Connectivity is the expectation in healthcare today with many patients showing an increasing appetite for non-traditional treatment options and care models. In fact, research by Statista reveals that 25% of European citizens would fully trust artificial intelligence (AI) enabled decisions for applications in patient monitoring. Whether it’s the use of tools ranging from deep learning to wearable devices, real-time apps and telemedicine, healthcare systems are slowly but surely recognising the power of connected care in supporting patient outcomes.
Perhaps the most obvious benefits of AI in healthcare are its ability to reduce errors and streamline workloads for time-strapped clinicians, with potentially lifesaving results. But while these benefits in themselves will have a significant impact on the sector, they are not what makes AI and deep learning so integral to its development. As the cardiologist and scientist Eric Topol aptly points out, the greatest opportunity offered by AI is in restoring ‘the precious and time-honored connection and trust’. Patient centricity is at the heart of making healthcare technology applications meaningful, and so making a lasting impact means designing solutions with people at their heart and harmonising tools with human capabilities and medical environments.
The rewards if providers get it right will be huge. The potential human impact of deep learning on healthcare is widespread, driving outcomes such as better educated and engaged patients, stronger coordination of care along the patient journey, and enabling effective remote care to discharge patients from hospital faster and more safely. But tapping into its potential means being able to navigate the complex ecosystem surrounding the patient, the unique environments in which they exist and any processes or cultural factors that could be barriers to adoption.
In the case of pharmaceutical trials, organisations could utilise deep learning to better engage subjects by both streamlining the process with automated image processing and taking measures to prevent trials from becoming invalidated. For example, participant retention can be improved by leveraging AI algorithms to understand individual patient behaviours or needs and perform remote monitoring from any location. Life sciences organisations can also use AI-powered wearable devices to diversify participants and remove travel or geographic barriers, as well as intelligently reuse data based on standards and metadata to cut down the need to start trials from scratch.
With the integration of health information from disparate sources, together with AI and an in-depth understanding of patient needs, health systems can provide personalised care with optimal outcomes. Exascale computing presents a huge opportunity in this space thanks to its exponential increases in memory, storage and compute power that make high fidelity digital twins of a human possible. In creating digital simulations of patients’ complex systems, organisations can then monitor health, simulate the effects of drugs, test their efficacy, and provide treatment recommendations beyond a one-size-fits-all approach.
This is especially important when the European Commission has recorded 200,000 deaths each year in Europe from drugs prescribed, which in part has been attributed to generic therapies that were not specifically designed for the patient. Without rapidly developing tools such as Exascale, clinicians often have no option but to make treatment decisions based on previous experience of patients in similar, but non-identical circumstances. Advances in deep learning empower them to test various ‘what if’ treatment scenarios and accurately predict how a patient will respond – orchestrating the answer in a deliberate and analytical manner to minimise risks and improve patient outcomes. Healthcare providers can utilise this intelligence to keep measuring, tracking and evaluating patient needs using high quality data for truly patent-centric care.
While there may be significant hurdles to overcome before deep learning is adopted to this extent – including educating clinicians, making ethical considerations and gaining widespread acceptance around its safety credentials – advances in AI and patient connectivity are paving the way for patient-first experiences supported by actionable data insights. Leveraging AI to generate synthetic patient data will be a core strategy for ensuring sensitive patient data isn’t compromised while also ramping up adoption and enabling a deeper understanding of how to optimise workflows. According to a Gartner study, synthetic data is expected to account for 60% of all data used in the development of AI by 2024, supporting experimentation with different medical use cases and scalability of the technology.
As the sector progresses deep learning deployment, whether that’s through developing mobile solutions for disease management or building apps that offer real-time data to users on information such as air pollution and allergens, it will be able to accelerate the interoperability of digital systems. Building this foundation for user-centric care will be increasingly key – especially as consumers gain more access and control over their day-to-day health. As such, the healthcare industry and its subsections – even those focused on R&D – should see connectivity as an investment in the patient to realise tangible results for managing, selecting, prescribing and administering medicine.
Article by Jessica Rengstorf, MPH, Director of Healthcare Strategy & Adrian Sutherland, Senior Architect, Healthcare at Endava
