Lucas Seuren

Research Fellow

  • Usher Institute
  • College of Medicine and Veterinary Medicine
  • Centre for Biomedicine, Self & Society

Contact details

Address

Street

Usher Institute – University of Edinburgh
Usher Building
5-7 Little France Road
Edinburgh BioQuarter - Gate 3

City
Edinburgh
Post code
EH16 4UX

Biography

Background

Lucas Seuren is a linguist and social scientist using qualitative methods to explore and evaluate the implementation and adoption of technology in health and social care.  Since completing his PhD, he has been leading research on how virtual modalities change the organisation and interactional dynamics of healthcare services, with a particular focus on video consultations. He has worked closely with providers and policymakers to shape appropriate guidance and evaluate novel health programs that integrate virtual care delivery. His current research interests focus on the adoption of AI and Machine Learning systems in healthcare services, especially those based on LLMs: for example, Ambient AI Scribes and AI-assisted discharge summaries. Using a socio-technical lens, he aims to understand how technology can be appropriately integrated into service routines.

Qualifications

PhD Linguistics (2018)

MA Linguistics (2013) - Cum Laude

MA Communication & Information Sciences (2012) - Cum Laude

BA Communication & Information Sciences (2011)

Research summary

Lucas's main research interests is in how technology shapes healthcare service delivery, especially the interactional dynamics between healthcare providers and patients. He uses qualitative methods such as video-based conversation analysis and qualitative interviews to investigate how people interact with and through technology and how they experience these novel systems. 

Current research interests

Lucas is currently exploring the development and implementation of AI and Machine Learning in different domains of healthcare. Within frontline services, his research explores Ambient AI Scribes, focusing on appropriate regulation, monitoring, and its implications for care experiences, such as clinical expertise and the therapeutic relationship. He is also leading qualitative research on Gliomatch, which investigates the development of a multimodal ML tool to facilitate stratification for patients with glioblastoma. His research uses implementation science to explore the pathway from clinical research to validation and implementation in clinical practice.

Project activity

Gliomatch: https://gliomatch.eu/

Past project grants

Supporting Consultations in Remote Physiotherapy (SCiP): a mixed-methods study of physical examinations by video in NHS physiotherapy services - NIHR Policy Research Programme 2021-2022

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