Abstract
Objective
To develop and optimise an app (iMPAKT) for improving implementation and measurement of person-centred practice in healthcare settings.
Methods
Two iterative rounds of testing were carried out based on cognitive task analysis and qualitative interview methods. The System Usability Scale (SUS) was also used to evaluate the app. Quantitative data on task completion and SUS scores were evaluated descriptively, with thematic analysis performed on qualitative data. The MoSCoW prioritisation system was used to identify key modifications to improve the app.
Results
Twelve participants took part (eight health professionals and four patient and public involvement representatives). Views on design and structure of the app were positive. The majority of the 16 tasks undertaken during the cognitive task analysis were easy to complete. Mean SUS scores were 73.5/100 (SD: 7.9; range = 60–92.5), suggesting good overall usability. For one section of the app that transcribes patients speaking about their experience of care, a non-intuitive user interface and lack of transcription accuracy were identified as key issues influencing usability and acceptability.
Conclusions
Findings from the evaluation were used to inform iterative modifications to further develop and optimise the iMPAKT App. These included improved navigational flow, and implementation of an updated artificial intelligence (AI) based Speech-To-Text software; allowing for more accurate, real-time transcription. Use of such AI-based software represents an interesting area that requires further evaluation. This is particularly apparent in relation to potential for large-scale collection of data on person-centred measures using the iMPAKT App, and for assessing initiatives designed to improve patient experience.
To develop and optimise an app (iMPAKT) for improving implementation and measurement of person-centred practice in healthcare settings.
Methods
Two iterative rounds of testing were carried out based on cognitive task analysis and qualitative interview methods. The System Usability Scale (SUS) was also used to evaluate the app. Quantitative data on task completion and SUS scores were evaluated descriptively, with thematic analysis performed on qualitative data. The MoSCoW prioritisation system was used to identify key modifications to improve the app.
Results
Twelve participants took part (eight health professionals and four patient and public involvement representatives). Views on design and structure of the app were positive. The majority of the 16 tasks undertaken during the cognitive task analysis were easy to complete. Mean SUS scores were 73.5/100 (SD: 7.9; range = 60–92.5), suggesting good overall usability. For one section of the app that transcribes patients speaking about their experience of care, a non-intuitive user interface and lack of transcription accuracy were identified as key issues influencing usability and acceptability.
Conclusions
Findings from the evaluation were used to inform iterative modifications to further develop and optimise the iMPAKT App. These included improved navigational flow, and implementation of an updated artificial intelligence (AI) based Speech-To-Text software; allowing for more accurate, real-time transcription. Use of such AI-based software represents an interesting area that requires further evaluation. This is particularly apparent in relation to potential for large-scale collection of data on person-centred measures using the iMPAKT App, and for assessing initiatives designed to improve patient experience.
| Original language | English |
|---|---|
| Number of pages | 17 |
| Journal | Digital Health |
| Volume | 10 |
| Early online date | 29 Oct 2024 |
| DOIs | |
| Publication status | Published (in print/issue) - 31 Dec 2024 |
Bibliographical note
© The Author(s) 2024.Data Access Statement
Data is available on request from the corresponding author.Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The iMPAKT project is supported by funding from a Burdett Trust Proactive Grant (United Kingdom) and the New South Wales Ministry of Health (Australia).
| Funders |
|---|
| New South Wales Ministry of Health |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Keywords
- mHealth
- Psychology
- Digital Health
- General
- Person-Centred Practice
- Usability
- Acceptability
- digital health
- usability
- person-centred practice
- acceptability
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