By 2030, AI will access multiple sources of data to reveal patterns in disease and aid treatment and care.

Healthcare systems will be able to predict an individual's risk of certain diseases and suggest preventative measures.

AI will help reduce waiting times for patients and improve efficiency in hospitals and health systems.

It’s a typically cold day in January 2030 and the peak of flu season. At this time of year a decade ago, clinics and doctor’s offices would be overflowing with sick people waiting to be seen; today, clinicians and patients move easily through the system.

So what’s changed? Connected care has become a reality, driven by years of immense pressure on global healthcare systems without enough skilled medical professionals to care for their rapidly growing and ageing populations and breakthroughs in powerful technology enablers, such as data science and artificial intelligence (AI).

AI can now reveal patterns across huge amounts of data that are too subtle or complex for people to detect. It does so by aggregating information from multiple sources that in 2020 remained trapped in silos, including connected home devices, medical records and, increasingly, non-medical data.

The first big consequence of this in 2030 is that health systems are able to deliver truly proactive, predictive healthcare.

1. AI-powered predictive care

AI and predictive analytics help us to understand more about the different factors in our lives that influence our health, not just when we might get the flu or what medical conditions we’ve inherited, but things relating to where we are born, what we eat, where we work, what our local air pollution levels are or whether we have access to safe housing and a stable income. These are some of the factors that the World Health Organization calls “the social determinants of health” (SDOH).

In 2030, this means that healthcare systems can anticipate when a person is at risk of developing a chronic disease, for example, and suggest preventative measures before they get worse. This development has been so successful that rates of diabetes, congestive heart failure and COPD (chronic obstructive heart disease), which are all strongly influenced by SDOH, are finally on the decline.

Understanding the size of the global healthcare challenge Image: Philips

2. Networked hospitals, connected care

Alongside predictive care comes another breakthrough related to where that care takes place. In 2030, a hospital is no longer one big building that covers a broad range of diseases; instead, it focuses care on the acutely ill and highly complex procedures, while less urgent cases are monitored and treated via smaller hubs and spokes, such as retail clinics, same-day surgery centres, specialist treatment clinics and even people’s homes.

These locations are connected to a single digital infrastructure. Centralized command centres analyse clinical and location data to monitor supply and demand across the network in real time. As well as using AI to spot patients at risk of deterioration, this network can also remove bottlenecks in the system and ensure that patients and healthcare professionals are directed to where they can best be cared for or where they are most needed.

The glue that binds this network together is no longer location. Instead, it is the experiences of the people it serves – which brings us to the third big difference in 2030.

3. Better patient and staff experiences

Why are experiences so important? For patients, research has long shown that they can have a direct effect on whether they get better or not. For clinicians, better work experiences became increasingly urgent – a decade ago they started suffering from huge rates of burnout, mainly caused by the stress of trying to help too many patients with too few resources.

In 2030, AI-powered predictive healthcare networks are helping to reduce wait times, improve staff workflows and take on the ever-growing administrative burden. The more that AI is used in clinical practice, the more clinicians are growing to trust it to augment their skills in areas such as surgery and diagnosis.

By learning from every patient, every diagnosis and every procedure, AI creates experiences that adapt to the professional and the patient. This not only improves health outcomes, but also reduces clinician shortages and burnout, while enabling the system to be financially sustainable.

This networked system spans communities and is powered by connected care, uniting people, places, hardware, software and services – creating true networks of care that improve lifelong health and well-being.

Back to reality

Back in 2020, we’re still a long way from achieving this vision. Unrelentingly complex technology, IT and data systems still impede staff workflows and threaten the continuity of care in the clinical areas in which they are used to help diagnose, treat, monitor and, hopefully, prevent and cure diseases.

Nevertheless, I see clear signs that all three of these ideas can one day become reality. Intelligent systems are already capable of performing expert tasks and augmenting human capabilities. Examples include AI that can detect cancerous lesions on an image, analyse and quantify physician notes or optimize patient flow in emergency care. Inside hospitals, the application of AI-enabled predictive analytics is already helping to save lives in intensive care units. Outside of hospitals, it is helping to identify certain at-risk groups so that pre-emptive primary or community care can reduce the need for hospital admissions.

But it’s a long and complex journey which no single company or organization can take alone. I believe that governments, health systems and private companies must continue working together in order to ensure that AI systems are fully interoperable and transparent and prevent bias and inequality. As healthcare continues to globalize, the need for international standards that protect the way in which AI uses personal data will become an urgent priority.