Exploring the critical intersections of health and science.
The focus of this research is to advance understanding and effective action toward achieving health equity, ensuring all individuals have a fair and just opportunity to attain their highest standard of health regardless of race, gender, socioeconomic background, disability, or geography. This work examines the interactions among social determinants of health, healthcare access, policy, and community engagement to identify and address systemic inequities.
The research activities in this unit focus on the rigorous generation and integration of real-world data (RWD) and real-world evidence (RWE) to inform health technology assessment (HTA), health economics, and outcomes research. The unit aims to bridge evidence gaps left by clinical trials by leveraging observational data, electronic health records, registries, and novel digital sources to assess the cost-effectiveness, safety, and long-term outcomes of health interventions...
...in actual clinical practice. Special emphasis is placed on methodological advances to generate transparent, robust, and representative evidence, ensuring data-driven insights support equitable, efficient, and patient-centred policy and reimbursement decisions. Through multi-disciplinary approaches, the unit addresses challenges in data quality, methodological rigour, and stakeholder alignment, ultimately improving healthcare value and patient outcomes across diverse populations.
This unit will investigate the principles and impact of patient engagement and shared decision-making (SDM) in healthcare settings. SDM is a collaborative process in which clinicians and patients work together to make decisions, combining the best available scientific evidence with the patient’s values, preferences, and circumstances. Research consistently demonstrates that effective patient engagement and SDM not only improve patient knowledge, satisfaction, and adherence to treatments but also contribute to better clinical outcomes, patient safety, and empowerment.
This unit aims to advance the science and implementation of digital health technologies by leveraging Artificial Intelligence (AI) and Machine Learning (ML) to improve patient outcomes, health system efficiency, and equity. The research integrates robust theoretical frameworks with practical innovation across electronic health records, wearables, sensors, mobile technologies, and social media data. Our focus is on developing, validating, and deploying next-generation AI models, ensuring their responsible and equitable use.
Implementation Science is the systematic study of strategies and methods that promote the integration of evidence-based research findings into routine practice and policy contexts. This field addresses the persistent “know-do gap” — the divide between the discovery of effective interventions and their consistent, equitable adoption in real-world settings. The central aim is to generate high-quality evidence on how best to design, test, and sustain implementation strategies.
This unit is centered on advancing the field of translational science, which focuses on understanding and overcoming the scientific and operational barriers that slow the transition of discoveries from the laboratory, clinic, or community into real-world health benefits. Unlike translational research, which is disease- or target-specific, translational science takes a broader, disease-agnostic approach to identify generalizable principles that accelerate translation.