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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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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
first_indexed 2024-07-04T16:10:18Z
last_indexed 2024-11-25T14:19:15Z
id cronfa66964
recordtype RisThesis
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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. 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spelling 2024-07-04T17:46:48.7847608 v2 66964 2024-07-04 Beyond Rare Disease Patients: Exploring Machine Learning Interventions To Support People Experiencing a Diagnostic Odyssey 853c2fabc13749873d76bcaf8b0e0f4e Emily Nielsen Emily Nielsen true false 2024-07-04 SAAS 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. E-Thesis Swansea, Wales, UK Human Centred Computing, Human-Computer Interaction, Participatory Design, Rare disease, Consumer Health, Diagnosis, Patient Centred Design 17 6 2024 2024-06-17 10.23889/SUthesis.66964 COLLEGE NANME Swansea Academy for Academic Success COLLEGE CODE SAAS Swansea University Owen, Tom ; Roach, Matt J. ; Dix, Alan J. Doctoral Ph.D EPSRC Centre for Doctoral Training in Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems and Amicus Therapeutics (EP/S021892/1) EPSRC Centre for Doctoral Training in Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems and Amicus Therapeutics (EP/S021892/1) 2024-07-04T17:46:48.7847608 2024-07-04T16:25:49.7031720 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Emily Nielsen 1 66964__30833__29302ab96f834eb9b81c45835652b287.pdf Nielsen_Emily_PhD_Thesis_Final_Cronfa.pdf 2024-07-04T17:43:33.7315591 Output 8469333 application/pdf E-Thesis – open access true 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. true eng https://creativecommons.org/licenses/by/4.0/deed.en
title Beyond Rare Disease Patients: Exploring Machine Learning Interventions To Support People Experiencing a Diagnostic Odyssey
spellingShingle Beyond Rare Disease Patients: Exploring Machine Learning Interventions To Support People Experiencing a Diagnostic Odyssey
Emily Nielsen
title_short Beyond Rare Disease Patients: Exploring Machine Learning Interventions To Support People Experiencing a Diagnostic Odyssey
title_full Beyond Rare Disease Patients: Exploring Machine Learning Interventions To Support People Experiencing a Diagnostic Odyssey
title_fullStr Beyond Rare Disease Patients: Exploring Machine Learning Interventions To Support People Experiencing a Diagnostic Odyssey
title_full_unstemmed Beyond Rare Disease Patients: Exploring Machine Learning Interventions To Support People Experiencing a Diagnostic Odyssey
title_sort Beyond Rare Disease Patients: Exploring Machine Learning Interventions To Support People Experiencing a Diagnostic Odyssey
author_id_str_mv 853c2fabc13749873d76bcaf8b0e0f4e
author_id_fullname_str_mv 853c2fabc13749873d76bcaf8b0e0f4e_***_Emily Nielsen
author Emily Nielsen
author2 Emily Nielsen
format E-Thesis
publishDate 2024
institution Swansea University
doi_str_mv 10.23889/SUthesis.66964
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
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description 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.
published_date 2024-06-17T08:31:45Z
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score 11.085372

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