Common barriers to the use of patient-generated data across clinical settings
Common barriers to the use of patient-generated data across clinical settings
Patient-generated data, such as data from wearable fitness trackers and smartphone apps, are viewed as a valuable information source towards personalised healthcare. However, studies in specific clinical settings have revealed diverse barriers to their effective use. In this paper, we address the following question: are there barriers prevalent across distinct workflows in clinical settings to using patient-generated data? We conducted a two-part investigation: a literature review of studies identifying such barriers; and interviews with clinical specialists across multiple roles, including emergency care, cardiology, mental health, and general practice. We identify common barriers in a six-stage workflow model of aligning patient and clinician objectives, judging data quality, evaluating data utility, rearranging data into a clinical format, interpreting data, and deciding on a plan or action. This workflow establishes common ground for HCI practitioners and researchers to explore solutions to improving the use of patient-generated data in clinical practices.
patient-generated data, personalized medicine, self-tracking, workflows, clinical decision making, mHealth, quantified self
1-13
Association for Computing Machinery
West, Peter
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Van Kleek, Max
4d869656-cd47-4cdf-9a4f-697fa9ba4105
Giordano, Richard
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Weal, Mark
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Shadbolt, Nigel
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21 April 2018
West, Peter
f9799b52-f299-41c7-bc6e-bcf15fdc9638
Van Kleek, Max
4d869656-cd47-4cdf-9a4f-697fa9ba4105
Giordano, Richard
13c61925-de2b-48ae-beab-6aedac3ed14c
Weal, Mark
e8fd30a6-c060-41c5-b388-ca52c81032a4
Shadbolt, Nigel
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
West, Peter, Van Kleek, Max, Giordano, Richard, Weal, Mark and Shadbolt, Nigel
(2018)
Common barriers to the use of patient-generated data across clinical settings.
Mandryk, Regan, Hancock, Mark, Perry, Mark and Cox, Anna
(eds.)
In CHI '18 : Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems.
Association for Computing Machinery.
.
(doi:10.1145/3173574.3174058).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Patient-generated data, such as data from wearable fitness trackers and smartphone apps, are viewed as a valuable information source towards personalised healthcare. However, studies in specific clinical settings have revealed diverse barriers to their effective use. In this paper, we address the following question: are there barriers prevalent across distinct workflows in clinical settings to using patient-generated data? We conducted a two-part investigation: a literature review of studies identifying such barriers; and interviews with clinical specialists across multiple roles, including emergency care, cardiology, mental health, and general practice. We identify common barriers in a six-stage workflow model of aligning patient and clinician objectives, judging data quality, evaluating data utility, rearranging data into a clinical format, interpreting data, and deciding on a plan or action. This workflow establishes common ground for HCI practitioners and researchers to explore solutions to improving the use of patient-generated data in clinical practices.
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Manuscript
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More information
Accepted/In Press date: 8 January 2018
e-pub ahead of print date: 21 April 2018
Published date: 21 April 2018
Venue - Dates:
2018 CHI Conference on Human Factors in Computing Systems, , Montréal, Canada, 2018-04-21 - 2018-04-26
Keywords:
patient-generated data, personalized medicine, self-tracking, workflows, clinical decision making, mHealth, quantified self
Identifiers
Local EPrints ID: 419930
URI: http://eprints.soton.ac.uk/id/eprint/419930
PURE UUID: b948a5a2-8161-4558-a2c1-832f16bf70e2
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Date deposited: 23 Apr 2018 16:30
Last modified: 16 Mar 2024 06:42
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Contributors
Author:
Peter West
Author:
Max Van Kleek
Author:
Mark Weal
Author:
Nigel Shadbolt
Editor:
Regan Mandryk
Editor:
Mark Hancock
Editor:
Mark Perry
Editor:
Anna Cox
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