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Title: The quantified patient in the doctor's office : understanding clinical workflows for using patient self-tracked data
Author: West, Peter
ISNI:       0000 0004 9352 6764
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2019
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Fitbit and Apple Health are two popular consumer technologies amongst a growing plethora of health wearables and smartphone apps. These devices have empowered a new kind of patient – the quantified patient – to collect data on diverse aspects of their own health. From heart rate and physical activity, to sleep and mood, these data have the potential to help clinicians diagnose disease, personalise treatments to individual patients, and avoid delivering unnecessary medical procedures. Realising this potential is vital as we enter an era of ageing population, chronic disease epidemics, and soaring healthcare costs. However, these self-tracked data are new to medicine, so it is unknown how clinicians might use such unfamiliar data. This research aimed to understand clinicians’ experiences with self-tracked data in their clinical workflows, such that future use of such data can be enabled through appropriate technology design and consideration of clinicians’ work practices. Interviews were conducted with 13 clinicians of a broad spectrum of clinical roles, including cardiology, general practice, and mental health. This was followed by workshops with five clinicians in the co-design of a software-based tool for using self-tracked data within the management of chronic heart conditions. These studies revealed that there are common clinical workflows for using self-tracked data, delineating a process of evaluating data usability while collaborating with the patient to ensure mutual understanding. However, constraints of the clinical settings and of data usability presented barriers to this workflow, limiting the potential for self-tracked data. The co-designed prototype unveiled several design principles for overcoming these barriers, reflecting the importance of clinicians’ participation in future research of self-tracked data. This research contributes an understanding of the diverse opportunities for self-tracked data and design principles for overcoming the barriers to using such data in a future data-driven medicine.
Supervisor: Giordano, Richard ; Weal, Mark Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available