Healthcare workers are digital native consumers. The new generation may have grown up without paper, pen and fax, but they know what good service delivery feels like. It is digitally accessible and responsive, self-service and empowering, with transparency of information and choice At the same time, our global health systems are under pressure like never before. The systems we crafted centuries ago to manage battlefield trauma and infectious diseases are not scaling to address the challenges of a population with lifestyleinduced chronic disease. Thanks to modern medical miracles, we are increasingly turning acute conditions like cancer into chronic diseases. In our developed world we simply do not have enough staff, beds and facilities to meet current demand, nor can we scale to meet future projections. Our workforces are increasingly specialised and centralised in metropolitan hubs, while patients are geographically spread.
We need a new health system paradigm underpinned by new models of care. We need a way to deliver high-quality services that are equitably accessible to patients wherever they are, with the same workforce and ideally at the same or lower cost. We need digital at the core. The good news is that we do not need to imagine the future. It is already here — deployed and delivering the outcomes and results we desperately need. The challenge is turning these pockets of innovation into the default model, as these new models of care include virtual care, self-directed care, preventative care, and precision medicine.
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At the same time, we need to make progress beyond eliminating paper and implementing systems of record in healthcare. If all we do is digitise, we may be missing the opportunity to transform care. That means using the data that we capture for insights and ensuring that the experience is modern and intuitive. One of the most promising technologies is artificial intelligence and this has delivered compelling capabilities in the field of machine vision for diagnostic imaging. Now we have trained models that are finding a ready home in radiology, pathology, dermatology, and ophthalmology. Creating an accurate diagnostic model for diagnostic imaging is a matter of clinician input, training data, data science skills, and computing resources, then fine-tuning through reinforcement feedback learning. Beyond medical imaging, however, marshalling these inputs has proven a barrier to the broader application of artificial intelligence in healthcare.
In the last year, however, generative AI has surfaced to provide an answer to these constraints. Large Language Models (LLMs) have decoded communication, i.e., our human operating system and can infer context and content to produce surprisingly insightful responses even for specialised fields like medicine. Forget the higher-order tasks that clinicians already do well, there is a role for AI to address the estimated 25 per cent of waste baked into how we deliver healthcare today.
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We can solve our workforce dilemma if we could let clinicians diagnose and treat patients, perform the top-of-license care they are uniquely trained to deliver and get satisfaction from, and leverage AI to mop up the administrative complexity, redundancy, fraud, rework, and error. That is latent productivity. Imagine an extra 25 per cent more staff; if we couple this with outreach technologies to engage patients and virtual care models to meet them where they live, we can then address access and equity It is an exciting time for digital health. Powerful technologies are available to modernise health services, leading to better outcomes for patients and experiences for clinicians. We have validated that these technologies can be delivered safely and effectively as new models of care. Now is the time to create a future health system paradigm that will help us address the challenges of today and the future.
References available on request
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This article appears in Omnia Health magazine. Read the full issue online today.
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