programs,” enthusiastically predicts Dr Michael Levy, senior vice president of global pharmacovigilance at Bayer Pharmaceuticals.

Meanwhile, other examples of the positive impact of AI from across the healthcare spectrum are increasingly coming to light. For instance, analysing a panel of the genome habitually took more than 160 hours to complete manually at Hôpitaux Universitaires de Genève, but nowadays the university hospital is leveraging Watson for Genomics to complete the function in a mere 10 minutes.

One of the most startling recent success stories can perhaps be traced to the University of North Carolina’s Lineberger Comprehensive Cancer Centre where oncologists tested Watson for Genomics on over 1,000 retrospective patient cases. In more than 300 instances, the tool identified additional potential therapeutic options. Of these, a full 96 were not previously identified as having an actionable mutation and thus the intervention of AI-technologies resulted in an important modification in these patients’ treatment plans.

 

Towards Better Patient Equity

For some proponents of AI, industrial medicine can even be leveraged to promote better health equity. With many of the most pernicious issues in health relating to an unwarranted variation in care, including a lack of standardization in determining prognosis and setting treatment plans, the belief is that by harnessing data-driven insights in a systematic manner, great strides can be taken in overcoming this longstanding challenge. “If sensibly applied, AI can play a role along the entire spectrum of care – from prevention to treatment to monitoring – generating a consistent level of care that is targeted, fit-for-purpose in its alignment to the individual patients needs and thus introducing a much more consumer-friendly language into patient interactions,” boldly assures Richard Saynor, CEO of Sandoz.

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