A Croatian team SAMO4DM2 is developing an AI tool designed to help family doctors identify patients with type 2 diabetes who may be at risk of not following their treatment — and decide what to do next.
For a family doctor, the problem is rarely a lack of information. The harder task is deciding which information needs attention now.
A medical practice may care for many people living with type 2 diabetes, each with a different treatment history and pattern of contact with the healthcare system. Reviewing every record for early signs of poor treatment adherence is difficult to reconcile with the pace of routine primary care.
Diabetes Navigator, developed by the Croatian team SAMO4DM2 during the AI4Health.Cro Innovation Challenge, is designed to narrow that field of attention.
The proposed AI tool would review existing patient data once a week and identify the 5% to 10% of patients with type 2 diabetes considered most at risk of not following their treatment.
For each patient, the tool would provide three reasons behind its assessment, expressed in clear clinical language. It would then suggest a practical next step — sending an SMS, making a telephone call or arranging an appointment.
The system is also designed to follow up on the intervention and allow its details, together with the history of previous actions, to be transferred into the patient’s medical record.
The aim is to fit the tool into the existing workflow of a family medicine practice, using infrastructure and data that are already available. Instead of creating another continuous stream of information for doctors to review, the system would provide a limited weekly overview focused on patients who may need attention.
The idea came from Ivica Škvorc, the team leader and a family physician with more than 20 years of experience. His motivation was rooted in everyday clinical work and the need for a tool that could help doctors anticipate poor treatment adherence among people receiving care for type 2 diabetes.
His focus is on developing digital health solutions that reduce the burden on healthcare professionals and allow them to concentrate on targeted work with patients.
The rest of the team brought expertise in business organisation, software quality, systems architecture and technology delivery.
Lana Škvorc has more than 10 years of experience in business organisation and EU projects, with a background in information technology and business systems management. Her work has included controlling, marketing, entrepreneurship, professional education and coaching.
Siniša Sambol is a software quality and business analysis specialist with more than a decade of experience in the IT industry. His expertise includes quality-assurance automation, business analysis, technical transformation and web development.
Željko Sučić has more than 20 years of experience in software engineering, systems architecture, enterprise integration, business analysis and development team management. His professional experience also includes the healthcare sector.
The division of roles was central to the development process. The physician defined the clinical parameters, while the technology specialists translated them into the structure of a digital tool.
According to Lana Škvorc, the project would have been considerably narrower if it had been developed by people from only one professional field. The team’s strength came from combining different forms of expertise and allowing each member to build on the work of the others.
The AI4Health.Cro Innovation Challenge gave the team an opportunity to develop the concept in a structured setting and work with healthcare data in a secure environment for the first time.
The next test will take place outside the competition — in medical practices, with healthcare professionals and patients, and through measurable results.
