Dr Hugh-Logan Ellis September Seminar Series
September 2, 2025
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.
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We are looking forward to welcoming Professor Honghan Wu, Professor of Health Informatics and AI at the University of Glasgow, who will deliver his talk “Large language model and Radiology: how to facilitate human and AI collaboration? " as part of our Seminar Series. Abstract: In this upcoming talk, Professor Honghan Wu explores the essential shift from viewing AI as a potential replacement for radiologists to recognizing it as a critical collaborative partner. Moving beyond basic tasks like detection and triage, the presentation highlights how AI can address practical clinical "pain points," such as reducing automated protocoling time by up to 60% and decreasing the time spent communicating with providers and patients by 30%. Professor Wu will present recent research on using knowledge-retrieval and Large Language Models for clinical report error correction and generation. The session concludes with an examination of the real-world deployment lifecycle, discussing the challenges of monitoring the over 700 FDA-cleared radiology AI devices currently in practice Seminar Series Event : “Large language model and Radiology: how to facilitate human and AI collaboration?" Date and Time: Thursday 25 November 2026, 15:00 – 16.00 hrs (GMT) Location: Venue to be confirmed. 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 Honghan Wu is a Professor of Health Informatics and AI, based in the School of Health and Wellbeing of the University of Glasgow, where he leads the research theme of data science and AI. Prof Wu is a co-director of Health Data Research Scotland. He also is an honorary professor at Hong Kong University, an honorary associate professor at Institute of Health Informatics, UCL, and a former Turing Fellow of The Alan Turing Institute, UK's national institute for data science and artificial intelligence. Prof Wu holds a PhD in Computing Science. His current research focuses on machine learning, natural language processing, knowledge graph and their applications in medicine.
We are looking forward to welcoming Dr. Bettina Moltrecht and Thomas Wood to introduce Harmony Meta , a groundbreaking platform developed over the past year to bridge the gap between disparate study catalogues and registers. While traditional data discovery relies on exact keyword matching, Harmony Meta utilizes Large Language Models and vector indexing to allow for semantic searching across 5.5 million variables . Abstract: This session will demonstrate how researchers can now locate longitudinal data using approximate synonyms—for instance, a search for "dyslexia" will successfully retrieve variables related to "difficulty reading." The platform indexes nearly every major longitudinal study ever conducted in the UK, including the Millennium Cohort Study , the 1970 British Cohort Study , and Born in Bradford . The presenters will discuss the technical backend of converting millions of variables into vectors and the practical implications for harmonizing data across different cohorts to identify population mental health trends. Try the Tool: https://harmonydata.ac.uk/search Seminar Series Event : " Harmony Meta: Using AI to Unlock 5.5 Million Variables in UK Longitudinal Studies" Date and Time: Thursday 24 September 2026, 15:00 – 16.00 (BST) Location: Venue to be confirmed. 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. Biographies Dr. Bettina Moltrecht Dr. Bettina Moltrecht is a mental health researcher based at University College London (UCL) and Anna Freud a UK-based mental health charity for children and families. Bettina combines a clinical, tech and research background, and has been co-leading the Harmony project with the aim to enhance population mental health research. Bettina is co-founder of UCL's Digital Mental Health Hub, and is co-investigator on various clinical trials to evaluate mental health interventions in the NHS. Thomas Wood Thomas Wood is the founder of Fast Data Science and the lead developer for the Harmony Meta backend. He holds a Master’s in Physics from Durham University and a Master’s in Computer Speech, Text and Internet Technology from the University of Cambridge. With over a decade of experience in machine learning and NLP, Thomas has consulted for the NHS, Tesco, and Boehringer Ingelheim. He also works as an expert witness and is working on NLP solutions for clinical trials, and generative AI solutions for legal question answering. Note on Funding and Partners: Harmony Meta was funded by the ESRC and developed in collaboration with Population Research UK (PRUK), the UCL Centre for Longitudinal Studies, DATAMIND UK, The Alan Turing Institute, and UK Research and Innovation.
