Predictive healthcare: the future (already here) for those with severe disabilities

Imagine waking up in the morning and, even before you get up, knowing how your child slept. Not because he told you, not because you spent the night checking the parameters on the monitor. But because a system silently tracked everything—saturation, respiratory rate, sleep quality—and alerted you only when there was something that needed attention. The rest of the night: silence. Rest.

Imagine arriving at your pulmonary appointment and finding the specialist already having your child's medical history from the last ten years in front of him, organized by parameters and timelines, with significant changes highlighted. Not because you brought a file, not because you spent the night before reconstructing the sequence of reports. Because the system did it for you, automatically.

Imagine a biometric anomaly being detected a week before it becomes a crisis. The system automatically schedules the follow-up appointment, sending the doctor a briefing with the relevant data. You don't have to chase anything.

I often imagine this future. Because I deal with its opposite every day.

My son is 14 years old, quadriplegic, and ventilated. His medical history is impressive: medical reports, ventilation parameters, neuromotor assessments, discharge letters, and tests of every kind accumulated over a decade. For years, I managed all of this with a briefcase, then a trolley, then a digital strategy I built piece by piece using tools designed for other purposes. I learned to digitize and organize documentation, building a homemade system to fill the gaps in the healthcare system.

And I still do it, every day. Because it's 2026 and the contrast is still stark. The artificial intelligence market is growing 58% in a year, health and technology startups are raising billions in investments worldwide, and yet the specialist I see every three months still asks for my medical history over and over again because he has no access to any data. The departments aren't communicating with each other. The system has no memory.

But change is underway. It's not uniform, it's not yet for everyone, but the pieces are there. And what I've learned, working every day at the intersection of severe disability and technology, is that the future of healthcare won't arrive all at once. It's arriving now, technology by technology, context by context.

The structural problem with the current healthcare system is that it was designed to respond to illness, not prevent it. You get sick, you go to the doctor, you get treatment. For those managing a chronic condition or a serious disability, this model means living in perpetual recklessness. The illness leads, the caregiver follows. According to 2024 data from the ASIM Fund, 80% of Italians have forgone a public healthcare benefit at least once: a figure that has grown by 15 percentage points in just one year . Nearly one in four Italians pays out of pocket for care that should be guaranteed, one of the highest percentages in Europe.

The paradigm shift we're moving toward is the opposite: predictive medicine, which intercepts signals before they become problems. And to do this, the first tool needed is time: not the doctor's time, but time as a dimension of health. A person's health isn't a snapshot taken every six months during a visit: it's a film that unfolds daily, with trends and variations that emerge only by examining data over several years. Longitudinal, systematic, continuous, and automated monitoring is the condition without which true prevention cannot exist.

Continuous monitoring: wearables that anticipate crises

Wearable devices are the first concrete building block of this system. Smartwatches and biometric sensors already measure heart rate, oxygenation, sleep quality, and temperature. Devices under development for 2026-2027 (already presented at CES in Las Vegas) will integrate cuff-free blood pressure and puncture-free blood glucose monitoring. For my son, having his respiratory and oxygen saturation parameters continuously tracked, with an automatic alert in the event of an anomaly, is not technological convenience. It's the difference between intercepting a crisis in its early stages and finding yourself in the emergency room in the middle of the night. Artificial intelligence that analyzes these flows over time can identify patterns that escape the human eye: signals that individually mean nothing, but together predict a crisis by days or weeks.

Telemedicine beyond video calls

This is complemented by telemedicine that goes far beyond video calls. Devices like TytoCare already allow for at-home cardiac and pulmonary auscultations, otoscopies, oxygen saturation measurements, and temperature readings, transmitting clinical-quality data to a remote doctor. For us, who manage every trip with specially equipped transportation and extended waiting times, this isn't a minor detail: it's the concrete possibility of having a visit that otherwise simply wouldn't happen.

And then there are the distributed diagnostic kiosks: standalone kiosks that in just a few minutes, without an appointment, allow users to perform electrocardiograms, body composition analyses, spirometry, and much more. Neko Health in Sweden and Diana Science in Italy are building full-body scanning platforms that provide a detailed picture of key parameters in a quarter of an hour. The vision, already partly a reality, is for these devices to leave hospitals and arrive in pharmacies, train stations, and shopping malls. A routine check-up that integrates into daily life rather than interrupting it.

AI as a nervous system: integrating data

All these data flows, however, only make sense if someone integrates and correlates them. And this is where artificial intelligence becomes the central nervous system of a new healthcare system. A system that continuously monitors, flags anomalies, compares new reports with previous ones, prepares briefings for the doctor before the visit, and manages appointments based on actual clinical priorities. It doesn't replace the doctor: it frees them from the administrative burden that currently takes away time from treatment. Doctors spend significant time searching for and reorganizing fragmented clinical data, time taken away from direct care. That time can be returned to patients.

For all this to work, however, a common infrastructure is needed: a digital place where data converges, accessible with the patient's consent. The Electronic Health Record (EHR) is already, in theory, this place. In practice, it's still far from being fully realized. But the direction is right: a repository that collects medical reports, biometric parameters, prescriptions, and discharge letters, which doctors and facilities can consult in real time. In this scenario, the patient ceases to be the physical bearer of his own medical history. The data precedes him. The emergency room that receives him already has the critical information. The new specialist isn't starting from scratch. For us, this isn't an improvement in convenience: it's the difference between a system that works with you and one that forces you to be the sole point of integration between all its parts. Today, that point is me. Tomorrow, it should be the infrastructure.

The digital twin: medicine for the uniqueness of the patient

The most advanced aspect of this approach is the digital twin: a digital twin of the patient, built by integrating clinical, genomic, biometric, and behavioral data into a model that evolves in real time. Doctors can simulate the effect of a drug on the digital model before prescribing it to the actual patient, test different therapeutic options without risk, and predict how a disease will evolve in that specific patient. In Italy, Humanitas is already developing publicly funded digital twins for the treatment of rare tumors, with the goal of extending the model to all complex diseases. Integrated with genomic sequencing (now accessible at increasingly affordable costs), this paves the way for therapies designed for the specific biology of that individual. Not standard protocols applied to a statistical average: medicine that recognizes that every patient is unique. A model that, taken to its logical conclusion, goes beyond personalized diagnosis to include personalized therapy and even further: the production of drugs designed specifically for the biology of that individual patient, tested first on their digital twin, and administered with the certainty of an efficacy that traditional clinical trials, built on average populations, can never guarantee in the same way.

For me, for my son, for families living with rare and complex conditions (but this also applies to everyone), this isn't a distant promise. It's the answer to the problem we know better than anyone: standard protocols often don't scale, every therapeutic decision is to some extent a guesswork, and the complexity of the individual always surpasses the case studies on which traditional medicine is based. The digital twin is, in its essence, the formal recognition of that uniqueness.

A future that already exists (but not for everyone)

What I feel when I imagine all this working together isn't abstract enthusiasm for technology. It's relief. The relief of someone who has spent years handcrafting what an integrated system should do on its own, and glimpses a world where that complexity finally has the tools to match.

This world is not a utopia. The pieces are already there. What's missing isn't technology: it's systemic integration, vision, the political will to invest structurally, and the awareness that a healthcare system that works for everyone, including the most vulnerable, isn't a cost, but an opportunity.

The future of healthcare is under construction. The question is whether we're truly willing to build it for everyone, without leaving out those who can't afford to wait.

What about you? Are you already using monitoring technologies to manage chronic conditions? Do you have experience with telemedicine or an electronic health record? Share your story in the comments or let us know: your predictive healthcare experience can help others navigate this shift.

📚 Glossary:

• Digital twin: personalized digital model of the patient

• Longitudinal monitoring: continuous data collection over time

• Asynchronous telemedicine: consultations without real-time video calls