Data and algorithms for sustainable healthcare innovation

From research to drug distribution, AI is unlocking new solutions for public health. But the governance of innovation poses ethical and regulatory challenges.

Italy is the second-oldest country in the world after Japan, with one of the lowest birth rates (1.18 children per woman) and one of the highest life expectancies (83.4 years), along with a steadily shrinking working-age population. The aging of the population, combined with the effectiveness of the National Health Service (SSN), which contributes to a long and healthy life, makes it difficult, if not impossible, to significantly increase healthcare spending.

The economic sustainability of the SSN requires moving beyond traditional cost-containment mechanisms, which often focus on short-term financial restraint, in favor of solid, forward-looking, value-oriented planning. This approach calls for a strategic vision to optimize resource allocation, integrating efficiency and quality in therapies and treatments with the ability to meet the growing needs of the population.

Rethinking healthcare and pharmaceutical funding and delivery models means investing in innovative solutions that prioritize patient needs and ensure equitable access.

In this context, and within the pharmaceutical sector, the push for innovation requires specific considerations and conditions.

The introduction of artificial intelligence (AI) across the drug lifecycle can offer extraordinary opportunities in research, development, manufacturing, distribution, access, and therapy management.

Artificial intelligence and big data are revolutionizing the research and development of new drugs, enabling the analysis of massive volumes of clinical data. This approach can facilitate or accelerate the identification of new patterns (such as in personalized care or precision medicine) and the discovery of new molecules.

Thanks to predictive analytics, drug production and distribution can be optimized by forecasting supply needs in different geographic areas, thereby improving the efficiency of the production and distribution chain.

With AI, data collection, analysis, and use can happen in real time, quickly providing insights on a drug’s potential clinical, organizational, and economic impact, and streamlining its assessment and access for patients.

In addition, AI and big data tools enable the analysis of large volumes of real-world evidence, offering valuable data on the effectiveness and safety of treatments in real-life settings. This analysis enhances the ability to identify therapeutic response patterns and evaluate the cost-benefit ratio of new therapies, offering essential information not only for the industry but also for regulatory agencies and policymakers tasked with assessing their value for the healthcare system.

The integration of telemedicine and digital platforms can transform the relationship between patients, doctors, and pharmaceutical companies, making therapy access and management more effective and efficient.

Despite its enormous potential, the adoption of AI in the pharmaceutical sector also presents significant challenges, including data security, patient privacy protection, and system interoperability. Moreover, integrating AI into business processes requires a cultural shift supported by internal skills development. Lastly, the decisions and regulations that govern the drug lifecycle take time.

A mismatch between innovation and regulation can pose risks in terms of safety and efficacy. If AI is adopted without proper regulation, there may be a risk of suboptimal or harmful applications. On the other hand, the benefits of AI could be lost, limiting the technology’s potential. It’s a difficult dilemma, with no clear or definitive solution, but one that undoubtedly calls for a reflection on the need for flexible, adaptive regulatory approaches and a constructive, forward-looking dialogue among all stakeholders (patients, industry, regulatory agencies, and more).

Originally published in Fortune Italia