Congratulations to our student, Dina Farran
May 22, 2024
View Dina's Abstract
We are delighted to share that one of our EPSRC DRIVE-Health CDT students and PhD candidate at the IoPPN, Dina Farran, has been awarded 1st prize presentation at the recent Royal College of Psychiatrists Faculty of Liaison Psychiatry annual conference earlier this month. Dina is working under the supervision of Professor Fiona Gaughran and Professor Mark Ashworth and is about to submit her thesis at the end of this month.
In the presentation, Dina summarised her PhD project consisting of a literature review, 2 observational studies, an intervention and 2 qualitative studies. Dina provides further detail below.
Background
Atrial fibrillation (AF), the most prevalent cardiac arrhythmia, is associated with an increased risk of stroke contributing to heart failure and death. In this project, we aim to improve patient safety by screening for stroke risk among people with AF and co-morbid mental illness.
Methods
(a) Conducted a systematic review and meta-analysis on prevalence, management, and outcomes of AF in people with Serious Mental Illness (SMI) versus the general population.
(b) Evaluated oral anticoagulation (OAC) prescription trends in people with AF and co-morbid SMI in King’s College Hospital.
(c) Identified the recorded rates of OAC prescription among people with AF and various mental illnesses and evaluated the association between mental illness severity and OAC prescription in eligible patients in South London and Maudsley (SLaM) NHS Foundation Trust.
(d) Implemented an electronic clinical decision support system (eCDSS) consisting of a visual prompt on patient electronic Personal Health Record to screen for AF-related stroke risk in three Mental Health of Older Adults wards at SLaM.
(e) Assessed the feasibility and acceptability of the eCDSS by qualitatively investigating clinicians’ perspective of the potential usefulness of the eCDSS (pre-intervention) and their experiences and their views regarding its impact on clinicians and patients (post-intervention).
Results
(a) People with SMI had low reported rates of AF. AF patients with SMI were less likely to receive OAC than the general population. When receiving warfarin, people with SMI, particularly bipolar disorder, experienced poor anticoagulation control compared to the general population. Meta-analysis showed that SMI was not significantly associated with an increased risk of stroke or major bleeding when adjusting for underlying risk factors.
(b) Among AF patients having a high stroke risk, those with co-morbid SMI were less likely than non-SMI patients to be prescribed any OAC, particularly warfarin (but not DOACs). However, there was no evidence of a significant difference between the two groups since 2019.
(c) Adjusting for age, sex, stroke and bleeding risk scores, patients with AF and co-morbid SMI were less likely to be prescribed any OAC compared to those with dementia, substance use disorders or common mental disorders. Among AF patients with co-morbid SMI, warfarin was less likely to be prescribed to those having alcohol or substance dependency, serious self-injury, hallucinations or delusions and activities of daily living impairment.
(d) Clinicians were asked to confirm the presence of AF, clinically assess stroke and bleeding risks, record risk scores in clinical notes and refer patients at high risk of stroke to OAC clinics.
(e) Clinicians reported that the eCDSS saved time, prompted them towards guidelines, boosted their confidence, and identified patients at risk. Perceived barriers to using the tool included low admission rate of AF cases, low or insufficient visibility of the alert/awareness of the tool, and impact of the eCDSS on workload.
Conclusions
This study presents a unique opportunity to quantify AF patients with mental illness who are at high risk of severe outcomes, using electronic health records. This has the potential to improve health outcomes and therefore patients' quality of life.
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We're pleased to announce that 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 - will deliver our September Seminar Series. In his talk “Extracting Clinical Value from EHR Data: Challenges, Pitfalls, and Practical Lessons" , In his talk, Hugh will share 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.

We’re pleased to announce that Dr Petroula Laiou from King's College London , will deliver our May Seminar Series with her talk, "Bridging the Gap: Turning Academic Research into Clinical Innovation " . Petroula will share her journey of translating cutting-edge academic research into a mission-driven MedTech company. The spinout is pioneering a novel approach to forecasting and preventing seizures in people with drug-resistant epilepsy - an innovation rooted in years of interdisciplinary work at the intersection of clinical neuroscience, signal processing, and artificial intelligence. Dr. Laiou will take the audience through the full translational pathway: from identifying an unmet clinical need, designing and analysing first-in-human studies, and developing a seizure prediction algorithm, to securing translational funding, navigating the intellectual property landscape, and filing an international patent (PCT/GB2024/052456). She will reflect on key lessons learned during her time in the King’s MedTech Accelerator Programme - where the team won the Best Innovation award - and share insights on building bridges between academia and industry, shaping a commercialization strategy, and transitioning from researcher to entrepreneur. The talk will also highlight the challenges and rewards of launching a spinout in the healthcare sector and offer practical advice for PhD students and early-career researchers considering the entrepreneurial route. Seminar Series Event: "Bridging the Gap: Turning Academic Research into Clinical Innovation" Date and Time: Wednesday 7 May 2025, 15:00 – 16.00 hrs (BST) Location: The Lorna Wing 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. Dr. Petroula Laiou is a Research Fellow in Predictive Modelling and Clinical Neuroscience at King’s College London. With a background in mathematics, computational physics, and a PhD in signal analysis, her research bridges computer science, neuroscience, and machine learning. Her work focuses on developing predictive models and digital biomarkers for neurological and psychiatric disorders, including epilepsy and depression. Dr. Laiou led the development of a novel seizure forecasting algorithm using intracranial EEG and cortical responses to electrical stimulation—research that led to the filing of an international patent (PCT/GB2024/052456). She is the recipient of multiple research grants, including an MRC award as Principal Investigator, and her translational work was recognised by the King’s MedTech Accelerator Programme, where her team won the Best Innovation award. She has authored over 40 peer-reviewed publications, presented at major international conferences, and actively contributes to interdisciplinary collaborations across academia, hospitals, and industry.