Alicia is Assistant Professor at the Cardiology department in Amsterdam UMC and an epidemiologist specialised in cardiovascular (pharmaco)epidemiology, with a focus on optimising and personalising the care and treatment for cardiovascular patients, particularly those with heart failure (HF). Her research leverages complex, linked real-world data, including electronic health records (EHR) and clinical registries, to support data-driven decision-making in cardiovascular care. By integrating pharmaco-epidemiological methods and predictive modelling, she investigates treatment effectiveness, adherence, and long-term outcomes, aiming to enhance patient-centred care. With a strong interest in applied science, she contributes to the implementation of patient-centred innovations within healthcare systems.

A central focus of her work is the data-driven re-phenotyping of cardiovascular diseases outside their current classification using routinely collected clinical data to predict risks of outcomes and inform targeted treatment strategies. She has developed and validated algorithms to classify HF subtypes and applies data-driven methods such as latent class analysis and machine learning to identify clinically meaningful patient profiles. These phenotypes are used to explore treatment heterogeneity and to emulate clinical trials using observational data, providing insights into optimal therapeutic strategies in routine care.

With a strong interest in translational research, Alicia contributes to the implementation and evaluation of predictive tools within healthcare systems, this is evident in the PROMPT-Amsterdam project in which an decision support alert is implemented in EPIC EHR system to alert physicians to prescribe all guideline recommended treatments for patients with heart failure, or in the CARE-HEART project, in which her team will develop AI-based models that use structured and unstructured EHR data to identify patients in need of proactive or palliative care. She is also committed to ensuring that these innovations in cardiovascular care are inclusive, taking into account patient wishes to inform the development of models.

Her work is grounded in the recognition that large-scale, routinely collected healthcare data represents a critical, yet, underutilised asset for transforming cardiovascular care. With a clear vision for bridging the gap between clinical research and real-world practice, she leads efforts to convert complex data into clinically interpretable, and implementation-ready tools. Through this work, she is building the foundation for a scalable, learning healthcare system, positioning herself as a key figure in the future of data-driven, patient-centred cardiovascular medicine.

Cardiovascular Epidemiology
Personalised Medicine
Causal Inference
Real-World Data and Data Linkage
Registry-based Research