{"id":6854,"date":"2026-06-23T15:14:45","date_gmt":"2026-06-23T13:14:45","guid":{"rendered":"https:\/\/ai4healthcro.eu\/?post_type=ht_kb&p=6854"},"modified":"2026-07-16T14:05:52","modified_gmt":"2026-07-16T12:05:52","slug":"best-practices-putting-medical-data-at-the-heart-of-innovation","status":"publish","type":"ht_kb","link":"https:\/\/ai4healthcro.eu\/?ht_kb=best-practices-putting-medical-data-at-the-heart-of-innovation","title":{"rendered":"Best Practices: Putting Medical Data at the Heart of Innovation"},"content":{"rendered":"\t\t
\n\t\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t

AI in healthcare depends on access to relevant data. Clinical records, diagnostic images and treatment histories can support the development of useful models, yet they also contain some of the most sensitive information an organisation can hold.<\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t

Access to relevant medical data is one of the main barriers facing healthcare innovators. AI developers need sufficiently large, well-prepared datasets to test whether their models work. Start-ups and SMEs often lack the institutional access, legal knowledge and infrastructure required to obtain them. Hospitals and public authorities, meanwhile, must protect patient privacy, comply with regulation and prevent security risks or inappropriate secondary use.<\/p>\n

AI4Health.Cro addressed these needs through a structured model that connected safe data access with clearly defined healthcare challenges. The project treated medical data as a resource for responsible innovation, governed through institutional procedures, anonymisation, secure processing and expert oversight.<\/p>\n

The challenge<\/h2>\n

Technical expertise alone is rarely enough to bring an AI solution into healthcare. Innovators must understand the clinical problem, identify the right data, establish legal access, prepare the dataset and demonstrate that results can be interpreted in a medical context.<\/p>\n

For clinicians and healthcare institutions, the questions are equally demanding. They need to know who can access the data, for what purpose, under which conditions and with what safeguards. They also need confidence that proposed solutions respond to genuine clinical or public-health needs.<\/p>\n

AI4Health.Cro created a pathway that addressed these requirements together.<\/p>\n

The best-practice approach<\/h2>\n

At the beginning of the project, the consortium established a dedicated data group involving 10 of its 16 partners. It brought together expertise in data acquisition, data management, anonymisation, processing, modelling, healthcare practice and regulation.<\/p>\n

The group developed a regulatory sandbox model referred to as a Secure Processing Environment. Its purpose was to enable real-world medical data to be extracted, anonymised, prepared and used under controlled conditions.<\/p>\n

The work applied procedures developed within the European Health Data Space framework, with key involvement from the Croatian Institute of Public Health, the Croatian Health Insurance Fund and the Ministry of Health.<\/p>\n

The process followed a series of practical steps:<\/p>\n