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 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.
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