Theme 1 (T1) investigates methods and frameworks for developing scalable and secure data-driven software systems: interoperable software architectures, scalable workflow analytics, clinical decision support, systems sustainability.

Theme 2 (T2) enables the vision of a highly heterogenous data environment where patient-generated content, including data from wearables and structured/unstructured information from electronic health records, can combine seamlessly, machine learning from heterogenous data, time series analytics and natural language processing for deep phenotyping.

Complex Simulations and Digital Twins

Theme 3 (T3) focuses on the paradigm of building simulated environments, including healthcare settings or virtual patients, to enable training machine learning and AI models: predictive analytics, systems simulation, casual inference.

Next-Generation Clinical User Interfaces

Theme 4 (T4) places usability front and centre to ensure health data science applications are usable in clinical settings and are aligned with users' workflows: knowledge representation, designing for usability, voice and video interfaces.

Co-designing Impactful Patient-Centric Healthcare Solutions

Theme 5 (T5) is a cross-cutting theme, exploring co-production and co-design, stakeholder engagement, evaluation techniques and maximising impact: public and patient involvement, co-production and co-design, medical device and data regulation, productising academic software, and health economics and evaluation.