The quantified patient in the doctor's office: challenges & opportunities
The quantified patient in the doctor's office: challenges & opportunities
While the Quantified Self and personal informatics fields have focused on the individual's use of self-logged data about themselves, the same kinds of data could, in theory, be used to improve diagnosis and care planning. In this paper, we seek to understand both the opportunities and bottlenecks in the use of self-logged data for differential diagnosis and care planning during patient visits to both primary and secondary care. We first conducted a literature review to identify potential factors influencing the use of self-logged data in clinical settings. This informed the design of our experiment, in which we applied a vignette-based role-play approach with general practitioners and hospital specialists in the US and UK, to elicit reflections on and insights about using patient self-logged data. Our analysis reveals multiple opportunities for the use of self-logged data in the differential diagnosis workflow, identifying capture, representational, and interpretational challenges that are potentially preventing self-logged data from being effectively interpreted and applied by clinicians to derive a patient's prognosis and plan of care.
quantified self, clinical decision making, self-tracking
West, Peter
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Giordano, Richard
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Van Kleek, Max
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Shadbolt, Nigel
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7 May 2016
West, Peter
f9799b52-f299-41c7-bc6e-bcf15fdc9638
Giordano, Richard
13c61925-de2b-48ae-beab-6aedac3ed14c
Van Kleek, Max
4d869656-cd47-4cdf-9a4f-697fa9ba4105
Shadbolt, Nigel
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
West, Peter, Giordano, Richard, Van Kleek, Max and Shadbolt, Nigel
(2016)
The quantified patient in the doctor's office: challenges & opportunities.
CHI '16: Conference on Human Factors in Computing Systems, , San Jose, United States.
07 - 12 May 2016.
13 pp
.
(doi:10.1145/2858036.2858445).
Record type:
Conference or Workshop Item
(Paper)
Abstract
While the Quantified Self and personal informatics fields have focused on the individual's use of self-logged data about themselves, the same kinds of data could, in theory, be used to improve diagnosis and care planning. In this paper, we seek to understand both the opportunities and bottlenecks in the use of self-logged data for differential diagnosis and care planning during patient visits to both primary and secondary care. We first conducted a literature review to identify potential factors influencing the use of self-logged data in clinical settings. This informed the design of our experiment, in which we applied a vignette-based role-play approach with general practitioners and hospital specialists in the US and UK, to elicit reflections on and insights about using patient self-logged data. Our analysis reveals multiple opportunities for the use of self-logged data in the differential diagnosis workflow, identifying capture, representational, and interpretational challenges that are potentially preventing self-logged data from being effectively interpreted and applied by clinicians to derive a patient's prognosis and plan of care.
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Accepted/In Press date: December 2015
Published date: 7 May 2016
Venue - Dates:
CHI '16: Conference on Human Factors in Computing Systems, , San Jose, United States, 2016-05-07 - 2016-05-12
Keywords:
quantified self, clinical decision making, self-tracking
Organisations:
Web & Internet Science, Faculty of Health Sciences
Identifiers
Local EPrints ID: 386827
URI: http://eprints.soton.ac.uk/id/eprint/386827
PURE UUID: 8186c142-c033-4022-ade2-67b3a29a4f61
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Date deposited: 04 Feb 2016 15:33
Last modified: 16 Mar 2024 04:05
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Author:
Peter West
Author:
Max Van Kleek
Author:
Nigel Shadbolt
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