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E-Thesis 87 views 36 downloads

Beyond Rare Disease Patients: Exploring Machine Learning Interventions To Support People Experiencing a Diagnostic Odyssey / Emily Nielsen

Swansea University Author: Emily Nielsen

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    Copyright: The author, Emily E. Nielsen, 2024. Released under the terms of a Creative Commons Attribution-Only (CC-BY) License. Third party content is excluded for use under the license terms.

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DOI (Published version): 10.23889/SUthesis.66964

Abstract

People with a rare condition face several hurdles throughout their odyssey to obtain a diagnosis. This odyssey lasts several years and involves frequent referrals and misdiagnoses, often resulting in permanent and severe consequences on patients’ health. In addition, patients feel unheard by their h...

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Published: Swansea, Wales, UK 2024
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Owen, Tom ; Roach, Matt J. ; Dix, Alan J.
URI: https://cronfa.swan.ac.uk/Record/cronfa66964
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Abstract: People with a rare condition face several hurdles throughout their odyssey to obtain a diagnosis. This odyssey lasts several years and involves frequent referrals and misdiagnoses, often resulting in permanent and severe consequences on patients’ health. In addition, patients feel unheard by their healthcare providers and isolated from their peers who ‘just don’t understand’. The UK Strategy for Rare Diseases states that patients can play a significant role in their diagnosis if given suitable resources. However, patients with rare diseases feel that they lack the support they need. This thesis explores the role that technology can have in addressing this gap in support.Within this context, this thesis spans a range of topics, from human-centred design approaches to generating data and presenting a new methodological approach. Through a human-centred approach, we characterise the needs of rare disease patients, thus opening the research space to include previously unmet support needs. In addition, we identify limitations with existing measures of success and highlight the importance of a reduction in the time of diagnosis for rare disease pre-diagnostic technology. This provides the basis the simulation-based methodological approach that we develop. The simulation-task aimed to mirror the information seeking tasks that rare disease patients undertake. To do this, we curate data that is representative of a rare disease patient’s perspective, both in terms of the terminology used and the stage in which symptoms and clinical findings are discovered. In addition, we curate a pre-diagnostic patient matching prototype that is designed around rare disease patients’ needs and demonstrate that (in comparison to two search engines) our application shows greater potential to: aid clinical experiences; facilitate empathetic support networks; and provide better facilitation of information-seeking. All of these contributions stem from a critical examination of the experiences that rare disease patients go through on their journeys towards diagnosis and aim to pave the way for future research within this area.
Keywords: Human Centred Computing, Human-Computer Interaction, Participatory Design, Rare disease, Consumer Health, Diagnosis, Patient Centred Design
College: Faculty of Science and Engineering
Funders: EPSRC Centre for Doctoral Training in Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems and Amicus Therapeutics (EP/S021892/1)