By Azir Aliu
If the first pillar was the language of the system, and the second pillar was the voice of the patient, the third pillar is the intelligence of healthcare.
Every healthcare system has its visible and its invisible part. The visible part consists of hospitals, clinics, physicians, equipment, and services that citizens directly experience. Behind them, however, exists another, deeper layer composed of data, processes, trends, risks, and decisions that determine whether the system will react too late or recognize in time where intervention is needed. After writing in previous columns about the common language of the system and the patient as an active participant in their own treatment, I will now focus on the third pillar of the Digital Health Strategy 2026–2030, which leads us to the next level, where healthcare manifests itself as a system that learns from its own data, predicts risks, and manages knowledge.
The third pillar of the strategy is dedicated to health data, analytics, public health intelligence, institutional governance, resilience, and security. It is the pillar that should transform healthcare from a system that mainly reacts to consequences into a system that recognizes risks in time and acts accordingly.
In healthcare, data is never an isolated number, a quantitative expression disconnected from a particular human condition. It is a trace of real life that contains information about a diagnosis, examination, therapy, risk, screening, epidemiological signal, hospitalization, or outcome. If this data remains hermetically sealed within separate institutions, it acquires only limited value. But if it is properly organized, protected, standardized, and analyzed, it becomes public health intelligence—that is, it is transformed into knowledge that can help the state plan better, physicians treat better, and patients receive support earlier.
This is particularly important for the diseases that place the greatest burden on our society, such as cardiovascular diseases, malignant illnesses, diabetes, the consequences of smoking, as well as unhealthy nutrition. These are conditions that do not appear suddenly in life. They have trends, risk groups, regional differences, and warning signs. If the system monitors them in time and succeeds in detecting them, prevention can be directed where it is most needed, rather than where data happened to arrive by chance.
In this sense, public health intelligence is a new form of responsibility. In practical terms, this means knowing where screening coverage is weak, where chronic patients are not being monitored regularly enough, where specialist services are lacking, where there is an increased risk of infectious disease, and where resources do not correspond to actual needs. Evidence-based policies require overcoming the administrative approach and abandoning bureaucratized intuition, in other words, developing an institutional obligation for healthcare to be guided by verifiable knowledge.
Experiences from around the world show that this is neither a distant nor an impractical idea.
In the United States, the National Syndromic Surveillance Program of the CDC (Centers for Disease Control and Prevention) uses data from emergency departments and other sources for the early monitoring of health threats and unusual trends. In the United Kingdom, the UK Health Security Agency enables the monitoring of respiratory viruses, antimicrobial resistance, and other public health indicators through public dashboards. In Finland, Findata demonstrates how health and social data, under strict rules, can be used for research, planning, and innovation without sacrificing citizens’ privacy. These examples show that modern healthcare does not only involve managing institutions and budgets, but also analytical capacity, trusted infrastructure, and sound rules.
For our country, this will mean building a national health analytics function that connects data across the system and transforms it into timely indicators. The Institute of Public Health, the public health centers, the Ministry of Health, the Health Insurance Fund, hospitals, and primary care physicians should all become part of a single interconnected public health logic. The objective should be to create risk maps, coverage indicators, early-warning programs, and preparedness scenarios.
This reflects the very essence of data-driven decisions—that is, governance in which decisions are made on the basis of the real situation on the ground rather than administrative habit. The Ministry and public health institutions should be able to identify in time where screening response rates are weak, where long waiting lists are forming, where chronic patients are ending up in hospitals more frequently, and where resources fail to match the actual pressure on services. In this way, health policy becomes more precise: resources are allocated according to need, programs are adjusted according to results, and institutions are evaluated according to the impact they create for citizens.
The coronavirus pandemic showed us that public health preparedness cannot be improvised in a moment of crisis. In a crisis, time is the most valuable resource. The system must know where capacity exists, where risk is emerging, and where human resources, equipment, communication, and preventive interventions need to be directed. That is why the third pillar embeds the logic of early warning, epidemiological intelligence, and real-time analytics into regular governance, rather than treating them solely as tools for emergencies.
Data alone is not enough to create better healthcare. Institutions capable of using it are equally necessary. Therefore, this pillar must be linked to governance, human resources, and the sustainable management of the healthcare system. Digital healthcare requires new professional profiles, the development of new competencies and skills, and ultimately an entirely new culture of work: health analysts, IT specialists, cybersecurity experts, people who understand clinical processes, and people who know how data is transformed into policy.
At the same time, the European Health Data Space establishes the framework within which national healthcare systems will gradually be able to cooperate through common rules. For us, this means preparing the health information generated within the domestic system so that it becomes understandable, comparable, and usable in a European context as well—for cross-border care, research, public health analyses, and better planning. This perspective requires healthcare institutions with clearly defined responsibilities, legal certainty, technical readiness, and a strong culture of privacy protection. Our data, institutions, and procedures should gradually become part of the European digital health architecture, and in that sense, European integration in healthcare will mean introducing a stronger methodology for order, security, and quality.
The third pillar, therefore, carries a profound value dimension. It speaks of a state that seeks to know before it reacts, of a healthcare system that does not view data as an administrative obligation but as a public responsibility. Institutions will learn from their own work while protecting citizens’ trust, because they will safeguard their privacy, their rights, and their health.
If the first pillar was the language of the system, and the second pillar was the voice of the patient, the third pillar is the intelligence of healthcare. Its mission is to transform information into knowledge, knowledge into decisions, and decisions into timely and sustainable health policy.
*The author is Minister of Health of North Macedonia