Jessica Rengstorf, MPH, Director of Healthcare Strategy & Adrian Sutherland, Senior Architect, Healthcare at Endava
The shift towards connected care is gaining momentum, with patients showing an increasing desire for treatment options made possible in the comfort of their own homes, from remote consultations to telemedicine and real-time healthcare apps. In turn, healthcare systems are gradually growing more joined up in recognition of the fact that a patient-first approach is vital to developing the industry for the future – where patients are not only directly linked to their healthcare provider and wider care team, but care is both more accessible and personalised to their specific needs.
So far, some healthcare systems have enabled virtual care services such as remote monitoring at home and implemented tools such as chatbots to rapidly answer common patient questions around the clock amid clinician shortages. But while other industries such as manufacturing and retail have been quick to adopt advancing technologies such as artificial intelligence (AI) and deep learning, healthcare providers have largely been slower to accelerate their digital initiatives. A survey from Deloitte uncovered that just 4% of physicians were able to benefit from wearable health device data being integrated into medical records – a figure that declined from the previous 2020 survey.
Despite this, research from Statista has also uncovered that 25% of European citizens would be completely comfortable with AI-enabled decisions within patient monitoring applications. With more patients gaining confidence in technology to aid care, providers who can build AI into the fabric of their services will have the opportunity to not only streamline workflows, but foster greater trust with patients, improve engagement and realise more meaningful outcomes. What’s more, the benefits of AI can extend to historically non-patient centric areas like pharmaceutical trials, where it can be leveraged to support participant engagement by removing geographic barriers, as well as to reuse data based on standards and metadata to accelerate trials.
As we hit an inflection point in AI and deep learning, the industry is beginning to unlock the potential for tailored health predictions and treatment recommendations. Exascale’s compute power, for instance, is showing promise with its capacity to create high fidelity digital twins of humans. This means that clinicians will have the power to test the effects of different medications on digital simulations of individual patients’ complex systems, rather than relying solely on past experience of patients with non-identical circumstances. Greater connectivity in this instance can be lifesaving, ensuring that patients are prescribed the correct medicines and that their impact is monitored effectively throughout treatment.
This is not to say that implementing sophisticated healthcare tools to work intuitively will be a straightforward or cut-and-paste process, however. Navigating the patient ecosystem means integrating health information and real-time data from disparate sources and understanding individual patient needs, medical environments and users as they evolve. But if successful, technologies such as AI can facilitate better coordination of care along the patient journey – from aiding the triage process to providing the most effective treatment route and supporting safe and fast patient discharge from hospital.
As healthcare providers prepare to mature their digital systems, expand use cases and enhance care models, AI will also be critical to generating synthetic patient data to prevent compromising sensitive information – a process that’s expected to account for 60% of all data used in AI by 2024. While more experimentation and work remains for providers to meet demands for connectivity, prioritising patients at the heart of these investments will be key to delivering healthier rewards long term.
