InnoHealth Academy · August 2026
By MD. Josep Maria Piqué Badia
Public health systems are stretched to the limit. Artificial intelligence could be the lever we have been waiting years for… or a threat that erodes equity, safety and ethics. The difference will not depend on the technology, but on how we decide to introduce it.
A system under strain, a reform that cannot wait
Public health systems in most Western countries are under severe strain from a combination of factors: compromised economic sustainability, exhausted professionals, a public perception that the system is deteriorating, and a disorderly introduction of AI without a defined strategy or governance, all against the backdrop of an emerging professional and labour crisis of a magnitude similar to the one we will see across most sectors. Taken together, this makes a profound and disruptive rethinking of how we approach citizens’ health more urgent than ever.
Big tech has already entered the health market
Against this backdrop, the large technology corporations are betting heavily on the health market with their AI tools. This could take us to a near-future scenario in which the agents created by these companies replace a substantial share of the prevention, diagnosis and treatment-recommendation tasks currently carried out by health professionals — without accessibility, equity, safety and fundamental ethical principles being guaranteed. Indeed, since the start of 2026 we have already seen several offerings launched in this direction by OpenAI, Anthropic and Google.
We are therefore facing a real threat. But if we react promptly, there is still time to turn it into an opportunity. A planned, regulated introduction of AI, accompanied by proper training, could be the lever we need — and have long been looking for — to finally change our health system.
A planned, regulated introduction of AI, accompanied by proper training, could be the lever we have been waiting years for to finally change our health system.
Two major areas for improvement in the repair-based system
Beyond the structured use of AI to streamline administrative processes, and the individual use physicians can make of it to strengthen their clinical decisions, today’s health system — which is predominantly repair-based — has an opportunity to resolve many of its inefficiencies through a properly regulated introduction of this technology.
Users’ perception of inefficiency is not so much concentrated in the complex, serious processes that require advanced technology and a rapid response, but rather in the routine ones, those of low complexity, or those demanding a high degree of coordination between levels of care. And it is precisely here that two major areas for improvement can be identified.
A digital front door with screening and self-triage. An AI-based consultation and screening layer would allow citizens to resolve a significant share of their questions or health needs without a professional’s intervention. And for whatever is not resolved at this first digital step, AI would provide a valuable self-triage tool to direct the citizen-patient towards the resource that can meet their need most quickly and efficiently: a pharmacy consultation, a nursing or medical consultation, a social service, or urgent care at a Primary Care Emergency Centre (CUAP). This decision algorithm would not only greatly improve user satisfaction, but would also shorten the system’s response time — and therefore waiting times — and reduce professionals’ workload, with the consequent savings in time and money.- Genuine coordination between primary and specialist care. The lack of coordination between primary and specialist care — or even between specialties — is another source of dissatisfaction: waiting times, needlessly repeated tests, conflicting clinical criteria. Establishing a territorial pool of specialists contractually linked to an Integrated Health Area, working in physical and digital proximity to a group of primary care physicians, would be the first step. This would be complemented by AI agents that manage waiting lists and unify the work of the primary care physician and the specialist in front of the patient, within a shared digital space and with an orchestration of agents supporting clinical decisions and the prioritisation of actions. A highly beneficial side effect would be greater equity in accessibility and quality of care between urban and rural areas, and the maintenance of a high level of training among those professionals — both primary care physicians and specialists — who practise in rural areas in situations of professional isolation.
From repairing to anticipating: the great leap
But there is yet another area where AI could bring great benefit to people’s health: not from the current repair-based structure, but from a new anticipatory one, designed to accompany citizens in maintaining their own health.
Today we spend more than 95% of our economic and professional resources on repairing illness, and less than 5% on preventing it. A decisive commitment to prediction and personalised prevention would have, in the long run, a far greater economic and social impact than what we currently obtain by spending enormous sums in the final stage of life for a marginal extension in time and quality.
The project: a health Living Lab in the Pyrenees
This new approach is what we intend to set in motion from InnoHealth Academy, in close collaboration with TIC Salut Social and the business cluster SolSoleil Health Pyrenees. Under the governance of the Department of Health, and framed within the CAIROS initiative recently launched by that Department as part of the new Health Map, we are preparing to deploy a proof of concept in a rural territory using Living Lab methodology. The project rests on five pillars:
How it will work: 200 people, one year, a digital twin
Over the course of one year, 200 volunteers from a rural area of the Pyrenees will be monitored through five major data sources: all data on their conditions, drawn from the shared electronic health record of the Department of Health; data from smart devices on physical activity and sleep patterns; data on nutrition and emotional wellbeing gathered through validated questionnaires; polygenic risk measured from whole genome sequencing; and biomarkers related to immune status and microbiota composition.
All the data collected will be stored on a cloud server governed by a non-profit Foundation, with internationally respected Trustees in the fields of ethics, technology and health, who will safeguard the custody and confidentiality of the data and ensure that each citizen is its sole owner and the one who grants the relevant permissions for its use. All of this will comply with current regulations and align with the rules and objectives of the European Health Data Space (EHDS).
With this data, and using advanced AI tools, a Digital Twin of each individual will be built. It will be used to run the relevant simulations in order to draw up a grid of health and emotional wellbeing recommendations, adaptable over time and to changing circumstances. These personalised recommendations will be delivered through a chatbot with which citizens can converse to qualify, explore or challenge them. The training of the AI agents and the query structure of the Digital Twin will be supported by the knowledge and best practices that a series of expert Think Tanks will gather from the scientific literature and from international initiatives in each of the health domains listed.
Behind this digital structure there will be a CRM operating in call centre mode, staffed by health professionals responsible for overseeing the digital recommendations and answering any questions the citizen feels have not been sufficiently resolved by their personal chatbot.
What we will measure and where we are heading
The project will be followed for one year, and the outcomes to be measured will be the feasibility of building a hybrid digital-professional structure of this nature, the degree of acceptance and adoption by citizens, adherence to the system and its recommendations over the full year, professionals’ performance within this new structure, and the satisfaction of both users and professionals with this new approach to health.
If the experience proves positive, the aim is to scale the model to other areas or regions and to use the accumulated knowledge and experience to design similar projects in the field of care delivery as well.
AI should not replace us: it should let us do what we have always wanted to do and the system would not allow. Anticipate, accompany, and leave no one behind.
I invite you to reflect:
→ In your own setting, which low-complexity processes could be resolved today with a well-designed digital front door?
→ Are we ready, as a system, to move from a repair-based model to an anticipatory one?
→ What do you think is the main risk if we let big tech alone lead this change?
InnoHealth Academy (IHA)
