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Translating Predictive Models for Alzheimer’s Disease to Clinical Practice: User Research, Adoption Opportunities, and Conceptual Design of a Decision Support Tool

Bellio, Maura; (2021) Translating Predictive Models for Alzheimer’s Disease to Clinical Practice: User Research, Adoption Opportunities, and Conceptual Design of a Decision Support Tool. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

Alzheimer’s Disease (AD) is a common form of Dementia with terrible impact on patients, families, and the healthcare sector. Recent computational advances, such as predictive models, have improved AD data collection and analysis, disclosing the progression pattern of the disease. Whilst clinicians currently rely on a qualitative, experience-led approach to make decisions on patients’ care, the Event-Based Model (EBM) has shown promising results for familial and sporadic AD, making it well positioned to inform clinical decision-making. What proves to be challenging is the translation of computational implementations to clinical applications, due to lack of human factors considerations. The aim of this Ph.D. thesis is to (1) explore barriers and opportunities to the adoption of predictive models for AD in clinical practice; and (2) develop and test the design concept of a tool to enable EBM exploitation by AD clinicians. Following a user-centred design approach, I explored current clinical needs and practices, by means of field observations, interviews, and surveys. I framed the technical-clinical gap, identifying the technical features that were better suited for clinical use, and research-oriented clinicians as the best placed to initially adopt the technology. I designed and tested with clinicians a prototype, icompass, and reviewed it with the technical teams through a series of workshops. This approach fostered a thorough understanding of clinical users’ context and perceptions of the tool’s potential. Furthermore, it provided recommendations to computer scientists pushing forward the models and tool’s development, to enhance user relevance in the future. This thesis is one of the few works addressing a lack of consensus on successful adoption and integration of such innovations to the healthcare environment, from a human factors’ perspective. Future developments should improve prototype fidelity, with interleaved clinical testing, refining design, algorithm, and strategies to facilitate the tool’s integration within clinical practice.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Translating Predictive Models for Alzheimer’s Disease to Clinical Practice: User Research, Adoption Opportunities, and Conceptual Design of a Decision Support Tool
Event: UCL
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2021. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10134507
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