The University of Southampton
University of Southampton Institutional Repository

Common barriers to the use of patient-generated data across clinical settings

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
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
Mandryk, Regan
Hancock, Mark
Perry, Mark
Cox, Anna
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
Mandryk, Regan
Hancock, Mark
Perry, Mark
Cox, Anna

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. pp. 1-13 . (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.

Text
Manuscript - Accepted Manuscript
Download (234kB)
Text
Slides
Available under License Creative Commons Attribution.
Download (2MB)

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
ORCID for Peter West: ORCID iD orcid.org/0000-0002-3605-8744
ORCID for Richard Giordano: ORCID iD orcid.org/0000-0002-2997-9502
ORCID for Mark Weal: ORCID iD orcid.org/0000-0001-6251-8786

Catalogue record

Date deposited: 23 Apr 2018 16:30
Last modified: 16 Mar 2024 06:42

Export record

Altmetrics

Contributors

Author: Peter West ORCID iD
Author: Max Van Kleek
Author: Mark Weal ORCID iD
Author: Nigel Shadbolt
Editor: Regan Mandryk
Editor: Mark Hancock
Editor: Mark Perry
Editor: Anna Cox

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×