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Post-discharge stroke patients’ information needs as input to proposing patient-centred eHealth services

Overview of attention for article published in BMC Medical Informatics and Decision Making, June 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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Citations

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Title
Post-discharge stroke patients’ information needs as input to proposing patient-centred eHealth services
Published in
BMC Medical Informatics and Decision Making, June 2016
DOI 10.1186/s12911-016-0307-2
Pubmed ID
Authors

Nadia Davoody, Sabine Koch, Ingvar Krakau, Maria Hägglund

Abstract

Despite the potential of eHealth services to revolutionize the way healthcare and prevention is provided many applications developed for patients fail to deliver their promise. Therefore, the aim of this study is to use patient journey mapping to explore post-discharge stroke patients' information needs to propose eHealth services that meet their needs throughout their care and rehabilitation processes. Three focus groups with younger (<65 years) and older (> = 65 years) stroke patients were performed. Content analysis was used to analyse the data. Stroke patients' information needs was explored using patient journey model. Four main events (discharge from hospital, discharge from rehab clinic, coming home, and clinical encounters) and two phases (at rehab clinic, at home) have been identified in patients' post-discharge journey. The main categories identified in this study indicate that patients not only need to have access to health related information about their care and rehabilitation processes but also practical guidance through healthcare and community services. Patients also have different information needs at different events and during different phases. Potential supportive eHealth services were suggested by the researchers considering different parts of the patients' journeys. Patient journey models and qualitative analysis of patients' information needs are powerful tools that can be used to improve healthcare from a patient perspective. As patients' understanding of their illness changes over time, their need of more flexible support throughout the care and rehabilitation processes increases. To design appropriate eHealth services that meet patients' information needs, it is imperative to understand the current care and rehabilitation processes and identify patients' information needs throughout their journey.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 174 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 174 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 23 13%
Researcher 22 13%
Student > Bachelor 19 11%
Student > Ph. D. Student 18 10%
Librarian 9 5%
Other 32 18%
Unknown 51 29%
Readers by discipline Count As %
Nursing and Health Professions 35 20%
Medicine and Dentistry 26 15%
Computer Science 10 6%
Engineering 8 5%
Agricultural and Biological Sciences 7 4%
Other 33 19%
Unknown 55 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 July 2023.
All research outputs
#3,836,119
of 24,061,085 outputs
Outputs from BMC Medical Informatics and Decision Making
#311
of 2,054 outputs
Outputs of similar age
#64,823
of 346,609 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#4
of 33 outputs
Altmetric has tracked 24,061,085 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,054 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 84% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 346,609 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.