Next Generation PhD Health Data Scientists
We enable the brightest minds in health data science to become the research and innovation leaders of tomorrow
Our ambition
The King's College London Centre for Doctoral Training in Data-Driven Health (KCL DRIVE-Health) is training the next generation of PhD health data scientists to become the innovation leaders of tomorrow. Our students work within an active NHS environment, and develop new models of data-driven care, whilst leveraging significant recent investment and infrastructure in Health Data Research within the UK.
Our strategic aims
To provide world-class training in health data science research to the next generation of health data scientists, who will have the multidisciplinary skills needed to enable transformations in public health and breakthrough treatments
To solve the most challenging problems in data-driven health research, through a diverse community of the brightest minds in health data science; and an open, collaborative culture which fosters exchange and champions innovation
To co-create a translational cross-sector collaboration with the NHS, industry, enterprise, policy makers and academia - a first of it's kind for health informatics training in the UK. We work with our broad network of strategic partners to ensure that the UK health sciences sector remains at the global forefront of innovation
Our science and learning environment
There are significant opportunities to revolutionise the delivery of healthcare, with the growing availability of biological, social, genomic, imaging and sensor/Internet-of-Things datasets and electronic health records. The KCL DRIVE-Health PhD Programmes focus on four key scientific themes.
THEME 1
THEME 2
THEME 3
THEME 4
Latest News, Views & Insights
We are pleased to welcome Dr Antonio de Marvao - Clinical Senior Lecturer at King's College London, and Consultant Cardiologist and Obstetric Physician at GSTT and KCH - who will deliver his talk “Detecting the Rare, Managing the Common: AI-Driven Cardiovascular Care Using EHR Data" as part of our Seminar Series. Abstract: Cardiovascular disease encompasses rare inherited conditions and highly prevalent disorders such as hypertension and cardiometabolic disease. Despite differing epidemiology, both require accurate, dynamic and scalable risk stratification. Electronic health records provide longitudinal, multimodal data at population scale. However, their heterogeneity and fragmentation demand advanced artificial intelligence methods to generate clinically actionable insight. Approximately 70 to 80 percent of NHS data exists in unstructured free text, rendering much of the clinically relevant signal inaccessible to conventional analytics without natural language processing or large language models. To address this challenge, we have been developing an AI-enabled framework for real-world cardiovascular risk prediction using integrated EHR data. The approach brings together structured clinical variables, imaging outputs and free-text documentation within secure hospital environments. Natural language processing and large language models are used to transform narrative records into computable features, while chain-of-thought reasoning architectures extract guideline-defined risk parameters directly from routine documentation. This enables automated calculation of established risk scores and dynamic longitudinal reassessment within an agentic workflow. Local, open-source models are evaluated across parameter scales to ensure an appropriate balance between accuracy, safety and computational efficiency for clinical deployment. In inherited cardiac conditions, this approach enables automated extraction of echocardiographic and clinical features required for sudden cardiac death risk prediction, reducing manual burden and supporting real-time monitoring. The same principles extend to hypertensive disorders of pregnancy, facilitating earlier detection, structured surveillance and stratification of long-term cardiovascular risk. Integration of high-resolution EHR-derived phenotypes with genomic and multi-omics datasets further supports progression from risk prediction to biological insight and therapeutic target discovery. Applied rigorously, AI methodologies operating on routine healthcare data provide a scalable foundation for precision cardiovascular care across the life course. Seminar Series Event : “Detecting the Rare, Managing the Common: AI-Driven Cardiovascular Care Using EHR Data" Date and Time: Tuesday 24 February 2026, 15:30 – 16.30 hrs (GMT) Location: IoPPN Seminar 1 & 2, Denmark Hill Campus Attendance: Mandatory for all DRIVE-Health students; a calendar invitation has already been sent. Registration: Alumni and wider King's College London research community all welcome - please email drive-health-cdt@kcl.ac.uk to let us know if you would like to attend. Biography Antonio de Marvao is a Clinical Senior Lecturer at KCL, and a Consultant Cardiologist and Obstetric Physician at GSTT and KCH, specialising in inherited cardiac conditions, maternal cardiology, and hypertensive disorders of pregnancy. His research sits at the intersection of electronic health records (EHR) derived phenotyping, genomics/multi-omics, and cardiovascular imaging, using machine learning to improve risk prediction modelling and personalise care, across the reproductive continuum - from pregnancy to postpartum - and long-term cardiovascular prevention. He leads work within the NHS England Genomic AI Network, applying natural language processing, large language models and multimodal EHR integration to identify patients with inherited cardiovascular disease, streamline specialist review, and improve access to genetic testing and family screening. In parallel, his group also uses AI and EHR data to better define and detect hypertensive disorders of pregnancy at scale, quantify disparities, and enable earlier, more targeted intervention.
We were pleased to welcome Dr Jacqueline Matthew - Clinical Research Fellow/Sonographer at King's College London - who delivered her talk “From Noise to Signal: A Clinical Researcher's Perspective on Translating Advances in Prenatal imaging into Practice" as part of our Seminar Series. Abstract: Over the past decade, machine learning approaches in prenatal imaging has advanced from exploratory academic prototypes to clinically usable, real-time tools, but the path between those two endpoints is rarely straightforward. In this talk, Jacqueline offered a clinical researcher’s perspective on translating biomedical engineering innovations into real-world impact, tracing the journey from the iFIND project’s early breakthroughs in automated fetal imaging to the creation of Fraiya, an AI-driven ultrasound platform now entering clinical deployment. She unpacked the technical, clinical, and regulatory hurdles that shape this trajectory: data acquisition at scale, annotation complexity, model robustness, pipeline optimisation for real-time use, clinical safety engineering, regulatory strategy, and integration with NHS digital ecosystems. Beyond the technical achievements, the session reflected honestly on the innovation “gaps” that researchers and engineers encounter when stepping into entrepreneurship. From productising research outputs, building 'with' clinicians and service users not just 'for' them, securing buy-in, navigating procurement, and proving value in operationally stretched healthcare services. The aim was to provide a pragmatic and motivating roadmap for researchers and innovators seeking to turn biomedical AI research into deployable, sustainable solutions in healthcare. Seminar Series Event : “From Noise to Signal: A Clinical Researcher's Perspective on Translating Advances in Prenatal imaging into Practice. Date and Time: Thursday 22 January 2026, 15:00 – 16.00 hrs (GMT) Location: K39, King's Building, Strand Campus Attendance: Mandatory for all DRIVE-Health students, therefore please accept the calendar invitation. Registration: Alumni and wider King's College London research community all welcome - please email drive-health-cdt@kcl.ac.uk to let us know if you would like to attend. Biography Jacqueline is a clinical academic, sonographer, and MedTech entrepreneur with over 20 years of experience in advancing pregnancy care through compassionate, technology-driven solutions. Specialising in ultrasound and fetal MRI, Jacqueline’s work focuses on leveraging cutting-edge imaging technologies to improve screening, diagnosis, and care for pregnant women. With a PhD in advanced 3D ultrasound and fetal MRI, Jacqueline uses machine learning to refine diagnostic pathways, pushing the boundaries of what’s possible in prenatal care. As Clinical Lead and Chief Medical Officer at an early-stage health tech startup, she has been at the forefront of developing a real-time AI-powered pregnancy ultrasound platform, with ambitions to transform how scans are performed, enhancing diagnostic accuracy, and empowering healthcare professionals to deliver more informed and compassionate care. Jacqueline’s work has earned her widespread recognition, including being named one of the inaugural winners of the NHS England CAHPO Gold Award for Excellence, which celebrates health professionals who exemplify exceptional contributions to healthcare and the NHS values.
We were thrilled to welcome Dr Abhi Pratap - Global Clinical Development Lead at Boehringer Ingelheim who delivered our October Seminar Series. In his talk “Why Mental Health Needs More Than New Drugs: Using Digital Health to Bring Patient-Centredness to Research and Care" , Abhi shared case examples from emerging clinical studies to show how digital health can bridge the gap between clinical research and patient care in mental health. We will explore digital health solutions that help quantify the real-world experiences of health that matter to people - bringing us closer to understanding what treatments work for whom, why, when, and for how long. Abstract: Innovation in mental health treatment has been strikingly limited compared to other fields of medicine. In the last 15 years, fewer than five truly novel psychiatric drugs have received regulatory approval. This stagnation reflects multifaceted challenges linked to heterogeneity of psychiatric disorders often lacking biological markers grounded in disease biology. Additionally, there is significant reliance on subjective clinician-, rater-, or patient-reported outcomes, which increases variability in trial outcomes and poses challenges in patient selection and endpoint determination. Clinical studies also encounter persistent obstacles, such as high dropout rates, poor generalizability, and endpoints that frequently do not reflect what patients and their families value most. Consequently, there is a critical gap in new treatment development that are patient-centered, enhancing quality of life in real-world settings. Use-case-centered implementation of digital health technologies offers a realistic path to address many of these barriers. Real-world data collected from smart devices can enable the continuous and ecologically valid capture of mood, cognition, behavior, and functioning, augmenting traditional, episodic assessments. This richer measurement framework can enhance sensitivity to change, reduce trial inefficiencies, and ground outcomes more directly in patients lived experience. In addition, the same smart devices can be used to deliver digital adaptations of psychosocial interventions, expanding access to evidence-based care and offering personalized and scalable options for populations that have been historically underserved due to stigma, geography, or cost. Dr. Abhi Pratap is the Global Clinical Development Lead at Boehringer Ingelheim, where he oversees clinical development programs for digital therapeutics aimed at addressing unmet needs in serious mental illnesses. Before joining Boehringer, he worked at Biogen, managing one of the largest decentralised studies on cognitive trajectories in real-world settings in collaboration with Apple. With over 15 years of experience in translational biomedical research, Dr. Pratap has led numerous health research studies that promote partnerships between academia and industry. His primary focus is on using digital health technologies to gain a deeper understanding of the real-life experiences of individuals with neurological and psychiatric disorders. His cross-sector research aims to accelerate patient-centered clinical development in central nervous system (CNS) disorders. Most recently, he led a successful pivotal Phase III trial targeting experiential negative symptoms of schizophrenia (NCT05838625) using a digital therapeutic. This study is among the first confirmatory trials to show improvement in negative symptoms to date. Additionally, Dr. Pratap serves as an adjunct faculty member at the University of Washington in Seattle and Boston University, and he is a visiting research fellow at King’s College London.
It was great to welcome back DRIVE-Health PhD student, Dr Hugh Logan-Ellis - a Diabetes and Endocrinology Registrar at King's and ex-Research Fellow in the Department of Medicine at Dalhousie University - who delivered our September Seminar Series. In his talk “Extracting Clinical Value from EHR Data: Challenges, Pitfalls, and Practical Lessons" , Hugh shared what clinicians have taught him about the reality of working with Electronic Health Record data and what they genuinely need from #AI tools, rather than what researchers might think they should want. Hugh has learned that making the most clinically useful tool could matter more than theoretical perfection. He'll discuss some principles he's gathered to help create AI solutions that fit seamlessly into clinical workflows, which he hopes might help others bridge the gap between academic research and genuine patient benefit. Using his PhD research on creating a single unit of health from #EHR data as a central example, Hugh will explore broader challenges: the messiness of real-world clinical data, the proliferation of unused risk scores, and why so many promising algorithms never make it past publication. These insights aim to help researchers develop tools that won't just die in papers, but have a real chance of improving clinical care. Seminar Series Event: " Extracting Clinical Value from EHR Data: Challenges, Pitfalls, and Practical Lessons" Date and Time: Thursday 25 September 2025, 12:00 – 13.00 hrs (BST) Location: The Judy Dunn Room, SGDP Building, Denmark Hill Campus, London, SE5 8AF Attendance: Mandatory for all DRIVE-Health students, therefore please accept the calendar invitation. Registration: Alumni and wider King's College London research community all welcome - please email drive-health-cdt@kcl.ac.uk to let us know if you would like to attend. Abstract: Picture the scene: It's Saturday morning, you're the senior resident doctor on call in a busy hospital, and you have a 40-page list of patients due for review. Half of your junior colleagues have called in sick, and you know you can't possibly see everyone. How do you decide who needs to be seen most urgently? The information to make these decisions is in the electronic health records, but accessing it quickly means opening each patient's chart individually. My PhD tries to tackle this problem: could we use an algorithm to compress scattered clinical data into a single, practical number? This question has led me on an interesting journey. I've spoken with clinicians from around the world about how they decide who is "sickest," discovering a surprising variety of terms for essentially the same idea and realising we might need more than one measure. My research has taken me to Canada to collaborate with Professor Kenneth Rockwood OC, whose groundbreaking work on frailty measurement has significantly shaped clinical practice worldwide. Working alongside him has given me valuable insights into why some academic ideas successfully transform patient care, while others remain confined to journals. As I explored increasingly sophisticated approaches to measure sickness, from simple laboratory-based indices to complex machine learning models, I stumbled across a key insight. Supervised machine learning can hindered by retrospective health data because when sick patients are successfully treated, they don’t have poor outcomes. This isn't just a quirky finding relevant to my PhD; it has broader implications for using a supervised paradigm on retrospective data whenever effective treatments are already in place. Bio Hugh is a resident medical doctor specialising in Internal Medicine and Diabetes and Endocrinology, working on his PhD at King's College London. His research focuses on measuring patient health status using electronic health records, drawing on his experience working across various healthcare settings in the UK and internationally.
